{ "data": { "a": { "slug": "mistral-large-4", "name": "Mistral Large 4", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "weights", "category": "open-weights", "summary": "Mistral Large 4 (\"Le Chonk\") is Mistral AI's open-weight mixture-of-experts model of about 1T parameters with 52B active, taking text and images, with three reasoning efforts and a 1M-token context, published in FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/mistral-large-4", "markdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json", "repo": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "license": "Mistral Ecosystem Open-Weight License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "mistralai/Mistral-Large-4-1T-A52B" }, { "registry": "huggingface", "name": "mistralai/Mistral-Large-4-1T-A52B-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both repositories report gated false (checked 2026-10-30).", "pricing": "free", "pricingNotes": "Free to download. The Mistral Ecosystem Open-Weight License allows commercial use, except that a licensee or affiliate that operates a Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use; internal use and use through Mistral's own products are exempt. Mistral's API serves it as mistral-large-4 at $0.70 input, $0.07 cached input and $2.10 output per million tokens (checked 2026-10-30).", "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 2026-10-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "frontier", "reasoning", "tool-calling", "long-context", "multilingual", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 54.6, "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": 1239, "categoryRank": 72, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 32, "interface": 95, "reach": 50, "risk": 75, "upkeep": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.", "bestFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.", "strengths": [ "Safetensors only, loaded by Transformers' own code with a Jinja template, in Mistral's verified organisation with a hash on every file", "Tool calling documented end to end with a named parser, several calls a turn and strict JSON Schema output", "Reasoning effort budgeted per request, low, medium or high, with the reasoning returned apart from the answer", "FP8 and NVFP4 builds from Mistral, and Mistral's own API serves the same model for agents without the hardware", "First among open-weight models on Cognition's FrontierCode 2.0 leaderboard, run in Mistral's own harness" ], "weaknesses": [ "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine", "No vLLM or SGLang release supports it yet, only Docker images tagged mistral-large-4", "A Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use", "Safety results are Mistral's own with no settings to rerun them, and no limitations, training data sources or knowledge cut-off are published", "The 1M context comes from YaRN scaling with no stated trained length, and thinking can't be turned off" ], "agentNotes": [ "Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it", "On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe", "Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch", "Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too", "Use temperature 1.0 and top_p 0.95 at every effort, per the card", "Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model", "The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message", "Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head", "If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.6 } ], "editorialScores": { "capability": 32, "interface": 95, "reach": 50, "risk": 75, "upkeep": 64 }, "provenanceScore": 84, "checked": { "categories": { "capability": "2026-10-30", "interface": "2026-10-30", "reach": "2026-10-30", "risk": "2026-10-30", "upkeep": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# NVFP4 on 4 or 8 Blackwell GPUs, with the card's vLLM image (no vLLM release supports it yet):\ndocker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host \\\n vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 \\\n --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral \\\n --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"mistralai/Mistral-Large-4-1T-A52B-NVFP4\", \"reasoning_effort\": \"medium\", \"temperature\": 1.0, \"top_p\": 0.95,\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/mistral-large-4" }, "sameCompany": [ "mistral-embeddings", "mistral-moderation", "mistral-voxtral-transcribe", "voxtral-mini-4b-realtime-2602", "mistral-ocr", "mistral-vibe-cli" ], "sameModel": [ "mistral-api" ], "weights": { "trainedBy": "Mistral AI", "params": "~1T total (1.05T per Mistral's docs), 52B active", "context": 1048576, "goodFor": "Frontier-sized multimodal model for coding agents, defensive security work and long documents, with tool calling, strict JSON Schema output and three reasoning efforts, for operators with a multi-GPU server.", "runtimes": [ "llama.cpp", "Transformers" ], "builds": [ { "name": "Mistral-Large-4-1T-A52B-NVFP4", "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 610, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (mistral-large-4 image)", "SGLang (mistral-large-4 image)" ], "note": "NVFP4 experts with FP8 linears, for Blackwell GPUs; Mistral-format params.json only, no config.json, so not for Transformers" }, { "name": "ggml-org Mistral-Large-4-1T-A52B-GGUF Q4_K_M", "url": "https://huggingface.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF", "who": "ggml-org (llama.cpp)", "precision": "q4_k_m", "sizeGB": 634.8, "memoryGB": 1024, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "llama.cpp's own organisation, linked from Mistral's card. Also BF16, Q8_0, Q2_K (380.7 GB) and IQ2_XXS (297.7 GB), plus an NVFP4 GGUF of 619.4 GB in a second repository; the 2-bit files are below the 4-bit line and aren't counted. Vision projector 3.4 GB extra" }, { "name": "Mistral-Large-4-1T-A52B", "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "who": "publisher", "precision": "fp8", "sizeGB": 1060.6, "memoryGB": 2048, "format": "safetensors", "runsOn": [ "Transformers", "vLLM (mistral-large-4 image)", "SGLang (mistral-large-4 image)" ], "note": "Block-scaled FP8 (128 by 128), the release precision; vision tower, projector, output head and some attention projections kept in BF16. Includes the DSpark draft head" } ], "run": "docker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85", "runBuild": "Mistral-Large-4-1T-A52B-NVFP4", "checked": "2026-10-30", "minMemoryGB": 1024 }, "family": { "lab": "Mistral AI", "family": "Mistral Large 4", "url": "https://www.anchorterminal.com/open-weights/#mistral", "olderCount": 49 }, "area": "models", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/commits/main", "securityTxt": "valid", "checked": "2026-10-30", "notes": [ "The files sit in Mistral's verified mistralai organisation, which the card, Mistral's announcement and its docs page for the model link to. Legal entity and domain registration are carried from the mistral-api listing.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored. The hosted model is mistral-api.", "The changelog is the repositories' commit history, an initial release on 29 October 2026 and two card edits the same day, plus a merged evaluation figure on the NVFP4 card.", "mistral.ai/.well-known/security.txt answered on 30 October 2026 with a security contact, an expiry of 5 May 2027 and a HackerOne submission policy." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-large-4.json" }, "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 4 scored categories. Mistral Large 4 leads on agent interface.", "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": 1239, "categoryRank": 37, "methodology": "0.7", "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 90, "risk": 80, "upkeep": 65 }, "provenanceScore": 76, "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": 76 }, "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": "2026-10-30T15:37:59.318803626Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-30T05:42:28.035739833Z", "changedAt": "2026-10-30T05:42:28.035739833Z", "fingerprint": "68ba2c16a3ad" } ], "updatedAt": "2026-10-30T15:37:59.318803626Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral 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": "Mistral Ecosystem Open-Weight License (modified MIT)", "b": "OpenMDW-1.1", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-29", "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": "none", "b": "4/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 4 scored categories. Mistral Large 4 leads on agent interface.", "question": "Which is better for AI agents, Mistral Large 4 or NVIDIA Nemotron 3.5 Bolt E4B 4GB?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 95 against 85" ], "also": null, "goodFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.", "slug": "mistral-large-4", "watchFor": "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine" }, { "aheadOn": [ "Reach, 90 against 50", "Risk, 80 against 75" ], "also": null, "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-mistral-large-4.json", "title": "Agents-A1-4B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Apertus v1.5 8B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Apodex-1.1-mini vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "BigBang-v1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Carbon-X vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Cosmos-Reason2-2B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Darwin-36B-Opus vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DeepSeek-V3.2 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DeepSeek V4.1 Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DeepSeek-V4-Flash-0731 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "DiffusionGemma 26B A4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Gemma 4 12B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-bolt-e4b-4gb.json", 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"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-mistral-large-4.json", "title": "Gemma 4 E2B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Gemma 4 E4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-4.5-Air vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-4.7-Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-5.3-Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "GLM-5.3 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "gpt-oss-120b vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "gpt-oss-20b vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Granite 4.2 3B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Hy3 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Inkling-Small vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Inkling vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Kimi K3 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Kolibri-1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Kronos vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Laguna M.1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Laguna S 2.1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mistral-large-4" }, { "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-mistral-large-4.json", "title": "Laguna XS 2.1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mistral-large-4" }, { "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": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4.json", "title": "LBS-27B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "LBS-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4.json", "title": "LFM2.5-2.6B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "LFM2.5-2.6B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mistral-large-4.json", "title": "Ling-3.0-tiny vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Ling-3.0-tiny vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiMo-V2.6 Distill Qwen 9B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4.json", "title": "MiniCPM-o 4.5 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiniCPM-o 4.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4.json", "title": "MiniCPM5-2B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiniCPM5-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4.json", "title": "MiniMax-M2.5 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiniMax-M2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4.json", "title": "MiniMax-M2.7 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiniMax-M2.7 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-mistral-large-4.json", "title": "MiniMax-M3.1-Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "MiniMax-M3.1-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-muse-glimmer-30b.json", "title": "Mistral Large 4 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b.json", "title": "Mistral Large 4 vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Mistral Large 4 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.json", "title": "Mistral Large 4 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b.json", "title": "Mistral Large 4 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Mistral Large 4 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.json", "title": "Mistral Large 4 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-ultra-550b-a55b.json", "title": "Mistral Large 4 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-occamy-1-0.json", "title": "Mistral Large 4 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-olmo-3-7b-think.json", "title": "Mistral Large 4 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-35b-a3b.json", "title": "Mistral Large 4 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-397b.json", "title": "Mistral Large 4 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b.json", "title": "Mistral Large 4 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Mistral Large 4 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-4b-instruct-2507.json", "title": "Mistral Large 4 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-0-8b.json", "title": "Mistral Large 4 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-122b-a10b.json", "title": "Mistral Large 4 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-2b.json", "title": "Mistral Large 4 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b.json", "title": "Mistral Large 4 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b.json", "title": "Mistral Large 4 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b.json", "title": "Mistral Large 4 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-27b.json", "title": "Mistral Large 4 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-35b-a3b.json", "title": "Mistral Large 4 vs Qwen3.6-35B-A3B", 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"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" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "reach", "mistral-large-4": 50, "name": "Reach", "nemotron-3-5-bolt-e4b-4gb": 90, "weight": 25 }, { "by": 5, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "risk", "mistral-large-4": 75, "name": "Risk", "nemotron-3-5-bolt-e4b-4gb": 80, "weight": 20 }, { "by": 10, "edge": "mistral-large-4", "key": "interface", "mistral-large-4": 95, "name": "Agent interface", "nemotron-3-5-bolt-e4b-4gb": 85, "weight": 10 }, { "by": 1, "edge": "nemotron-3-5-bolt-e4b-4gb", "key": "upkeep", "mistral-large-4": 64, "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 Mistral Large 4's 54.6 (C), and leads in 3 of 4 scored categories. Mistral Large 4 leads on agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "mistral-large-4": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.", "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/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb", "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.min.md" }, "markdown": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 4 scored categories. Mistral Large 4 leads on agent interface. Both do open-weight models you run yourself.\n\n- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json\n- NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #46 of 117 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 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Mistral Large 4 (C)\n\nGood for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.\n\nAhead on:\n- Agent interface, 95 against 85\n\nWatch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine\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- Reach, 90 against 50\n- Risk, 80 against 75\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 | Mistral Large 4 | 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) | 50 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +40 |\n| Risk | 20% (20 this run) | 75 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +5 |\n| Agent interface | 10% (10 this run) | 95 | 85 | Mistral Large 4 +10 |\n| Documentation and upkeep | 10% (10 this run) | 64 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.6 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Mistral Large 4 | NVIDIA Nemotron 3.5 Bolt E4B 4GB |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral 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 | Mistral Ecosystem Open-Weight License (modified MIT) | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-29 | 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 | none | 4/5 (4) |\n\n## Verdicts\n\n**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.\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### Mistral Large 4\n\n1. Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it\n2. On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe\n3. Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch\n4. Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too\n5. Use temperature 1.0 and top_p 0.95 at every effort, per the card\n6. Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model\n7. The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message\n8. Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head\n9. If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence\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, Mistral Large 4 or NVIDIA Nemotron 3.5 Bolt E4B 4GB?\n\nNVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 4 scored categories. Mistral Large 4 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-large-4\", \"b\": \"nemotron-3-5-bolt-e4b-4gb\"}`. From a terminal: `anchor compare mistral-large-4 nemotron-3-5-bolt-e4b-4gb`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json\n\n## Other comparisons with Mistral Large 4 or NVIDIA Nemotron 3.5 Bolt E4B 4GB\n\n- [Agents-A1-4B vs Mistral Large 4](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.md)\n- [Apodex-1.1-mini vs NVIDIA Nemotron 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4GB](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 E2B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4.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 Mistral Large 4](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4.md)\n- [gpt-oss-20b vs NVIDIA Nemotron 3.5 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Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2.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": "2026-10-31", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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": "Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "" } ], "description": "NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to Mistral Large 4's 54.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Mistral Large 4 C 54.6", "NVIDIA Nemotron 3.5 Bolt E4B 4GB C 61", "scores" ], "h1": "Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "image": "https://www.anchorterminal.com/assets/og/compare-mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.png", "path": "/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb", "published": "2026-10-01", "section": "tools", "title": "Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB (2026)", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb" }, "tokens": { "markdown": 7550, "slim": 480 }, "version": 1 }