{ "data": { "a": { "slug": "deepseek-v4-1-flash", "name": "DeepSeek V4.1 Flash", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek V4.1 Flash is DeepSeek's open-weight multimodal mixture-of-experts model, 552B backbone parameters with 8B active in prefill and 16B in decode and a 1M-token context, published on Hugging Face under MIT. The 17 October update is graded.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash-1017" }, { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's own API serves the same weights as deepseek-flash, listed separately. Running them yourself costs the hardware, 510 GB of files per repository (checked 2026-10-20).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted", "new" ], "lastRelease": "2026-10-17", "graded": true, "anchor": { "graded": true, "score": 59, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 52, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.", "bestFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "strengths": [ "MIT licence with no use policy, downloadable with no account", "1M tokens of context trained in, with an effort budget from 1 to 100 and a non-thinking mode", "Tool-call format documented both ways with test cases, and DeepSeek ships the parser", "JSON Schema output documented in DeepSeek's deepseek-recipe library", "Released support in vLLM 0.32.0 and SGLang 0.5.22, and DeepSeek's API serves the same update" ], "weaknesses": [ "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine", "No Jinja template in the 17 October repository, prompts are built by running DeepSeek's code", "No safety evaluation or agent-use guidance, and only a company-wide security address", "None of the 40 most recent discussions had a reply from DeepSeek", "Not in llama.cpp, Transformers, LM Studio or Ollama's local library" ], "agentNotes": [ "DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command", "Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding", "Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4", "Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75", "Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends", "Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "lastMoved": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# DeepSeek's reference demo, from the repository's inference folder (8 GPUs)\npip install -r inference/requirements.txt\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" \\\n --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\"\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-1-flash" }, "sameCompany": [ "deepseek-search-api", "deepseek-v3-2", "deepseek-v4-flash-0731" ], "sameModel": [ "deepseek-api" ], "weights": { "trainedBy": "DeepSeek", "params": "552B", "active": "8B prefill, 16B decode", "context": 1048576, "goodFor": "Multimodal mixture-of-experts model for agents and long context, text and images in, with tool calls and a reasoning budget.", "runtimes": [ "vLLM", "SGLang" ], "builds": [ { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 510.3, "memoryGB": 1024, "format": "safetensors", "note": "FP8 with FP4 experts (F8_E4M3 and I8 tensors); the graded 17 October update. The September repository, deepseek-ai/DeepSeek-V4.1-Flash, is the same size" } ], "run": "pip install -r inference/requirements.txt \u0026\u0026 python convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\" \u0026\u0026 torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "runBuild": "DeepSeek-V4.1-Flash-1017", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V4", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V4-Flash-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "params": "284B total, 13B activated (V4-Flash figures; the 0731 card does not restate them)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731" }, { "name": "DeepSeek-V4-Flash-Vision-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp", "params": "not stated on card" }, { "name": "DeepSeek-V4-Pro-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-0813", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813", "params": "1.6T total, 49B activated (V4-Pro figures; the 0813 card does not restate them)" }, { "name": "DeepSeek-V4.1-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash", "params": "552B backbone, 8B activated in prefill / 16B in decode", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" } ], "olderCount": 72, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-20", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the September repository's commit history, whose messages say what changed (an encoding fix on 16 October, an indexer cache fix on 12 October, the chat template on 1 October).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 20 October 2026.", "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.json", "live": { "slug": "deepseek-v4-1-flash", "securityTxt": { "url": "https://deepseek.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-21T15:59:01.743888833Z" }, "pages": [ { "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-21T00:50:03.414291506Z", "changedAt": "2026-10-21T00:50:03.414291506Z", "fingerprint": "96f7ad6c7b35" } ], "updatedAt": "2026-10-21T15:59:01.743888833Z" } }, "answer": "DeepSeek V4.1 Flash scores 59 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 5 scored categories. LFM2.5-2.6B leads on reach and documentation and upkeep.", "b": { "slug": "lfm2-5-2-6b", "name": "LFM2.5-2.6B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM2.5-2.6B is Liquid AI's open-weight 2.6B hybrid model of convolution and attention layers for on-device agents, with tool calling, thinking always on and a 131,072-token context, published on 4 August 2026 with Liquid's own GGUF, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm2-5-2-6b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json", "repo": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-MLX" }, { "registry": "ollama", "name": "LiquidAI/lfm2.5-2.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.6 GB for Liquid's Q4_0 GGUF and 5.4 GB in BF16 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://docs.liquid.ai/lfm/models/lfm25-2.6b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "edge", "reasoning", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-09-22", "graded": true, "anchor": { "graded": true, "score": 51.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 71, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "bestFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "strengths": [ "Liquid's own 4-bit GGUF and MLX builds are 1.6 GB, under 2 GB of memory with the fifth added", "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with an lfm2 tool parser in vLLM and SGLang", "Tool-call format documented by Liquid, several calls per turn, and 131,072 tokens of trained context", "Safetensors and GGUF only, no repository code, a checksum on every file and no gate", "Builds at many precisions from Liquid itself, refreshed in September 2026" ], "weaknesses": [ "Commercial use isn't licensed for organisations with $10M or more of annual revenue", "Thinking is always on, with no way to turn it off or budget it", "No safety evaluation, agent guidance or security contact", "No structured-output documentation and no knowledge cut-off on the card", "Liquid answered 2 of 16 discussions" ], "agentNotes": [ "Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`", "With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately", "Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set", "Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead", "Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought", "Earlier thinking is dropped from the prompt unless `preserve_thinking` is true", "Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.5 } ], "editorialScores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "provenanceScore": 55, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation --temp 0.1 --top-k 50 --repeat-penalty 1.1\n# or\nollama run LiquidAI/lfm2.5-2.6b\n# or, with vLLM:\nvllm serve LiquidAI/LFM2.5-2.6B --enable-auto-tool-choice --tool-call-parser lfm2 --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"LiquidAI/lfm2.5-2.6b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm2-5-2-6b" }, "sameCompany": [ "liquid-d1" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM2.5-2.6B-Base", "params": "2.69B", "context": 131072, "goodFor": "Small on-device agent model for tool use, data extraction, RAG and long-context work on a laptop or phone, always thinking, not for agentic coding or knowledge-heavy tasks.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "LFM2.5-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "who": "publisher", "precision": "bf16", "sizeGB": 5.4, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] }, { "name": "LFM2.5-2.6B-GGUF Q4_0", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF", "who": "publisher", "precision": "q4_0", "sizeGB": 1.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM2.5-2.6B-Q4_0.gguf, 1.59 GB from the Hub's file list. The repository also holds Q4_K_M (1.67 GB), Q5_K_M, Q6_K, Q8_0 (2.87 GB), F16 and BF16, and a quantisation-aware LFM2.5-2.6B-QAD-Q4_0.gguf added on 19 August 2026" }, { "name": "LFM2.5-2.6B-MLX 4bit", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 1.6, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX" ], "note": "4bit/model.safetensors (1.58 GB) and tokenizer, 1.60 GB summed. The repository also holds 5, 6 and 8-bit, BF16, MXFP4, MXFP8 and NVFP4 folders, each also published as its own LFM2.5-2.6B-MLX-* repository" }, { "name": "ollama LiquidAI/lfm2.5-2.6b:q4_k_m", "url": "https://ollama.com/LiquidAI/lfm2.5-2.6b", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.7, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Liquid's own namespace in Ollama's registry, latest and q4_k_m share digest 83f596831da1; q4_0 is 1.6 GB. Sizes as Ollama's tags page shows them" } ], "run": "llama-server -hf LiquidAI/LFM2.5-2.6B-GGUF:Q4_0 --jinja -c 131072 --temp 0.1 --top-k 50 --repeat-penalty 1.1", "runBuild": "LFM2.5-2.6B-GGUF Q4_0", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM2 (LFM2 and LFM2.5)", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM2.5-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-24B-A2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-Base", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-3B", "params": "3B (Hub safetensors count 3.12B)" }, { "name": "LFM2.5-2.6B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base", "params": "2.6B (Hub safetensors count 2.70B)" }, { "name": "LFM2.5-Encoder-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-Prompt-Router", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Policy-Linter", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Diffusion", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-350M-Spellchecker", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker", "params": "350M (Hub safetensors count 357M)" }, { "name": "LFM2.5-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-230M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M-Base", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-PII-Detector", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-8B-A1B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-VL-450M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-VL-1.6B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-Audio-1.5B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B-JP", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2.5-1.2B-JP-202606", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Longevity", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-1.2B-Longevity", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Longevity", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2.5-Embedding-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-350M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M-Base", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.84B)" }, { "name": "LFM2.5-1.2B-Thinking", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-1.2B-Instruct", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2.5-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B", "params": "1.6B (Hub safetensors count 1.60B)" }, { "name": "LFM2.5-1.2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Transcript", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Transcript", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-1.2B-JP", "url": "https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-2.6B-Exp", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B-Exp", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2.5-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-ColBERT-350M", "url": "https://huggingface.co/LiquidAI/LFM2-ColBERT-350M", "params": "350M (Hub safetensors count 353M)" }, { "name": "LFM2-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-3B", "params": "3B (Hub safetensors count 3.00B)" }, { "name": "LFM2-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.34B)" }, { "name": "LFM2-350M-PII-Extract-JP", "url": "https://huggingface.co/LiquidAI/LFM2-350M-PII-Extract-JP", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2-2.6B", "params": "2.6B (Hub safetensors count 2.57B)" }, { "name": "LFM2-350M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Extract", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-350M-ENJP-MT", "url": "https://huggingface.co/LiquidAI/LFM2-350M-ENJP-MT", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Tool", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Tool", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-1.2B-RAG", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-RAG", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-Audio-1.5B", "url": "https://huggingface.co/LiquidAI/LFM2-Audio-1.5B", "params": "1.5B (Hub safetensors count 1.47B)" }, { "name": "LFM2-350M-Math", "url": "https://huggingface.co/LiquidAI/LFM2-350M-Math", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B-Extract", "params": "1.2B (Hub safetensors count 1.17B)" }, { "name": "LFM2-VL-450M", "url": "https://huggingface.co/LiquidAI/LFM2-VL-450M", "params": "450M (Hub safetensors count 451M)" }, { "name": "LFM2-VL-1.6B", "url": "https://huggingface.co/LiquidAI/LFM2-VL-1.6B", "params": "1.6B (Hub safetensors count 1.58B)" }, { "name": "LFM2-700M", "url": "https://huggingface.co/LiquidAI/LFM2-700M", "params": "700M (Hub safetensors count 742M)" }, { "name": "LFM2-350M", "url": "https://huggingface.co/LiquidAI/LFM2-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2-1.2B", "url": "https://huggingface.co/LiquidAI/LFM2-1.2B", "params": "1.2B (Hub safetensors count 1.17B)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026.", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits are README edits, a config fix and the DSpark link; Liquid's deprecations page records which earlier models this one replaces, without dates.", "/.well-known/security.txt answered 404 on www.liquid.ai on 21 October 2026, and Liquid4All/.github has no SECURITY.md.", "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Liquid AI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-17", "b": "2026-09-22", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepSeek V4.1 Flash scores 59 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 5 scored categories. LFM2.5-2.6B leads on reach and documentation and upkeep.", "question": "Which is better for AI agents, DeepSeek V4.1 Flash or LFM2.5-2.6B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 69 against 13" ], "also": null, "goodFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "slug": "deepseek-v4-1-flash", "watchFor": "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine" }, { "aheadOn": [ "Reach, 95 against 50", "Documentation and upkeep, 57 against 48" ], "also": null, "goodFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "slug": "lfm2-5-2-6b", "watchFor": "Commercial use isn't licensed for organisations with $10M or more of annual revenue" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.json", "title": "Agents-A1-4B vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.json", "title": "Agents-A1-4B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.json", "title": "Apertus v1.5 8B vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.json", "title": "Apertus v1.5 8B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.json", "title": "Apodex-1.1-mini vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json", "title": "Apodex-1.1-mini vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash.json", "title": "BigBang-v1 vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json", "title": "BigBang-v1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.json", "title": "Cosmos-Reason2-2B vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json", "title": "Cosmos-Reason2-2B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.json", "title": "Darwin-36B-Opus vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.json", "title": "Darwin-36B-Opus vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.json", "title": "DeepSeek-V3.2 vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.json", "title": "DeepSeek-V3.2 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731.json", "title": "DeepSeek V4.1 Flash vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-diffusiongemma-26b-a4b-it.json", "title": "DeepSeek V4.1 Flash vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.json", "title": "DeepSeek V4.1 Flash vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.json", "title": "DeepSeek V4.1 Flash vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.json", "title": "DeepSeek V4.1 Flash vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.json", "title": "DeepSeek V4.1 Flash vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b.json", "title": "DeepSeek V4.1 Flash vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b.json", "title": "DeepSeek V4.1 Flash vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-3b.json", "title": "DeepSeek V4.1 Flash vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3.json", "title": "DeepSeek V4.1 Flash vs Hy3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling.json", "title": "DeepSeek V4.1 Flash vs Inkling", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small.json", "title": "DeepSeek V4.1 Flash vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.json", "title": "DeepSeek V4.1 Flash vs Kimi K3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.json", "title": "DeepSeek V4.1 Flash vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.json", "title": "DeepSeek V4.1 Flash vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.json", "title": "DeepSeek V4.1 Flash vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.json", "title": "DeepSeek V4.1 Flash vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.json", "title": "DeepSeek V4.1 Flash vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b.json", "title": "DeepSeek V4.1 Flash vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5.json", "title": "DeepSeek V4.1 Flash vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.json", "title": "DeepSeek V4.1 Flash vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.json", "title": "DeepSeek V4.1 Flash vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.json", "title": "DeepSeek V4.1 Flash vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.json", "title": "DeepSeek V4.1 Flash vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.json", "title": "DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.json", "title": "DeepSeek V4.1 Flash vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think.json", "title": "DeepSeek V4.1 Flash vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-30b-a3b-instruct-2507.json", "title": "DeepSeek V4.1 Flash vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-4b-instruct-2507.json", "title": "DeepSeek V4.1 Flash vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-0-8b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-4b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next.json", "title": "DeepSeek V4.1 Flash vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-2b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2.json", "title": "DeepSeek V4.1 Flash vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.json", "title": "DeepSeek V4.1 Flash vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.json", "title": "DeepSeek V4.1 Flash vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.json", "title": "DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.json", "title": "DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.json", "title": "DiffusionGemma 26B A4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 12B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 26B A4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 31B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 E2B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 E4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.json", "title": "GLM-4.5-Air vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.json", "title": "GLM-4.7-Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json", "title": "GLM-5.3-Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.json", "title": "GLM-5.3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.json", "title": "gpt-oss-120b vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.json", "title": "gpt-oss-20b vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.json", "title": "Granite 4.2 3B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json", "title": "Hy3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.json", "title": "Inkling-Small vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json", "title": "Inkling vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.json", "title": "Kimi K3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.json", "title": "Kolibri-1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.json", "title": "Laguna M.1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.json", "title": "Laguna S 2.1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.json", "title": "Laguna XS 2.1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.json", "title": "LFM2.5-2.6B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.json", "title": "LFM2.5-2.6B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b.json", "title": "LFM2.5-2.6B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.json", "title": "LFM2.5-2.6B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.json", "title": "LFM2.5-2.6B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.json", "title": "LFM2.5-2.6B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.json", "title": "LFM2.5-2.6B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.json", "title": "LFM2.5-2.6B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.json", "title": "LFM2.5-2.6B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.json", "title": "LFM2.5-2.6B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.json", "title": "LFM2.5-2.6B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b.json", "title": "LFM2.5-2.6B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.json", "title": "LFM2.5-2.6B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b.json", "title": "LFM2.5-2.6B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.json", "title": "LFM2.5-2.6B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.json", "title": "LFM2.5-2.6B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.json", "title": "LFM2.5-2.6B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.json", "title": "LFM2.5-2.6B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.json", "title": "LFM2.5-2.6B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.json", "title": "LFM2.5-2.6B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 56, "deepseek-v4-1-flash": 69, "edge": "deepseek-v4-1-flash", "key": "capability", "lfm2-5-2-6b": 13, "name": "Capability", "weight": 35 }, { "by": 45, "deepseek-v4-1-flash": 50, "edge": "lfm2-5-2-6b", "key": "reach", "lfm2-5-2-6b": 95, "name": "Reach", "weight": 25 }, { "by": 0, "deepseek-v4-1-flash": 50, "edge": "", "key": "risk", "lfm2-5-2-6b": 50, "name": "Risk", "weight": 20 }, { "by": 0, "deepseek-v4-1-flash": 75, "edge": "", "key": "interface", "lfm2-5-2-6b": 75, "name": "Agent interface", "weight": 10 }, { "by": 9, "deepseek-v4-1-flash": 48, "edge": "lfm2-5-2-6b", "key": "upkeep", "lfm2-5-2-6b": 57, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "DeepSeek V4.1 Flash scores 59 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 5 scored categories. LFM2.5-2.6B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-1-flash": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.", "lfm2-5-2-6b": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b", "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.min.md" }, "markdown": "DeepSeek V4.1 Flash scores 59 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 5 scored categories. LFM2.5-2.6B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- DeepSeek V4.1 Flash: grade C, 59/100, rank #63 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json\n- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### DeepSeek V4.1 Flash (C)\n\nGood for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.\n\nAhead on:\n- Capability, 69 against 13\n\nWatch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine\n\n### LFM2.5-2.6B (D)\n\nGood for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold\n\nAhead on:\n- Reach, 95 against 50\n- Documentation and upkeep, 57 against 48\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n\n## Score by category\n\n| Category | Weight | DeepSeek V4.1 Flash | LFM2.5-2.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 69 | 13 | DeepSeek V4.1 Flash +56 |\n| Reach | 25% (25 this run) | 50 | 95 | LFM2.5-2.6B +45 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 75 | 75 | even |\n| Documentation and upkeep | 10% (10 this run) | 48 | 57 | LFM2.5-2.6B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59 · C** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | DeepSeek V4.1 Flash | LFM2.5-2.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Liquid AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-17 | 2026-09-22 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.\n\n**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\n\n## Before you call either\n\n### DeepSeek V4.1 Flash\n\n1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive\n2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command\n3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding\n4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4\n5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75\n6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends\n7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update\n\n### LFM2.5-2.6B\n\n1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`\n2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately\n3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set\n4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead\n5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought\n6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true\n7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, DeepSeek V4.1 Flash or LFM2.5-2.6B?\n\nDeepSeek V4.1 Flash scores 59 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 5 scored categories. LFM2.5-2.6B leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-1-flash\", \"b\": \"lfm2-5-2-6b\"}`. From a terminal: `anchor compare deepseek-v4-1-flash lfm2-5-2-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n\n## Other comparisons with DeepSeek V4.1 Flash or LFM2.5-2.6B\n\n- [Agents-A1-4B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.md)\n- [Agents-A1-4B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.md)\n- [Apertus v1.5 8B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.md)\n- [Apertus v1.5 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.md)\n- [Apodex-1.1-mini vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.md)\n- [Apodex-1.1-mini vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.md)\n- [BigBang-v1 vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash.md)\n- [BigBang-v1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.md)\n- [Cosmos-Reason2-2B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.md)\n- [Cosmos-Reason2-2B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.md)\n- [Darwin-36B-Opus vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.md)\n- [Darwin-36B-Opus vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V3.2 vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.md)\n- [DeepSeek-V3.2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.md)\n- [DeepSeek V4.1 Flash vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731.md)\n- [DeepSeek V4.1 Flash vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-diffusiongemma-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it.md)\n- [DeepSeek V4.1 Flash vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.md)\n- [DeepSeek V4.1 Flash vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.md)\n- [DeepSeek V4.1 Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b.md)\n- [DeepSeek V4.1 Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b.md)\n- [DeepSeek V4.1 Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-3b.md)\n- [DeepSeek V4.1 Flash vs Hy3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3.md)\n- [DeepSeek V4.1 Flash vs Inkling](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling.md)\n- [DeepSeek V4.1 Flash vs Inkling-Small](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small.md)\n- [DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek V4.1 Flash vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.md)\n- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)\n- [DeepSeek V4.1 Flash vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.md)\n- [DeepSeek V4.1 Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.md)\n- [DeepSeek V4.1 Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.md)\n- [DeepSeek V4.1 Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md)\n- [DeepSeek V4.1 Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5.md)\n- [DeepSeek V4.1 Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.md)\n- [DeepSeek V4.1 Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.md)\n- [DeepSeek V4.1 Flash vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-30b-a3b-instruct-2507.md)\n- [DeepSeek V4.1 Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-4b-instruct-2507.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-0-8b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-4b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-2b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2.md)\n- [DeepSeek V4.1 Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.md)\n- [DeepSeek V4.1 Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.md)\n- [DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.md)\n- [DiffusionGemma 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 12B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 31B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E2B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.md)\n- [GLM-4.5-Air vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.md)\n- [GLM-4.7-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.md)\n- [gpt-oss-120b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.md)\n- [gpt-oss-20b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.md)\n- [Hy3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.md)\n- [Inkling-Small vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.md)\n- [Inkling vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kolibri-1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.md)\n- [Laguna M.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.md)\n- [Laguna S 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna XS 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.md)\n- [LFM2.5-2.6B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.md)\n- [LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LFM2.5-2.6B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.md)\n- [LFM2.5-2.6B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b.md)\n- [LFM2.5-2.6B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.md)\n- [LFM2.5-2.6B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.md)\n- [LFM2.5-2.6B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.md)\n- [LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LFM2.5-2.6B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b.md)\n- [LFM2.5-2.6B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.md)\n- [LFM2.5-2.6B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.md)\n- [LFM2.5-2.6B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.md)\n- [LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [LFM2.5-2.6B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.md)\n- [LFM2.5-2.6B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.md)\n- [LFM2.5-2.6B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.md)\n- [LFM2.5-2.6B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b.md)\n- [LFM2.5-2.6B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.md)\n- [LFM2.5-2.6B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b.md)\n- [LFM2.5-2.6B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.md)\n- [LFM2.5-2.6B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.md)\n- [LFM2.5-2.6B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.md)\n- [LFM2.5-2.6B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b.md)\n- [LFM2.5-2.6B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.md)\n- [LFM2.5-2.6B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [LFM2.5-2.6B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.md)\n- [LFM2.5-2.6B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [LFM2.5-2.6B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct.md)\n- [LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [LFM2.5-2.6B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.md)\n- [LFM2.5-2.6B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.md)\n- [LFM2.5-2.6B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.md)\n- [LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.md)\n- [LFM2.5-2.6B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.md)\n- [LFM2.5-2.6B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.md)\n- [LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.md)\n- [LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "DeepSeek V4.1 Flash vs LFM2.5-2.6B", "url": "" } ], "description": "DeepSeek V4.1 Flash scores 59 (C) to LFM2.5-2.6B's 51.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "DeepSeek V4.1 Flash C 59", "LFM2.5-2.6B D 51.5", "scores" ], "h1": "DeepSeek V4.1 Flash vs LFM2.5-2.6B", "image": "https://www.anchorterminal.com/assets/og/compare-deepseek-v4-1-flash-vs-lfm2-5-2-6b.png", "path": "/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b", "published": "2026-10-01", "section": "tools", "title": "DeepSeek V4.1 Flash vs LFM2.5-2.6B for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b" }, "tokens": { "markdown": 6250, "slim": 480 }, "version": 1 }