{ "data": { "a": { "slug": "deepseek-v4-flash-0731", "name": "DeepSeek-V4-Flash-0731", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek-V4-Flash-0731 is the 31 July 2026 release of DeepSeek's V4 Flash, a mixture-of-experts text model with 284B parameters, 13B active and a 1M-token context, under MIT. DeepSeek's API retired V4 Flash on 10 September 2026.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4-Flash-0731" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's API retired V4 Flash on 10 September 2026 and routes its old names to V4.1 Flash, so no hosted DeepSeek endpoint serves these weights. Running them yourself costs the hardware, 166.9 GB of files (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "long-context", "mit", "safetensors", "fp8", "fp4", "self-hosted", "new" ], "lastRelease": "2026-07-31", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "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": 23, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "bestFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context", "strengths": [ "MIT licence with nothing added, downloadable with no account", "1M tokens of context trained in", "Runs in vLLM, SGLang, llama.cpp, Transformers and LM Studio from released versions", "Parallel tool calls in a documented DSML format, with reasoning off or at three effort levels", "Six live inference providers on Hugging Face for agents without the hardware", "Safetensors only, with a SHA-256 on every shard" ], "weaknesses": [ "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine", "No Jinja template, so prompts come from running DeepSeek's encoding code or a runtime's own encoder", "No safety evaluation or agent-use guidance published, and only a company-wide security address", "DeepSeek hasn't replied to any of the 61 discussions, including reports of tool-call errors", "The card lacks intended use, limitations, languages and a knowledge cut-off" ], "agentNotes": [ "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)", "Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template", "Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"", "Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max", "Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash", "LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 66, "interface": 75, "reach": 70, "risk": 50, "upkeep": 43 }, "provenanceScore": 75, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# From DeepSeek's card (one 4x GB300 node). Add vLLM's --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 for tool calls\nvllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \\\n --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \\\n --data-parallel-size 4 --enable-expert-parallel \\\n --moe-backend deep_gemm_mega_moe \\\n --attention-config '{\"use_fp4_indexer_cache\": true}' \\\n --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-flash-0731" }, "sameCompany": [ "deepseek-api", "deepseek-search-api", "deepseek-v4-1-flash", "deepseek-v3-2" ], "weights": { "trainedBy": "DeepSeek", "params": "284B", "active": "13B", "context": 1048576, "goodFor": "Text model for agents and long documents, with thinking or chat mode, reasoning effort and tool calls in DSML tags.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "DeepSeek-V4-Flash-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 166.9, "memoryGB": 256, "format": "safetensors", "note": "MoE experts in FP4, most other weights FP8; the only build DeepSeek publishes" } ], "run": "vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4", "runBuild": "DeepSeek-V4-Flash-0731", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V4", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V4-Flash-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-Vision-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp", "params": "not stated on card" }, { "name": "DeepSeek-V4-Pro-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-0813", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813", "params": "1.6T total, 49B activated (V4-Pro figures; the 0813 card does not restate them)" }, { "name": "DeepSeek-V4.1-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash", "params": "552B backbone, 8B activated in prefill / 16B in decode", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" }, { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "params": "552B backbone, 8B / 16B activated (V4.1-Flash figures; not restated)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" } ], "olderCount": 72, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, four commits from 31 July to 1 August 2026 (the release and a card edit adding the SGLang cookbook).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.", "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.json", "live": { "slug": "deepseek-v4-flash-0731", "securityTxt": { "url": "https://deepseek.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:38:15.598857779Z" }, "domain": { "domain": "deepseek.com", "registered": "2000-08-13", "source": "https://rdap.verisign.com/com/v1/domain/deepseek.com", "checkedAt": "2026-10-25T13:50:05.058593646Z" }, "pages": [ { "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-30T05:41:37.804214097Z", "changedAt": "2026-10-30T05:41:37.804214097Z", "fingerprint": "1cb7e5ed16e7" } ], "updatedAt": "2026-10-30T15:38:15.598857779Z" } }, "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 5 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "Mistral AI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Mistral Ecosystem Open-Weight License (modified MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-31", "b": "2026-10-29", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 5 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, DeepSeek-V4-Flash-0731 or Mistral Large 4?" } ], "goodFor": [ { "aheadOn": [ "Capability, 66 against 32", "Reach, 70 against 50" ], "also": null, "goodFor": "Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context", "slug": "deepseek-v4-flash-0731", "watchFor": "The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine" }, { "aheadOn": [ "Risk, 75 against 50", "Agent interface, 95 against 75", "Documentation and upkeep, 64 against 43" ], "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.json", "title": "Agents-A1-4B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731" }, { "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/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json", "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731" }, { "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/apodex-1-1-mini-vs-deepseek-v4-flash-0731.json", "title": "Apodex-1.1-mini vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731" }, { "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/bigbang-v1-vs-deepseek-v4-flash-0731.json", "title": "BigBang-v1 vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.json", "title": "BigBang-v1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4" }, { "json": 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IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e4b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air.json", "title": "DeepSeek-V4-Flash-0731 vs GLM-4.5-Air", "url": 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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-bolt-e4b-4gb.json", "title": "Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "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-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": 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"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", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-27b.json", "title": "Mistral Large 4 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next.json", "title": "Mistral Large 4 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Mistral Large 4 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-next.json", "title": "Mistral Large 4 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Mistral Large 4 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-2b-instruct.json", "title": "Mistral Large 4 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Mistral Large 4 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct.json", "title": "Mistral Large 4 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct.json", "title": "Mistral Large 4 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct.json", "title": "Mistral Large 4 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwopus3-8-27b-flash-v2.json", "title": "Mistral Large 4 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.json", "title": "Mistral Large 4 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b.json", "title": "Mistral Large 4 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b.json", "title": "Mistral Large 4 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.json", "title": "Mistral Large 4 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Mistral Large 4 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b.json", "title": "Mistral Large 4 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2.json", "title": "Mistral Large 4 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2" } ], "scores": [ { "by": 34, "deepseek-v4-flash-0731": 66, "edge": "deepseek-v4-flash-0731", "key": "capability", "mistral-large-4": 32, "name": "Capability", "weight": 35 }, { "by": 20, "deepseek-v4-flash-0731": 70, "edge": "deepseek-v4-flash-0731", "key": "reach", "mistral-large-4": 50, "name": "Reach", "weight": 25 }, { "by": 25, "deepseek-v4-flash-0731": 50, "edge": "mistral-large-4", "key": "risk", "mistral-large-4": 75, "name": "Risk", "weight": 20 }, { "by": 20, "deepseek-v4-flash-0731": 75, "edge": "mistral-large-4", "key": "interface", "mistral-large-4": 95, "name": "Agent interface", "weight": 10 }, { "by": 21, "deepseek-v4-flash-0731": 43, "edge": "mistral-large-4", "key": "upkeep", "mistral-large-4": 64, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 5 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-flash-0731": "A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4", "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.min.md" }, "markdown": "DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 5 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #30 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json\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- 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### DeepSeek-V4-Flash-0731 (B)\n\nGood for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context\n\nAhead on:\n- Capability, 66 against 32\n- Reach, 70 against 50\n\nWatch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine\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 50\n- Agent interface, 95 against 75\n- Documentation and upkeep, 64 against 43\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\n## Score by category\n\n| Category | Weight | DeepSeek-V4-Flash-0731 | Mistral Large 4 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 66 | 32 | DeepSeek-V4-Flash-0731 +34 |\n| Reach | 25% (25 this run) | 70 | 50 | DeepSeek-V4-Flash-0731 +20 |\n| Risk | 20% (20 this run) | 50 | 75 | Mistral Large 4 +25 |\n| Agent interface | 10% (10 this run) | 75 | 95 | Mistral Large 4 +20 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 64 | Mistral Large 4 +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **54.6 · C** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V4-Flash-0731 | Mistral Large 4 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | Mistral AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Mistral Ecosystem Open-Weight License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-31 | 2026-10-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.5/5 (4) | none |\n\n## Verdicts\n\n**DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository.\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## Before you call either\n\n### DeepSeek-V4-Flash-0731\n\n1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{\"use_fp4_indexer_cache\": true}' --speculative-config '{\"method\":\"dspark\",\"num_speculative_tokens\":7,\"draft_sample_method\":\"greedy\"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe)\n2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template\n3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string=\"true\" and JSON values string=\"false\"\n4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max\n5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash\n6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory\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## Questions\n\n### Which is better for AI agents, DeepSeek-V4-Flash-0731 or Mistral Large 4?\n\nDeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 5 scored categories. Mistral Large 4 leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-flash-0731\", \"b\": \"mistral-large-4\"}`. From a terminal: `anchor compare deepseek-v4-flash-0731 mistral-large-4`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json and https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json\n\n## Other comparisons with DeepSeek-V4-Flash-0731 or Mistral Large 4\n\n- [Agents-A1-4B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.md)\n- [Agents-A1-4B vs Mistral Large 4](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.md)\n- [Apertus v1.5 8B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.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- [Apodex-1.1-mini vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731.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- [BigBang-v1 vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731.md)\n- [BigBang-v1 vs Mistral Large 4](https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.md)\n- [Carbon-X vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-flash-0731.md)\n- [Carbon-X vs Mistral Large 4](https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.md)\n- [Cosmos-Reason2-2B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-flash-0731.md)\n- [Cosmos-Reason2-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.md)\n- [Darwin-36B-Opus vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-flash-0731.md)\n- [Darwin-36B-Opus vs Mistral Large 4](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4.md)\n- [DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.md)\n- [DeepSeek-V3.2 vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4.md)\n- [DeepSeek V4.1 Flash vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731.md)\n- [DeepSeek V4.1 Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4.md)\n- [DeepSeek-V4-Flash-0731 vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-diffusiongemma-26b-a4b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-12b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-31b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e4b-it.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-7-flash.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-5.3](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.md)\n- 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K3](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.md)\n- [DeepSeek-V4-Flash-0731 vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1.md)\n- [DeepSeek-V4-Flash-0731 vs Kronos](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kronos.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-m-1.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-s-2-1.md)\n- [DeepSeek-V4-Flash-0731 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-laguna-xs-2-1.md)\n- [DeepSeek-V4-Flash-0731 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.md)\n- [DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V4-Flash-0731 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-0-tiny.md)\n- [DeepSeek-V4-Flash-0731 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-distill-qwen-9b.md)\n- [DeepSeek-V4-Flash-0731 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm-o-4-5.md)\n- [DeepSeek-V4-Flash-0731 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minicpm5-2b.md)\n- [DeepSeek-V4-Flash-0731 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5.md)\n- [DeepSeek-V4-Flash-0731 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-7.md)\n- [DeepSeek-V4-Flash-0731 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m3-1-flash.md)\n- [DeepSeek-V4-Flash-0731 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-muse-glimmer-30b.md)\n- [DeepSeek-V4-Flash-0731 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-3-33b.md)\n- [DeepSeek-V4-Flash-0731 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-4b.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-ultra-550b-a55b.md)\n- [DeepSeek-V4-Flash-0731 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0.md)\n- [DeepSeek-V4-Flash-0731 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-olmo-3-7b-think.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-9b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-30b-a3b-instruct-2507.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-4b-instruct-2507.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-0-8b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-2b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-397b-a17b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-4b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-9b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-27b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-6-35b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-8-27b.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-8-flash-next.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-30b-a3b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-next.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-omni-30b-a3b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-2b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-30b-a3b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-32b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek-V4-Flash-0731 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-vl-8b-instruct.md)\n- 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[DeepSeek-V4-Flash-0731 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-unlimited-ocr-2.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- [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 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 31B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4.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 E4B IT vs Mistral Large 4](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.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-4.7-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4.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 vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4.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-20b vs Mistral Large 4](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4.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- [Hy3 vs Mistral Large 4](https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4.md)\n- [Inkling-Small vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4.md)\n- [Inkling vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4.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- [Kimi K3 vs Mistral Large 4](https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4.md)\n- [Kolibri-1 vs Mistral Large 4](https://www.anchorterminal.com/compare/kolibri-1-vs-mistral-large-4.md)\n- [Kronos vs Mistral Large 4](https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.md)\n- [Laguna M.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-m-1-vs-mistral-large-4.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 XS 2.1 vs Mistral Large 4](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mistral-large-4.md)\n- [LBS-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/lbs-27b-vs-mistral-large-4.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- [Ling-3.0-tiny vs Mistral Large 4](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mistral-large-4.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- [MiniCPM-o 4.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4.md)\n- [MiniCPM5-2B vs Mistral Large 4](https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4.md)\n- [MiniMax-M2.5 vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4.md)\n- [MiniMax-M2.7 vs Mistral Large 4](https://www.anchorterminal.com/compare/minimax-m2-7-vs-mistral-large-4.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- [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 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vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b.md)\n- [Mistral Large 4 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/mistral-large-4-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": "DeepSeek-V4-Flash-0731 vs Mistral Large 4", "url": "" } ], "description": "DeepSeek-V4-Flash-0731 scores 62.4 (B) 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": [ "DeepSeek-V4-Flash-0731 B 62.4", "Mistral Large 4 C 54.6", "scores" ], "h1": "DeepSeek-V4-Flash-0731 vs Mistral Large 4", "image": "https://www.anchorterminal.com/assets/og/compare-deepseek-v4-flash-0731-vs-mistral-large-4.png", "path": "/compare/deepseek-v4-flash-0731-vs-mistral-large-4", "published": "2026-10-01", "section": "tools", "title": "DeepSeek-V4-Flash-0731 vs Mistral Large 4 for AI agents (2026)", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4" }, "tokens": { "markdown": 7300, "slim": 480 }, "version": 1 }