{ "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", "dns": "mistral-large-4.grade.anchorterminal.com", "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": 1518, "categoryRank": 95, "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": "2026-11-04" }, "negative": 0, "verdict": "A 1T-parameter mixture-of-experts model from Mistral 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 no released vLLM or SGLang loads it. The licence 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", "A technical report in the repository since 2 November 2026 covers architecture, pretraining mix, post-training and serving" ], "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 on 4 November 2026 (v0.33.0 and v0.5.23), 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 internal runs, and no limitations, training data sources or knowledge cut-off are published", "The 1M context comes from YaRN scaling over a 40K pretraining 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": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "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", "devstral-small-2" ], "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": "2026-11-04", "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", "live": { "slug": "mistral-large-4", "securityTxt": { "url": "https://mistral.ai/.well-known/security.txt", "state": "valid", "expires": "2027-05-05T23:59:59.000Z", "checkedAt": "2026-11-10T16:09:22.417413189Z" }, "domain": { "domain": "mistral.ai", "registered": "2019-05-15", "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai", "checkedAt": "2026-11-08T14:56:43.249208963Z" }, "pages": [ { "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:10.57278799Z", "changedAt": "2026-11-10T16:23:10.57278799Z", "fingerprint": "08ae42b78905" } ], "updatedAt": "2026-11-10T16:23:10.57278799Z" } }, "answer": "Mistral Large 4 scores 54.6 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.", "b": { "slug": "qwen-agentworld-35b-a3b", "name": "Qwen-AgentWorld-35B-A3B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen-AgentWorld-35B-A3B is Alibaba's open-weight language world model, a 35B mixture of experts with 3B active that simulates agent environments by predicting the next observation across seven domains, published in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json", "dns": "qwen-agentworld-35b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen-AgentWorld-35B-A3B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a LICENSE file in the repository. Running it yourself costs the hardware, 69.3 GB for the BF16 files, or 22.1 GB for Unsloth's 4-bit GGUF (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "world-model", "agent-simulation", "reasoning", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-06-25", "graded": true, "anchor": { "graded": true, "score": 52.1, "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": 1518, "categoryRank": 104, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.", "bestFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "strengths": [ "Simulates seven kinds of agent environment in one model, with the system prompts for each published", "Apache 2.0, ungated, safetensors only", "Runs in vLLM, SGLang, Transformers and llama.cpp from their releases", "Fits a 128 GB machine in BF16, and 32 GB from Unsloth's 4-bit GGUF", "A 262,144-token context for long interaction histories" ], "weaknesses": [ "Not a general assistant, and the card doesn't describe using it to call tools", "No quantised build from Alibaba, so the GGUF route depends on a third party", "No reply from Alibaba on any of 10 discussions, and no change to the files since 25 June 2026", "No limitations, safety results or security contact on the card", "Its results are on Alibaba's own benchmark" ], "agentNotes": [ "Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation", "With vLLM pass `--language-model-only`, or startup fails on the missing vision weights", "Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends", "Keep at least 128K of context, since the simulation depends on the history", "Thinking is on by default and comes back through the qwen3 reasoning parser", "A simulated observation is a prediction. Check anything that matters against the real environment" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.1 } ], "editorialScores": { "interface": 50, "reach": 55, "risk": 55, "upkeep": 41 }, "provenanceScore": 66, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM, as the card gives it (four GPUs)\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B --port 8000 --tensor-parallel-size 4 \\\n --max-model-len 262144 --reasoning-parser qwen3 --language-model-only --trust-remote-code\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"Qwen/Qwen-AgentWorld-35B-A3B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.6}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen-agentworld-35b-a3b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen)", "builtOn": "Qwen3.5-35B-A3B-Base, named on the card and in its metadata", "params": "34.7B", "active": "3B", "context": 262144, "goodFor": "A simulator of agent environments (MCP tools, search, terminal, software engineering, Android, web and OS) that predicts the next observation, for training and testing agents without the real environment.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q4_k_m", "sizeGB": 22.13, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 22.13 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (Q8_0)", "url": "https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF", "who": "unsloth", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Alibaba's. 36.90 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 69.32, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Alibaba publishes. Language weights only" } ], "run": "llama-server -hf unsloth/Qwen-AgentWorld-35B-A3B-GGUF:UD-Q4_K_M --jinja", "runBuild": "unsloth Qwen-AgentWorld-35B-A3B-GGUF (UD-Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 32 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen-AgentWorld", "url": "https://www.anchorterminal.com/open-weights/#qwen", "olderCount": 109 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file names Alibaba Cloud, as the Qwen3.6-35B-A3B listing records for the organisation.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, nine commits between 22 and 25 June 2026, the upload and updates to the card.", "The Qwen3.6-35B-A3B listing found no security.txt on qwen.ai. unchecked on 10 November." ], "score": 66 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.json" }, "edges": [ { "by": 45, "category": "interface", "leads": "mistral-large-4", "name": "Agent interface", "reasons": { "a": "The chat template is in the repository (20). The card documents how tools are declared and come back and names the parser (25), and the template renders several calls in one turn (20). Strict JSON Schema output is documented with how to ask for it (15). Reasoning effort is budgeted per request with the reasoning returned apart from the answer (10). Context scores 5, because the 1M window comes from YaRN scaling and the technical report gives 40K as the longest pretraining sequence. 20 + 25 + 20 + 15 + 10 + 5 = 95.", "b": "Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50." }, "scores": { "a": 95, "b": 50 } }, { "by": 23, "category": "upkeep", "leads": "mistral-large-4", "name": "Documentation and upkeep", "reasons": { "a": "The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14). Training data is described by its mix in the technical report, with no sources, cut-off or filtering (5). The files changed on 2 November 2026, when the report was added (20). A Mistral member replied in three of nine discussions (10). It's a first release with older Large models in their own repositories and no dated note (5), and the files agree with each other and the card (10). 14 + 5 + 20 + 10 + 5 + 10 = 64.", "b": "Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41." }, "scores": { "a": 64, "b": 41 } }, { "by": 20, "category": "risk", "leads": "mistral-large-4", "name": "Risk", "reasons": { "a": "File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template. Provenance scores 15, Mistral's verified organisation with a hash on every file. Safety evaluation scores 10, refusal and prompt-injection results Mistral ran itself, listed in its 2 November technical report as internal, and red-teaming by outside firms with nothing published. Agent guidance scores 10 for a statement of what the refusals cover and a claim of trained injection resistance, with only general safeguards. Licence risk scores 10, 5 off for the Model-as-a-Service limit. The security programme scores 5 for security.txt with no public advisories. 25 + 15 + 10 + 10 + 10 + 5 = 75.", "b": "File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55." }, "scores": { "a": 75, "b": 55 } }, { "by": 5, "category": "reach", "leads": "qwen-agentworld-35b-a3b", "name": "Reach", "reasons": { "a": "Memory scores 0, since the smallest 4-bit build, Mistral's NVFP4, is 610 GB, about 732 GB with a fifth added, far above 256 GB. Runtimes score 10, released Transformers and llama.cpp have the architecture, while vLLM v0.33.0 and SGLang v0.5.23 still lack it, the llama.cpp build Ollama 0.40.14 pins predates it, and LM Studio has no entry. Builds score 15 for Mistral's FP8 and NVFP4 releases. Terms score 10, a modified MIT licence that is free commercially except for Model-as-a-Service businesses. Download scores 10 with no gate, and hosted too scores 5 because Mistral's API serves these weights. 0 + 10 + 15 + 10 + 10 + 5 = 50.", "b": "Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55." }, "scores": { "a": 50, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral AI", "b": "Alibaba (Qwen team)", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-29", "b": "2026-06-25", "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" } ], "faq": [ { "answer": "Mistral Large 4 scores 54.6 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.", "question": "Which is better for AI agents, Mistral Large 4 or Qwen-AgentWorld-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 75 against 55", "Agent interface, 95 against 50", "Documentation and upkeep, 64 against 41" ], "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, 55 against 50" ], "also": null, "goodFor": "Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine", "slug": "qwen-agentworld-35b-a3b", "watchFor": "Not a general assistant, and the card doesn't describe using it to call tools" } ], "history": null, "job": { "capability": "model.open-weights", "name": 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"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-qwen-agentworld-35b-a3b.json", "title": "Inkling-Small vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b" }, { "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-qwen-agentworld-35b-a3b.json", "title": "Inkling vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.json", "title": 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"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-qwen-agentworld-35b-a3b.json", "title": "Kimi K3 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b" }, { "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-qwen-agentworld-35b-a3b.json", "title": "Kolibri-1 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.json", "title": "Kronos vs Mistral Large 4", "url": 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"https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.json", "title": "Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b" }, { "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-qwen-agentworld-35b-a3b.json", "title": "Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b" }, { "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-qwen-agentworld-35b-a3b.json", "title": 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"json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mistral-large-4.json", "title": "LFM3-VL-2B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b.json", "title": "LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b" }, { "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-qwen-agentworld-35b-a3b.json", "title": "Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mistral-large-4.json", "title": "Ling-3.1-flash-1019 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b.json", "title": "Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mistral-large-4.json", "title": "LongCat-2.5 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.json", "title": "LongCat-2.5 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b" }, { "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 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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-north-mini-code-1-0.json", 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Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen-AgentWorld-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint.json", "title": "Qwen-AgentWorld-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b.json", "title": "Qwen-AgentWorld-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash.json", "title": "Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2.json", "title": "Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b.json", "title": "Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b.json", "title": "Qwen-AgentWorld-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "qwen-agentworld-35b-a3b", "key": "reach", "mistral-large-4": 50, "name": "Reach", "qwen-agentworld-35b-a3b": 55, "weight": 25 }, { "by": 20, "edge": "mistral-large-4", "key": "risk", "mistral-large-4": 75, "name": "Risk", "qwen-agentworld-35b-a3b": 55, "weight": 20 }, { "by": 45, "edge": "mistral-large-4", "key": "interface", "mistral-large-4": 95, "name": "Agent interface", "qwen-agentworld-35b-a3b": 50, "weight": 10 }, { "by": 23, "edge": "mistral-large-4", "key": "upkeep", "mistral-large-4": 64, "name": "Documentation and upkeep", "qwen-agentworld-35b-a3b": 41, "weight": 10 } ], "summary": "Mistral Large 4 scores 54.6 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "mistral-large-4": "A 1T-parameter mixture-of-experts model from Mistral 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 no released vLLM or SGLang loads it. The licence bars Model-as-a-Service businesses without a separate deal.", "qwen-agentworld-35b-a3b": "A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b", "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.min.md" }, "markdown": "Mistral Large 4 scores 54.6 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. Both do open-weight models you run yourself.\n\n- Mistral Large 4: grade C, 54.6/100, rank #128 of 160 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- Qwen-AgentWorld-35B-A3B: grade D, 52.1/100, rank #141 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.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 6359 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- Risk, 75 against 55\n- Agent interface, 95 against 50\n- Documentation and upkeep, 64 against 41\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### Qwen-AgentWorld-35B-A3B (D)\n\nGood for: Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine\n\nAhead on:\n- Reach, 55 against 50\n\nWatch for: Not a general assistant, and the card doesn't describe using it to call tools\n\n\n## Why the edge\n\n### Agent interface: Mistral Large 4 +45\n\n- Mistral Large 4 (95): The chat template is in the repository (20). The card documents how tools are declared and come back and names the parser (25), and the template renders several calls in one turn (20). Strict JSON Schema output is documented with how to ask for it (15). Reasoning effort is budgeted per request with the reasoning returned apart from the answer (10). Context scores 5, because the 1M window comes from YaRN scaling and the technical report gives 40K as the longest pretraining sequence. 20 + 25 + 20 + 15 + 10 + 5 = 95.\n- Qwen-AgentWorld-35B-A3B (50): Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.\n\n### Documentation and upkeep: Mistral Large 4 +23\n\n- Mistral Large 4 (64): The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14). Training data is described by its mix in the technical report, with no sources, cut-off or filtering (5). The files changed on 2 November 2026, when the report was added (20). A Mistral member replied in three of nine discussions (10). It's a first release with older Large models in their own repositories and no dated note (5), and the files agree with each other and the card (10). 14 + 5 + 20 + 10 + 5 + 10 = 64.\n- Qwen-AgentWorld-35B-A3B (41): Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.\n\n### Risk: Mistral Large 4 +20\n\n- Mistral Large 4 (75): File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template. Provenance scores 15, Mistral's verified organisation with a hash on every file. Safety evaluation scores 10, refusal and prompt-injection results Mistral ran itself, listed in its 2 November technical report as internal, and red-teaming by outside firms with nothing published. Agent guidance scores 10 for a statement of what the refusals cover and a claim of trained injection resistance, with only general safeguards. Licence risk scores 10, 5 off for the Model-as-a-Service limit. The security programme scores 5 for security.txt with no public advisories. 25 + 15 + 10 + 10 + 10 + 5 = 75.\n- Qwen-AgentWorld-35B-A3B (55): File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map. Provenance 15, files sit in the Qwen organisation, the one Alibaba's Qwen repositories on GitHub link to per the Qwen3.6-35B-A3B listing (10), and the Hub gives a SHA-256 for every large file (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, refusal statement or general cautions on the card. Licence risk 15, Apache 2.0 has no use policy, no threshold and no terms the licensor can change or revoke, and the card's licence link points at the LICENSE file in the repository. Security programme 0, no reporting route on the card. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n\n### Reach: Qwen-AgentWorld-35B-A3B +5\n\n- Mistral Large 4 (50): Memory scores 0, since the smallest 4-bit build, Mistral's NVFP4, is 610 GB, about 732 GB with a fifth added, far above 256 GB. Runtimes score 10, released Transformers and llama.cpp have the architecture, while vLLM v0.33.0 and SGLang v0.5.23 still lack it, the llama.cpp build Ollama 0.40.14 pins predates it, and LM Studio has no entry. Builds score 15 for Mistral's FP8 and NVFP4 releases. Terms score 10, a modified MIT licence that is free commercially except for Model-as-a-Service businesses. Download scores 10 with no gate, and hosted too scores 5 because Mistral's API serves these weights. 0 + 10 + 15 + 10 + 10 + 5 = 50.\n- Qwen-AgentWorld-35B-A3B (55): Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.\n\n## Score by category\n\n| Category | Weight | Mistral Large 4 | Qwen-AgentWorld-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 55 | Qwen-AgentWorld-35B-A3B +5 |\n| Risk | 20% (20 this run) | 75 | 55 | Mistral Large 4 +20 |\n| Agent interface | 10% (10 this run) | 95 | 50 | Mistral Large 4 +45 |\n| Documentation and upkeep | 10% (10 this run) | 64 | 41 | Mistral Large 4 +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.6 · C** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Mistral Large 4 | Qwen-AgentWorld-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | Alibaba (Qwen team) |\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) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-29 | 2026-06-25 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Mistral Large 4.** A 1T-parameter mixture-of-experts model from Mistral 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 no released vLLM or SGLang loads it. The licence bars Model-as-a-Service businesses without a separate deal.\n\n**Qwen-AgentWorld-35B-A3B.** A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.\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### Qwen-AgentWorld-35B-A3B\n\n1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation\n2. With vLLM pass `--language-model-only`, or startup fails on the missing vision weights\n3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends\n4. Keep at least 128K of context, since the simulation depends on the history\n5. Thinking is on by default and comes back through the qwen3 reasoning parser\n6. A simulated observation is a prediction. Check anything that matters against the real environment\n\n## Questions\n\n### Which is better for AI agents, Mistral Large 4 or Qwen-AgentWorld-35B-A3B?\n\nMistral Large 4 scores 54.6 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen-agentworld-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-large-4\", \"b\": \"qwen-agentworld-35b-a3b\"}`. From a terminal: `anchor compare mistral-large-4 qwen-agentworld-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/qwen-agentworld-35b-a3b.json\n\n## Other comparisons with Mistral Large 4 or Qwen-AgentWorld-35B-A3B\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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen-agentworld-35b-a3b.md)\n- [Arete-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4.md)\n- [Arete-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.md)\n- [BigBang-v1 vs Mistral Large 4](https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.md)\n- [BigBang-v1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen-agentworld-35b-a3b.md)\n- [Carbon-X vs Mistral Large 4](https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.md)\n- [Carbon-X vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-qwen-agentworld-35b-a3b.md)\n- [Cola2 vs Mistral Large 4](https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4.md)\n- [Cola2 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b.md)\n- [Cosmos-Reason2-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.md)\n- [Cosmos-Reason2-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen-agentworld-35b-a3b.md)\n- [Darwin-36B-Opus vs Mistral Large 4](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4.md)\n- [Darwin-36B-Opus vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen-agentworld-35b-a3b.md)\n- [DeepSeek-V3.2 vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4.md)\n- [DeepSeek-V3.2 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen-agentworld-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4.md)\n- [DeepSeek V4.1 Flash vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen-agentworld-35b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen-agentworld-35b-a3b.md)\n- [Devstral Small 2 vs Mistral Large 4](https://www.anchorterminal.com/compare/devstral-small-2-vs-mistral-large-4.md)\n- [Devstral Small 2 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen-agentworld-35b-a3b.md)\n- [DiffusionGemma 26B A4B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4.md)\n- [DiffusionGemma 26B A4B IT vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen-agentworld-35b-a3b.md)\n- [EXAONE 4.5 33B vs Mistral Large 4](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mistral-large-4.md)\n- [EXAONE 4.5 33B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-qwen-agentworld-35b-a3b.md)\n- [Gemma 4 12B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mistral-large-4.md)\n- [Gemma 4 12B IT vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen-agentworld-35b-a3b.md)\n- [Gemma 4 26B A4B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mistral-large-4.md)\n- [Gemma 4 26B A4B IT vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen-agentworld-35b-a3b.md)\n- [Gemma 4 31B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4.md)\n- [Gemma 4 31B IT vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen-agentworld-35b-a3b.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 Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b.md)\n- [Granite 4.2 30B vs Mistral Large 4](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mistral-large-4.md)\n- [Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-qwen-agentworld-35b-a3b.md)\n- [Granite 4.2 3B vs Mistral Large 4](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.md)\n- [Granite 4.2 3B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen-agentworld-35b-a3b.md)\n- [Granite 4.2 8B vs Mistral Large 4](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mistral-large-4.md)\n- [Granite 4.2 8B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-qwen-agentworld-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs Mistral Large 4](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mistral-large-4.md)\n- [Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.md)\n- [Hy3 vs Mistral Large 4](https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4.md)\n- [Hy3 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/hy3-vs-qwen-agentworld-35b-a3b.md)\n- [Inkling-Small vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4.md)\n- [Inkling-Small vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen-agentworld-35b-a3b.md)\n- [Inkling vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4.md)\n- [Inkling vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-qwen-agentworld-35b-a3b.md)\n- [Kimi K3.1 Pro vs Mistral Large 4](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4.md)\n- [Kimi K3.1 Pro vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen-agentworld-35b-a3b.md)\n- [Kimi K3 vs Mistral Large 4](https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4.md)\n- [Kimi K3 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.md)\n- [Kolibri-1 vs Mistral Large 4](https://www.anchorterminal.com/compare/kolibri-1-vs-mistral-large-4.md)\n- [Kolibri-1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b.md)\n- [Kronos vs Mistral Large 4](https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.md)\n- [Kronos vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b.md)\n- [Laguna M.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-m-1-vs-mistral-large-4.md)\n- [Laguna M.1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen-agentworld-35b-a3b.md)\n- [Laguna S 2.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mistral-large-4.md)\n- [Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-qwen-agentworld-35b-a3b.md)\n- [Laguna XS 2.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mistral-large-4.md)\n- [Laguna XS 2.1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen-agentworld-35b-a3b.md)\n- [LBS-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4.md)\n- [LBS-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM2.5-2.6B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4.md)\n- [LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-1.7B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mistral-large-4.md)\n- [LFM3-1.7B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-Audio-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mistral-large-4.md)\n- [LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-Omni-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mistral-large-4.md)\n- [LFM3-Omni-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b.md)\n- [LFM3-VL-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mistral-large-4.md)\n- [LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen-agentworld-35b-a3b.md)\n- [Ling-3.0-tiny vs Mistral Large 4](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mistral-large-4.md)\n- [Ling-3.0-tiny vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-qwen-agentworld-35b-a3b.md)\n- [Ling-3.1-flash-1019 vs Mistral Large 4](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mistral-large-4.md)\n- [Ling-3.1-flash-1019 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-qwen-agentworld-35b-a3b.md)\n- [LongCat-2.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/longcat-2-5-vs-mistral-large-4.md)\n- [LongCat-2.5 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Mistral Large 4](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen-agentworld-35b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Mistral Large 4](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-mistral-large-4.md)\n- [MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.md)\n- [MiniCPM-o 4.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4.md)\n- [MiniCPM-o 4.5 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-qwen-agentworld-35b-a3b.md)\n- [MiniCPM-V 4.7 1B vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-mistral-large-4.md)\n- [MiniCPM-V 4.7 1B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-qwen-agentworld-35b-a3b.md)\n- [MiniCPM-V 4.7 35B-A3B vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-mistral-large-4.md)\n- [MiniCPM-V 4.7 35B-A3B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b.md)\n- [MiniCPM5-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4.md)\n- [MiniCPM5-2B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-qwen-agentworld-35b-a3b.md)\n- [MiniMax-M2.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4.md)\n- [MiniMax-M2.5 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen-agentworld-35b-a3b.md)\n- [MiniMax-M2.7 vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4.md)\n- [MiniMax-M2.7 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-qwen-agentworld-35b-a3b.md)\n- [MiniMax-M3.1-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-mistral-large-4.md)\n- [MiniMax-M3.1-Flash vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-qwen-agentworld-35b-a3b.md)\n- [Mistral Large 4 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mistral-large-4-vs-muse-glimmer-30b.md)\n- [Mistral Large 4 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-2-3b.md)\n- [Mistral Large 4 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-3-33b.md)\n- [Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Mistral Large 4 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Mistral Large 4 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.md)\n- [Mistral Large 4 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-4b.md)\n- [Mistral Large 4 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Mistral Large 4 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-super-120b-a12b.md)\n- [Mistral Large 4 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-ultra-550b-a55b.md)\n- [Mistral Large 4 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/mistral-large-4-vs-north-mini-code-1-0.md)\n- [Mistral Large 4 vs occamy-1.0](https://www.anchorterminal.com/compare/mistral-large-4-vs-occamy-1-0.md)\n- [Mistral Large 4 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/mistral-large-4-vs-olmo-3-7b-think.md)\n- [Mistral Large 4 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-122b.md)\n- [Mistral Large 4 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-35b-a3b.md)\n- [Mistral Large 4 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-397b.md)\n- [Mistral Large 4 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b.md)\n- [Mistral Large 4 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mistral-large-4-vs-otel-2-0-llm-31b-it.md)\n- [Mistral Large 4 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Mistral Large 4 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-4b-instruct-2507.md)\n- [Mistral Large 4 vs 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