{ "data": { "a": { "slug": "bigbang-v1", "name": "BigBang-v1", "vendor": "The Endless Frontier (endless-frontier)", "vendorUrl": "https://huggingface.co/endless-frontier", "kind": "weights", "category": "open-weights", "summary": "BigBang-v1 is The Endless Frontier's general-purpose fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active), post-trained on about 10,000 synthetic research and technical problems, published on Hugging Face in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/bigbang-v1", "markdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json", "repo": "https://huggingface.co/endless-frontier/BigBang-v1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "endless-frontier/BigBang-v1" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 71.9 GB for the BF16 files, the only build from the publisher (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://huggingface.co/endless-frontier/BigBang-v1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "derivative", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-08-06", "graded": true, "anchor": { "graded": true, "score": 54.4, "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": 67, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 55, "risk": 55, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.", "bestFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.", "strengths": [ "Apache 2.0 like its base, not gated", "Same architecture, config and template as Qwen3.6-35B-A3B, so released llama.cpp, vLLM, SGLang and Transformers run it", "Safetensors only with a SHA-256 for every weight file", "262,144 tokens of context and several tool calls a turn" ], "weaknesses": [ "BF16 is the only build from the publisher, needing about 86 GB", "No report, safety results, agent guidance or security contact", "Training data described only as about 10,000 synthetic examples", "No reply from the publisher to any of 7 discussions", "Every benchmark figure is the publisher's own, with no set-up stated" ], "agentNotes": [ "On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`", "The card says to keep at least 128K of context so its thinking isn't cut short", "Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output", "For a smaller build you'll need someone else's quantisation, the publisher has none", "Validate JSON output yourself or use the runtime's guided decoding" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.4 } ], "editorialScores": { "interface": 60, "reach": 55, "risk": 55, "upkeep": 46 }, "provenanceScore": 13, "checked": { "categories": { "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": "vllm serve endless-frontier/BigBang-v1 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"endless-frontier/BigBang-v1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/bigbang-v1" }, "weights": { "trainedBy": "The Endless Frontier (endless-frontier)", "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text. Identical config.json, parameter count and chat template, and the repository's LICENSE is Qwen's Apache 2.0 file. BigBang is also Apache 2.0", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "General-purpose mixture-of-experts model tuned for scientific research, search and coding tasks with thinking on, text and images in.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "BigBang-v1", "url": "https://huggingface.co/endless-frontier/BigBang-v1", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "BigBang-v1", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "The Endless Frontier (endless-frontier)", "family": "BigBang", "url": "https://www.anchorterminal.com/open-weights/#endless-frontier", "olderCount": 2, "unrestrictedCopies": 3 }, "area": "models", "provenance": { "legalEntity": "", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity, website, paper or GitHub repository is named on the card. The Hugging Face organisation endless-frontier (fullname The Endless Frontier, 17 members, three models) isn't verified.", "Nothing is hosted by the publisher, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit messages are uploads and benchmark-table fixes.", "No security.txt, since there is no domain." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/bigbang-v1.json" }, "answer": "BigBang-v1 scores 54.4 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface 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": "The Endless Frontier (endless-frontier)", "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": "Apache-2.0", "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": "2026-08-06", "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": "2.3/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "BigBang-v1 scores 54.4 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, BigBang-v1 or LFM2.5-2.6B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 50" ], "also": null, "goodFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.", "slug": "bigbang-v1", "watchFor": "BF16 is the only build from the publisher, needing about 86 GB" }, { "aheadOn": [ "Reach, 95 against 55", "Agent interface, 75 against 60", "Documentation and upkeep, 57 against 46" ], "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-bigbang-v1.json", "title": "Agents-A1-4B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1" }, { "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-bigbang-v1.json", "title": "Apertus v1.5 8B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1" }, { "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-bigbang-v1.json", "title": "Apodex-1.1-mini vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1" }, { "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-cosmos-reason2-2b.json", "title": "BigBang-v1 vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-darwin-36b-opus.json", "title": "BigBang-v1 vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.json", "title": "BigBang-v1 vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2" }, { "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-deepseek-v4-flash-0731.json", "title": "BigBang-v1 vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-diffusiongemma-26b-a4b-it.json", "title": "BigBang-v1 vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.json", "title": "BigBang-v1 vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.json", "title": "BigBang-v1 vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.json", "title": "BigBang-v1 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it.json", "title": "BigBang-v1 vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it.json", "title": "BigBang-v1 vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.json", "title": "BigBang-v1 vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.json", "title": "BigBang-v1 vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3.json", "title": "BigBang-v1 vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.json", "title": "BigBang-v1 vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b.json", "title": "BigBang-v1 vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b.json", "title": "BigBang-v1 vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b.json", "title": "BigBang-v1 vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3.json", "title": "BigBang-v1 vs Hy3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling.json", "title": "BigBang-v1 vs Inkling", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling-small.json", "title": "BigBang-v1 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "BigBang-v1 vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.json", "title": "BigBang-v1 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.json", "title": "BigBang-v1 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.json", "title": "BigBang-v1 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.json", "title": "BigBang-v1 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1.json", "title": "BigBang-v1 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.json", "title": "BigBang-v1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-distill-qwen-9b.json", "title": "BigBang-v1 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5.json", "title": "BigBang-v1 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm5-2b.json", "title": "BigBang-v1 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.json", "title": "BigBang-v1 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.json", "title": "BigBang-v1 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b.json", "title": "BigBang-v1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "BigBang-v1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b.json", "title": "BigBang-v1 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b.json", "title": "BigBang-v1 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "BigBang-v1 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b.json", "title": "BigBang-v1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b.json", "title": "BigBang-v1 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.json", "title": "BigBang-v1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-olmo-3-7b-think.json", "title": "BigBang-v1 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.json", "title": "BigBang-v1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b.json", "title": "BigBang-v1 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b.json", "title": "BigBang-v1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-30b-a3b-instruct-2507.json", "title": "BigBang-v1 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-4b-instruct-2507.json", "title": "BigBang-v1 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-0-8b.json", "title": "BigBang-v1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b.json", "title": "BigBang-v1 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-2b.json", "title": "BigBang-v1 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-397b-a17b.json", "title": "BigBang-v1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-4b.json", "title": "BigBang-v1 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.json", "title": "BigBang-v1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-27b.json", "title": "BigBang-v1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b.json", "title": "BigBang-v1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-27b.json", "title": "BigBang-v1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next.json", "title": "BigBang-v1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "BigBang-v1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next.json", "title": "BigBang-v1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "BigBang-v1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-2b-instruct.json", "title": "BigBang-v1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "BigBang-v1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct.json", "title": "BigBang-v1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-4b-instruct.json", "title": "BigBang-v1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-8b-instruct.json", "title": "BigBang-v1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2.json", "title": "BigBang-v1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b.json", "title": "BigBang-v1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b.json", "title": "BigBang-v1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b.json", "title": "BigBang-v1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "BigBang-v1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b.json", "title": "BigBang-v1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b" }, { "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-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-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-lfm2-5-2-6b.json", "title": "DeepSeek V4.1 Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b" }, { "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": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "bigbang-v1": 55, "by": 40, "edge": "lfm2-5-2-6b", "key": "reach", "lfm2-5-2-6b": 95, "name": "Reach", "weight": 25 }, { "bigbang-v1": 55, "by": 5, "edge": "bigbang-v1", "key": "risk", "lfm2-5-2-6b": 50, "name": "Risk", "weight": 20 }, { "bigbang-v1": 60, "by": 15, "edge": "lfm2-5-2-6b", "key": "interface", "lfm2-5-2-6b": 75, "name": "Agent interface", "weight": 10 }, { "bigbang-v1": 46, "by": 11, "edge": "lfm2-5-2-6b", "key": "upkeep", "lfm2-5-2-6b": 57, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "BigBang-v1 scores 54.4 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "bigbang-v1": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.", "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/bigbang-v1-vs-lfm2-5-2-6b", "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.min.md" }, "markdown": "BigBang-v1 scores 54.4 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- BigBang-v1: grade C, 54.4/100, rank #83 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.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### BigBang-v1 (C)\n\nGood for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.\n\nAhead on:\n- Risk, 55 against 50\n\nWatch for: BF16 is the only build from the publisher, needing about 86 GB\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 55\n- Agent interface, 75 against 60\n- Documentation and upkeep, 57 against 46\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 | BigBang-v1 | LFM2.5-2.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 95 | LFM2.5-2.6B +40 |\n| Risk | 20% (20 this run) | 55 | 50 | BigBang-v1 +5 |\n| Agent interface | 10% (10 this run) | 60 | 75 | LFM2.5-2.6B +15 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 57 | LFM2.5-2.6B +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.4 · C** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | BigBang-v1 | LFM2.5-2.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | The Endless Frontier (endless-frontier) | 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 | Apache-2.0 | 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 | 2026-08-06 | 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 | 2.3/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.\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### BigBang-v1\n\n1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n2. The card says to keep at least 128K of context so its thinking isn't cut short\n3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output\n4. For a smaller build you'll need someone else's quantisation, the publisher has none\n5. Validate JSON output yourself or use the runtime's guided decoding\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, BigBang-v1 or LFM2.5-2.6B?\n\nBigBang-v1 scores 54.4 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bigbang-v1\", \"b\": \"lfm2-5-2-6b\"}`. From a terminal: `anchor compare bigbang-v1 lfm2-5-2-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n\n## Other comparisons with BigBang-v1 or LFM2.5-2.6B\n\n- [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.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 BigBang-v1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.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 BigBang-v1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1.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 Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b.md)\n- [BigBang-v1 vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/bigbang-v1-vs-darwin-36b-opus.md)\n- [BigBang-v1 vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.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 DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731.md)\n- [BigBang-v1 vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-diffusiongemma-26b-a4b-it.md)\n- [BigBang-v1 vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.md)\n- [BigBang-v1 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.md)\n- [BigBang-v1 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.md)\n- [BigBang-v1 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it.md)\n- [BigBang-v1 vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it.md)\n- [BigBang-v1 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.md)\n- [BigBang-v1 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.md)\n- [BigBang-v1 vs GLM-5.3](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3.md)\n- [BigBang-v1 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.md)\n- [BigBang-v1 vs gpt-oss-120b](https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b.md)\n- [BigBang-v1 vs gpt-oss-20b](https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b.md)\n- [BigBang-v1 vs Granite 4.2 3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b.md)\n- [BigBang-v1 vs Hy3](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3.md)\n- [BigBang-v1 vs Inkling](https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling.md)\n- [BigBang-v1 vs Inkling-Small](https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling-small.md)\n- [BigBang-v1 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [BigBang-v1 vs Kimi K3](https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.md)\n- [BigBang-v1 vs Kolibri-1](https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.md)\n- [BigBang-v1 vs Laguna M.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.md)\n- [BigBang-v1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-s-2-1.md)\n- [BigBang-v1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-xs-2-1.md)\n- [BigBang-v1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-0-tiny.md)\n- [BigBang-v1 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-distill-qwen-9b.md)\n- [BigBang-v1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm-o-4-5.md)\n- [BigBang-v1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-minicpm5-2b.md)\n- [BigBang-v1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5.md)\n- [BigBang-v1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-7.md)\n- [BigBang-v1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b.md)\n- [BigBang-v1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [BigBang-v1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-30b-a3b.md)\n- [BigBang-v1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-4b.md)\n- [BigBang-v1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [BigBang-v1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b.md)\n- [BigBang-v1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-ultra-550b-a55b.md)\n- [BigBang-v1 vs occamy-1.0](https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.md)\n- [BigBang-v1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/bigbang-v1-vs-olmo-3-7b-think.md)\n- [BigBang-v1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.md)\n- [BigBang-v1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b.md)\n- [BigBang-v1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b.md)\n- [BigBang-v1 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-30b-a3b-instruct-2507.md)\n- [BigBang-v1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-4b-instruct-2507.md)\n- [BigBang-v1 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-0-8b.md)\n- [BigBang-v1 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-122b-a10b.md)\n- [BigBang-v1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-2b.md)\n- [BigBang-v1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-397b-a17b.md)\n- [BigBang-v1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-4b.md)\n- [BigBang-v1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-5-9b.md)\n- [BigBang-v1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-27b.md)\n- [BigBang-v1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-6-35b-a3b.md)\n- [BigBang-v1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-27b.md)\n- [BigBang-v1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-8-flash-next.md)\n- [BigBang-v1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct.md)\n- [BigBang-v1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-next.md)\n- [BigBang-v1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-omni-30b-a3b-instruct.md)\n- [BigBang-v1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-2b-instruct.md)\n- [BigBang-v1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-30b-a3b-instruct.md)\n- [BigBang-v1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-32b-instruct.md)\n- [BigBang-v1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-4b-instruct.md)\n- [BigBang-v1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-vl-8b-instruct.md)\n- [BigBang-v1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2.md)\n- [BigBang-v1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-smollm3-3b.md)\n- [BigBang-v1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b.md)\n- [BigBang-v1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-27b.md)\n- [BigBang-v1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/bigbang-v1-vs-swift-1-5-qwen3-8-flash-next.md)\n- [BigBang-v1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.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": "BigBang-v1 vs LFM2.5-2.6B", "url": "" } ], "description": "BigBang-v1 scores 54.4 (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": [ "BigBang-v1 C 54.4", "LFM2.5-2.6B D 51.5", "scores" ], "h1": "BigBang-v1 vs LFM2.5-2.6B", "image": "https://www.anchorterminal.com/assets/og/compare-bigbang-v1-vs-lfm2-5-2-6b.png", "path": "/compare/bigbang-v1-vs-lfm2-5-2-6b", "published": "2026-10-01", "section": "tools", "title": "BigBang-v1 vs LFM2.5-2.6B for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b" }, "tokens": { "markdown": 5800, "slim": 480 }, "version": 1 }