{ "data": { "a": { "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": 1210, "categoryRank": 75, "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", "live": { "slug": "lfm2-5-2-6b", "securityTxt": { "url": "https://liquid.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-25T15:27:49.412118793Z" }, "llmsTxt": { "url": "https://docs.liquid.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-25T14:52:24.663143087Z" }, "domain": { "domain": "liquid.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/liquid.ai", "checkedAt": "2026-10-25T14:01:59.325890117Z" }, "updatedAt": "2026-10-25T15:27:49.412118793Z" } }, "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 4 scored categories. LFM2.5-2.6B leads on reach.", "b": { "slug": "nemotron-3-5-bolt-e4b-4gb", "name": "NVIDIA Nemotron 3.5 Bolt E4B 4GB", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3.5 Bolt E4B 4GB is NVIDIA's open-weight small language model for local voice assistants, interactive characters and device agents, post-trained from Google's Gemma 4 E4B, with 8B parameters (4B effective) and a 64K-token context.", "url": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16" }, { "registry": "ollama", "name": "nemotron-3.5-bolt:e4b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for NVIDIA-Nemotron-3.5-Bolt-E4B-4GB, NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 and both DFlash drafter repositories (checked 2026-10-24).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1 for NVIDIA's post-training and Apache 2.0 for the Gemma 4 base. Running it costs the hardware, 6.18 GB for NVIDIA's Q4_K_M GGUF and 16.06 GB for the BF16 repository. NVIDIA's API catalogue also serves it as nvidia/nemotron-3.5-bolt-e4b, which this listing doesn't grade (checked 2026-10-24).", "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 2026-10-24).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-24" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "gemma4", "fine-tune", "reasoning", "tool-calling", "conversational", "edge", "gguf", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-23", "graded": true, "anchor": { "graded": true, "score": 61, "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": 1210, "categoryRank": 37, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 90, "risk": 80, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-24" }, "negative": 0, "verdict": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.", "bestFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.", "strengths": [ "NVIDIA's Q4_K_M GGUF is 6.18 GB, and NVIDIA measured 3.5 to 5 GB of VRAM in use on RTX and Jetson", "Released support in llama.cpp, vLLM, Transformers, SGLang, Ollama and LM Studio", "Trained for multi-step tool use and instruction hierarchy, with the gemma4 parser named and several calls per turn", "Thinking switches per request and can be budgeted, and conversational_mode asks for brief spoken replies", "OpenMDW-1.1 on NVIDIA's part and Apache 2.0 on the Gemma 4 base, no gate, and the RL datasets published", "DFlash drafters for llama.cpp and vLLM, which NVIDIA says speed decoding by up to 2.7 times" ], "weaknesses": [ "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway", "No safety evaluation results published", "One quantised build from NVIDIA, and its GGUF is text only with no vision or audio projector", "The card says thinking is on by default while the template defaults to off", "Independent results so far cover instruction following only (IFBench and MultiChallenge)" ], "agentNotes": [ "Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K", "With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking", "With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja", "Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do", "Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says", "Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt", "Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says", "Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 90, "risk": 80, "upkeep": 65 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-10-24", "reach": "2026-10-24", "risk": "2026-10-24", "upkeep": "2026-10-24" }, "last": "2026-10-24", "oldest": "2026-10-24", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3.5-bolt:e4b\n# or, with llama.cpp and NVIDIA's own GGUF (the card's recipe, without Docker):\nhf download nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --local-dir bolt\nllama-server -m bolt/Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000\n# or, with vLLM 0.31.0 or later on the BF16 weights:\nvllm serve nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 --served-model-name nemotron-bolt --max-model-len 65536 \\\n --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4 \\\n --limit-mm-per-prompt '{\"image\":0,\"video\":0,\"audio\":0}'", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nemotron-bolt\", \"messages\": [{\"role\": \"user\", \"content\": \"What is 17 multiplied by 23?\"}],\n \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-5-bolt-e4b-4gb" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA (post-training), on Google's Gemma 4 E4B", "builtOn": "google/gemma-4-E4B-it (reinforcement-learning post-training, declared in the BF16 card's metadata; base Apache 2.0)", "params": "8B", "active": "4B effective", "context": 65536, "goodFor": "A small, fast cousin for voice and device assistants, game characters and robot interfaces that interleave short replies with multi-turn tool calls on a consumer GPU or a Jetson.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "who": "publisher", "precision": "q4_k_m", "sizeGB": 6.18, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf, 6,180,446,208 bytes, imatrix-calibrated on about 1M tokens of RL rollouts. Text only, no vision or audio projector. Replaced on 23 October 2026 to set the context metadata to 65,536 tokens" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-MLX-4bit", "who": "lm-studio", "precision": "4bit", "sizeGB": 4.23, "memoryGB": 8, "format": "mlx", "runsOn": [ "LM Studio" ], "note": "The smallest 4-bit build from a runtime's library, 4,231,265,397 bytes for the repository. LM Studio's catalogue page also lists its Q4_K_M, Q6_K and Q8_0 GGUFs and an 8-bit MLX build, and gives 5 GB as minimum memory" }, { "name": "lmstudio-community NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF", "who": "lm-studio", "precision": "q4_k_m", "sizeGB": 6.18, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "6,180,446,208 bytes, a different file from NVIDIA's by checksum. Q6_K is 6.22 GB and Q8_0 8.03 GB" }, { "name": "ollama nemotron-3.5-bolt:e4b-q4_K_M", "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 7.9, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Size as Ollama's tags page shows it. The e4b tag is 7.2 to 7.9 GB by platform, with text and image input. Ollama shows a 128K context, above NVIDIA's 64K" }, { "name": "ollama nemotron-3.5-bolt:e4b-nvfp4", "url": "https://ollama.com/library/nemotron-3.5-bolt:e4b-nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 7.2, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama (MLX)" ], "note": "MLX build for Apple silicon with a 64K context, size as Ollama's tags page shows it. Ollama also lists e4b-mxfp8 (12 GB), e4b-q8_0 (9.8 GB) and e4b-bf16 (17 GB)" }, { "name": "ggml-org NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 8.03, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation, with a BF16 GGUF (15.05 GB) and a DFlash NVFP4 drafter GGUF. No 4-bit build there" }, { "name": "NVIDIA-Nemotron-3.5-Bolt-E4B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 16.06, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM", "Transformers", "SGLang" ], "note": "Whole repository, 15.99 GB of weights. The card intends it mainly for fine-tuning and evaluation, and its vLLM recipe sets the image, video and audio limits to 0" } ], "run": "llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf --alias nemotron-bolt -c 65536 -ngl 999 --jinja --port 8000", "runBuild": "NVIDIA-Nemotron-3.5-Bolt-E4B-4GB (Q4_K_M)", "checked": "2026-10-24", "minMemoryGB": 8 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" } ], "olderCount": 47 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB/commits/main", "securityTxt": "unknown", "checked": "2026-10-24", "notes": [ "The card names NVIDIA Corporation as model developer. The files sit in the verified nvidia organisation on Hugging Face, the one developer.nvidia.com and the card's blog link to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licences stand in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.", "The changelog is the repositories' commit history. Since release only the cards changed, apart from the GGUF replaced on release day to set its context to 65,536 tokens.", "www.nvidia.com/.well-known/security.txt stays unknown, as recorded for the other Nemotron listings. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.json", "live": { "slug": "nemotron-3-5-bolt-e4b-4gb", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-25T15:27:25.017341078Z" }, "updatedAt": "2026-10-25T15:27:25.017341078Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "NVIDIA", "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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-22", "b": "2026-10-23", "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": "2.5/5 (4)", "b": "4/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 4 scored categories. LFM2.5-2.6B leads on reach.", "question": "Which is better for AI agents, LFM2.5-2.6B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 90" ], "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" }, { "aheadOn": [ "Risk, 80 against 50", "Agent interface, 85 against 75", "Documentation and upkeep, 65 against 57" ], "also": [ "Rated higher by the reviewer agents, 4.0 against 2.5 out of 5" ], "goodFor": "Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.", "slug": "nemotron-3-5-bolt-e4b-4gb", "watchFor": "64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "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/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "BigBang-v1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.json", "title": "Carbon-X vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Carbon-X vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Darwin-36B-Opus vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-v3-2-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DeepSeek-V3.2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DeepSeek V4.1 Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/deepseek-v4-flash-0731-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DeepSeek-V4-Flash-0731 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "DiffusionGemma 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-4-12b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 12B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-4-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 31B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 E2B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Gemma 4 E4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-4-5-air-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-4.5-Air vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-4-7-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-4.7-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-5.3-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/glm-5-3-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "GLM-5.3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-120b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "gpt-oss-120b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/gpt-oss-20b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "gpt-oss-20b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/granite-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Granite 4.2 3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/hy3-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Hy3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-small-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Inkling-Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/inkling-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Inkling vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/kimi-k3-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Kimi K3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Kolibri-1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b.json", "title": "Kronos vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Kronos vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-m-1-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Laguna M.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Laguna S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb" }, { "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/laguna-xs-2-1-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Laguna XS 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": 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"https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-30b-a3b-instruct-2507.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-0-8b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-2b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-397b-a17b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-4b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-9b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-35b-a3b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-30b-a3b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-2b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-30b-a3b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-32b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-recursive-flint.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-smollm3-3b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-thinkingcap-qwen3-8-27b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "lfm2-5-2-6b", "key": "reach", "lfm2-5-2-6b": 95, "name": "Reach", "nemotron-3-5-bolt-e4b-4gb": 90, "weight": 25 }, { "by": 30, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "risk", "lfm2-5-2-6b": 50, "name": "Risk", "nemotron-3-5-bolt-e4b-4gb": 80, "weight": 20 }, { "by": 10, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "interface", "lfm2-5-2-6b": 75, "name": "Agent interface", "nemotron-3-5-bolt-e4b-4gb": 85, "weight": 10 }, { "by": 8, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "upkeep", "lfm2-5-2-6b": 57, "name": "Documentation and upkeep", "nemotron-3-5-bolt-e4b-4gb": 65, "weight": 10 } ], "summary": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 4 scored categories. LFM2.5-2.6B leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "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.", "nemotron-3-5-bolt-e4b-4gb": "A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb", "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.min.md" }, "markdown": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 4 scored categories. LFM2.5-2.6B leads on reach. Both do open-weight models you run yourself.\n\n- LFM2.5-2.6B: grade D, 51.5/100, rank #96 of 110 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- NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #45 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.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 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\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 90\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C)\n\nGood for: Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.\n\nAhead on:\n- Risk, 80 against 50\n- Agent interface, 85 against 75\n- Documentation and upkeep, 65 against 57\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 4.0 against 2.5 out of 5\n\nWatch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway\n\n\n## Score by category\n\n| Category | Weight | LFM2.5-2.6B | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 90 | LFM2.5-2.6B +5 |\n| Risk | 20% (20 this run) | 50 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +30 |\n| Agent interface | 10% (10 this run) | 75 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 |\n| Documentation and upkeep | 10% (10 this run) | 57 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.5 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM2.5-2.6B | NVIDIA Nemotron 3.5 Bolt E4B 4GB |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | NVIDIA |\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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-22 | 2026-10-23 |\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 | 2.5/5 (4) | 4/5 (4) |\n\n## Verdicts\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**NVIDIA Nemotron 3.5 Bolt E4B 4GB.** A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.\n\n## Before you call either\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### NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n1. Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K\n2. With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget \u003ctokens\u003e` to cap thinking\n3. With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja\n4. Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do\n5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says\n6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt\n7. Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says\n8. Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines\n\n## Questions\n\n### Which is better for AI agents, LFM2.5-2.6B or NVIDIA Nemotron 3.5 Bolt E4B 4GB?\n\nNVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 4 scored categories. LFM2.5-2.6B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm2-5-2-6b\", \"b\": \"nemotron-3-5-bolt-e4b-4gb\"}`. From a terminal: `anchor compare lfm2-5-2-6b nemotron-3-5-bolt-e4b-4gb`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json\n\n## Other comparisons with LFM2.5-2.6B or NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n- [Agents-A1-4B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.md)\n- [Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.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- [Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.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- [Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [BigBang-v1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.md)\n- [BigBang-v1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Carbon-X vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/carbon-x-vs-lfm2-5-2-6b.md)\n- [Carbon-X vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-bolt-e4b-4gb.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- [Cosmos-Reason2-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-5-bolt-e4b-4gb.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- [Darwin-36B-Opus vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-5-bolt-e4b-4gb.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-V3.2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-5-bolt-e4b-4gb.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 V4.1 Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.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- [DeepSeek-V4-Flash-0731 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-bolt-e4b-4gb.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- [DiffusionGemma 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.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 12B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-bolt-e4b-4gb.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 26B A4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-5-bolt-e4b-4gb.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 31B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb.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 E2B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb.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- [Gemma 4 E4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb.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-4.5-Air vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-bolt-e4b-4gb.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-4.7-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-5-bolt-e4b-4gb.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-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-bolt-e4b-4gb.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- [GLM-5.3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-5-bolt-e4b-4gb.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-120b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-5-bolt-e4b-4gb.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- [gpt-oss-20b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-bolt-e4b-4gb.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- [Granite 4.2 3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Hy3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.md)\n- [Hy3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling-Small vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.md)\n- [Inkling-Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.md)\n- [Inkling vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb.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- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kolibri-1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.md)\n- [Kolibri-1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kronos vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b.md)\n- [Kronos vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb.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 M.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-bolt-e4b-4gb.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 S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-5-bolt-e4b-4gb.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- [Laguna XS 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-bolt-e4b-4gb.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 MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m3-1-flash.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 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"https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-25", "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": "LFM2.5-2.6B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "" } ], "description": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to LFM2.5-2.6B's 51.5 (D) for open-weight models you run yourself. 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