{ "data": { "a": { "slug": "mistral-large-4", "name": "Mistral Large 4", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "weights", "category": "open-weights", "summary": "Mistral Large 4 (\"Le Chonk\") is Mistral AI's open-weight mixture-of-experts model of about 1T parameters with 52B active, taking text and images, with three reasoning efforts and a 1M-token context, published in FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/mistral-large-4", "markdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-large-4.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json", "repo": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "license": "Mistral Ecosystem Open-Weight License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "mistralai/Mistral-Large-4-1T-A52B" }, { "registry": "huggingface", "name": "mistralai/Mistral-Large-4-1T-A52B-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both repositories report gated false (checked 2026-10-30).", "pricing": "free", "pricingNotes": "Free to download. The Mistral Ecosystem Open-Weight License allows commercial use, except that a licensee or affiliate that operates a Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use; internal use and use through Mistral's own products are exempt. Mistral's API serves it as mistral-large-4 at $0.70 input, $0.07 cached input and $2.10 output per million tokens (checked 2026-10-30).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "frontier", "reasoning", "tool-calling", "long-context", "multilingual", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 54.6, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1239, "categoryRank": 72, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 32, "interface": 95, "reach": 50, "risk": 75, "upkeep": 64 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-30" }, "negative": 0, "verdict": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.", "bestFor": "Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.", "strengths": [ "Safetensors only, loaded by Transformers' own code with a Jinja template, in Mistral's verified organisation with a hash on every file", "Tool calling documented end to end with a named parser, several calls a turn and strict JSON Schema output", "Reasoning effort budgeted per request, low, medium or high, with the reasoning returned apart from the answer", "FP8 and NVFP4 builds from Mistral, and Mistral's own API serves the same model for agents without the hardware", "First among open-weight models on Cognition's FrontierCode 2.0 leaderboard, run in Mistral's own harness" ], "weaknesses": [ "The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine", "No vLLM or SGLang release supports it yet, only Docker images tagged mistral-large-4", "A Model-as-a-Service business needs a separate commercial licence from Mistral before any commercial use", "Safety results are Mistral's own with no settings to rerun them, and no limitations, training data sources or knowledge cut-off are published", "The 1M context comes from YaRN scaling with no stated trained length, and thinking can't be turned off" ], "agentNotes": [ "Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it", "On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe", "Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch", "Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too", "Use temperature 1.0 and top_p 0.95 at every effort, per the card", "Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model", "The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message", "Add `--speculative-config '{\"method\": \"dspark\", \"num_speculative_tokens\": 3}'` to use the embedded draft head", "If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 54.6 } ], "editorialScores": { "capability": 32, "interface": 95, "reach": 50, "risk": 75, "upkeep": 64 }, "provenanceScore": 84, "checked": { "categories": { "capability": "2026-10-30", "interface": "2026-10-30", "reach": "2026-10-30", "risk": "2026-10-30", "upkeep": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# NVFP4 on 4 or 8 Blackwell GPUs, with the card's vLLM image (no vLLM release supports it yet):\ndocker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host \\\n vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 \\\n --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral \\\n --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"mistralai/Mistral-Large-4-1T-A52B-NVFP4\", \"reasoning_effort\": \"medium\", \"temperature\": 1.0, \"top_p\": 0.95,\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/mistral-large-4" }, "sameCompany": [ "mistral-embeddings", "mistral-moderation", "mistral-voxtral-transcribe", "voxtral-mini-4b-realtime-2602", "mistral-ocr", "mistral-vibe-cli" ], "sameModel": [ "mistral-api" ], "weights": { "trainedBy": "Mistral AI", "params": "~1T total (1.05T per Mistral's docs), 52B active", "context": 1048576, "goodFor": "Frontier-sized multimodal model for coding agents, defensive security work and long documents, with tool calling, strict JSON Schema output and three reasoning efforts, for operators with a multi-GPU server.", "runtimes": [ "llama.cpp", "Transformers" ], "builds": [ { "name": "Mistral-Large-4-1T-A52B-NVFP4", "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 610, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (mistral-large-4 image)", "SGLang (mistral-large-4 image)" ], "note": "NVFP4 experts with FP8 linears, for Blackwell GPUs; Mistral-format params.json only, no config.json, so not for Transformers" }, { "name": "ggml-org Mistral-Large-4-1T-A52B-GGUF Q4_K_M", "url": "https://huggingface.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF", "who": "ggml-org (llama.cpp)", "precision": "q4_k_m", "sizeGB": 634.8, "memoryGB": 1024, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "llama.cpp's own organisation, linked from Mistral's card. Also BF16, Q8_0, Q2_K (380.7 GB) and IQ2_XXS (297.7 GB), plus an NVFP4 GGUF of 619.4 GB in a second repository; the 2-bit files are below the 4-bit line and aren't counted. Vision projector 3.4 GB extra" }, { "name": "Mistral-Large-4-1T-A52B", "url": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B", "who": "publisher", "precision": "fp8", "sizeGB": 1060.6, "memoryGB": 2048, "format": "safetensors", "runsOn": [ "Transformers", "vLLM (mistral-large-4 image)", "SGLang (mistral-large-4 image)" ], "note": "Block-scaled FP8 (128 by 128), the release precision; vision tower, projector, output head and some attention projections kept in BF16. Includes the DSpark draft head" } ], "run": "docker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 --ipc=host vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice --gpu-memory-utilization 0.85", "runBuild": "Mistral-Large-4-1T-A52B-NVFP4", "checked": "2026-10-30", "minMemoryGB": 1024 }, "family": { "lab": "Mistral AI", "family": "Mistral Large 4", "url": "https://www.anchorterminal.com/open-weights/#mistral", "olderCount": 49 }, "area": "models", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/mistralai/Mistral-Large-4-1T-A52B/commits/main", "securityTxt": "valid", "checked": "2026-10-30", "notes": [ "The files sit in Mistral's verified mistralai organisation, which the card, Mistral's announcement and its docs page for the model link to. Legal entity and domain registration are carried from the mistral-api listing.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored. The hosted model is mistral-api.", "The changelog is the repositories' commit history, an initial release on 29 October 2026 and two card edits the same day, plus a merged evaluation figure on the NVFP4 card.", "mistral.ai/.well-known/security.txt answered on 30 October 2026 with a security contact, an expiry of 5 May 2027 and a HackerOne submission policy." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-large-4.json" }, "answer": "Recursive Flint scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 4 scored categories. Mistral Large 4 leads on risk and agent interface.", "b": { "slug": "recursive-flint", "name": "Recursive Flint", "vendor": "Recursive", "vendorUrl": "https://www.recursive.com", "kind": "weights", "category": "open-weights", "summary": "Recursive Flint is Recursive's open-weight mixture-of-experts coding model for agents, 39.8B total and about 2.5B active parameters, with reasoning, tool calling and a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/recursive-flint", "markdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/recursive-flint.json", "repo": "https://huggingface.co/RecursiveSI/flint", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "RecursiveSI/flint" }, { "registry": "huggingface", "name": "RecursiveSI/flint-int4" }, { "registry": "huggingface", "name": "RecursiveSI/flint-int4-gguf" }, { "registry": "huggingface", "name": "RecursiveSI/flint-nvfp4-gguf" }, { "registry": "ollama", "name": "flint" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and all three quantised builds (checked 2026-10-22).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 79.6 GB for the BF16 files and 22.6 GB for Recursive's smallest 4-bit build (MLX INT4). The card mentions hosted API terms separate from the weight licence, but no Recursive API or inference provider serving Flint was confirmed (checked 2026-10-22).", "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 2026-10-22).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-22" }, "docsUrl": "https://huggingface.co/RecursiveSI/flint", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "mlx", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-21", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1239, "categoryRank": 42, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 90, "reach": 75, "risk": 60, "upkeep": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-22" }, "negative": 0, "verdict": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.", "bestFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.", "strengths": [ "Plain Apache 2.0 with no gate, account or use policy", "Recursive's own MLX INT4, Q4_1 GGUF and NVFP4 GGUF builds, the smallest 22.6 GB", "Tool format, parser, parallel calls and JSON Schema output documented by Recursive", "262,144 tokens of context with no position scaling", "Safetensors and GGUF only, with a checksum on every file in Recursive's organisation", "llama.cpp, Ollama and LM Studio support on release day" ], "weaknesses": [ "No independent benchmark results yet, every published score is Recursive's", "No reasoning switch or budget, so hard prompts can spend tens of thousands of tokens thinking", "The BF16 route needs one 141 GB GPU and Recursive's SGLang plugin, and vLLM and released Transformers don't run it", "No safety results and no trained instruction hierarchy", "No inference provider or confirmed Recursive API for agents without the hardware" ], "agentNotes": [ "Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp", "With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on", "Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out", "Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask", "Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call", "Message content must be a string, flatten content-part arrays first", "Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 90, "reach": 75, "risk": 60, "upkeep": 67 }, "provenanceScore": 47, "checked": { "categories": { "interface": "2026-10-22", "reach": "2026-10-22", "risk": "2026-10-22", "upkeep": "2026-10-22" }, "last": "2026-10-22", "oldest": "2026-10-22", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run flint\n# or, with llama.cpp b11803 or later:\nllama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 262144 -np 1 -fa on --jinja \\\n --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"flint\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/recursive-flint" }, "weights": { "trainedBy": "Recursive", "params": "39.8B total, about 2.5B active", "context": 262144, "goodFor": "Agentic coding on a 32 to 48 GB machine, investigating codebases, fixing bugs and writing tests with multi-turn tool calls and reasoning.", "runtimes": [ "llama.cpp", "Ollama", "LM Studio" ], "builds": [ { "name": "flint-int4", "url": "https://huggingface.co/RecursiveSI/flint-int4", "who": "publisher", "precision": "int4 (group-64 affine, INT8 embeddings)", "sizeGB": 22.6, "memoryGB": 32, "format": "mlx", "runsOn": [ "rec", "Ollama" ], "note": "For Apple Silicon. Recursive's rec engine asks for an M4 Pro or newer with 48 GB" }, { "name": "flint-int4-gguf Q4_1", "url": "https://huggingface.co/RecursiveSI/flint-int4-gguf", "who": "publisher", "precision": "q4_1", "sizeGB": 26, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "flint-int4-q4_1.gguf, 25,992,966,976 bytes, the INT4 export's trained codes stored as Q4_1. The card targets Metal and asks for a Mac with at least 48 GB at full context" }, { "name": "flint-nvfp4-gguf", "url": "https://huggingface.co/RecursiveSI/flint-nvfp4-gguf", "who": "publisher", "precision": "nvfp4", "sizeGB": 22.9, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "flint-nvfp4.gguf, 22,863,084,160 bytes, for NVIDIA GPUs with CUDA. The card recommends at least 48 GB at full context" }, { "name": "ollama flint:latest", "url": "https://ollama.com/library/flint:latest", "who": "ollama", "precision": "4-bit", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Digest 905c74dfd56b, shown as 23 GB (Ollama rounds to whole GB). The library also has qat-int4 (23 GB), q4_1 (26 GB), gguf-nvfp4 (23 GB) and mlx-bf16 (80 GB) tags. Ollama 0.40.8 runs Flint in its MLX runner; its bundled llama.cpp (b11738) predates Flint, so the GGUF tags on Linux weren't confirmed in a release" }, { "name": "lmstudio-community flint-GGUF Q4_K_M", "url": "https://huggingface.co/lmstudio-community/flint-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 24.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "flint-Q4_K_M.gguf, 24,166,841,184 bytes, the build behind LM Studio's recursivesi/flint catalogue page. Also Q6_K (32.7 GB, 48 GB of memory) and Q8_0 (42.4 GB, 64 GB)" }, { "name": "flint", "url": "https://huggingface.co/RecursiveSI/flint", "who": "publisher", "precision": "bf16", "sizeGB": 79.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "SGLang 0.5.16 with Recursive's flint_sglang kit" ], "note": "Recursive's SGLang kit needs one GPU with at least 141 GB (B300, B200 or H200). Transformers support isn't in a release yet" } ], "run": "llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080", "runBuild": "flint-int4-gguf Q4_1", "checked": "2026-10-22", "minMemoryGB": 32 }, "family": { "lab": "Recursive", "family": "Flint", "url": "https://www.anchorterminal.com/open-weights/#recursive" }, "area": "models", "provenance": { "legalEntity": "Recursive Superintelligence, Inc. (the Hugging Face organisation's full name)", "domain": "recursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "unknown", "checked": "2026-10-22", "notes": [ "The Hugging Face organisation RecursiveSI is named Recursive Superintelligence, Inc. and isn't verified by Hugging Face. Recursive's rec repository, which carries Recursive's logo and links recursive.com, downloads the INT4 build from RecursiveSI/flint-int4 at a pinned commit.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The card links www.recursive.com, docs.recursive.com and github.com/recursive-oss/rec. Neither recursive.com host could be read on 22 October 2026, so security.txt and the domain's registration weren't checked.", "The card names a security reporting address at recursive.com, and rec's SECURITY.md asks for private reports through GitHub's vulnerability reporting." ], "score": 47 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/recursive-flint.json", "live": { "slug": "recursive-flint", "securityTxt": { "url": "https://recursive.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:39:19.905908697Z" }, "domain": { "domain": "recursive.com", "registered": "1995-08-24", "source": "https://rdap.verisign.com/com/v1/domain/recursive.com", "checkedAt": "2026-10-25T14:00:31.039423608Z" }, "updatedAt": "2026-10-30T15:39:19.905908697Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral AI", "b": "Recursive", "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": "2026-10-21", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Recursive Flint scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 4 scored categories. Mistral Large 4 leads on risk and agent interface.", "question": "Which is better for AI agents, Mistral Large 4 or Recursive Flint?" } ], "goodFor": [ { "aheadOn": [ "Risk, 75 against 60", "Agent interface, 95 against 90" ], "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, 75 against 50" ], "also": null, "goodFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.", "slug": "recursive-flint", "watchFor": "No independent benchmark results yet, every published score is Recursive's" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.json", "title": "Agents-A1-4B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.json", "title": "Agents-A1-4B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4.json", "title": "Apertus v1.5 8B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.json", "title": "Apertus v1.5 8B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.json", "title": "Apodex-1.1-mini vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.json", "title": "Apodex-1.1-mini vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.json", "title": "BigBang-v1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.json", "title": "BigBang-v1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.json", "title": "Carbon-X vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.json", "title": "Carbon-X vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4.json", "title": "Cosmos-Reason2-2B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.json", "title": "Cosmos-Reason2-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4.json", "title": "Darwin-36B-Opus vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint.json", "title": "Darwin-36B-Opus vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4.json", "title": "DeepSeek-V3.2 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint.json", "title": "DeepSeek-V3.2 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4.json", "title": "DeepSeek V4.1 Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint.json", "title": "DeepSeek V4.1 Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4.json", "title": "DeepSeek-V4-Flash-0731 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint.json", "title": "DeepSeek-V4-Flash-0731 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4.json", "title": "DiffusionGemma 26B A4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.json", "title": "DiffusionGemma 26B A4B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mistral-large-4.json", "title": "Gemma 4 12B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-recursive-flint.json", "title": "Gemma 4 12B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mistral-large-4.json", "title": "Gemma 4 26B A4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-recursive-flint.json", "title": "Gemma 4 26B A4B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4.json", "title": "Gemma 4 31B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint.json", "title": "Gemma 4 31B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4.json", "title": "Gemma 4 E2B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint.json", "title": "Gemma 4 E2B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.json", "title": "Gemma 4 E4B IT vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-recursive-flint.json", "title": "Gemma 4 E4B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4.json", "title": "GLM-4.5-Air vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-recursive-flint.json", "title": "GLM-4.5-Air vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4.json", "title": "GLM-4.7-Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.json", "title": "GLM-4.7-Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4.json", "title": "GLM-5.3-Flash vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.json", "title": "GLM-5.3-Flash vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4.json", "title": "GLM-5.3 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint.json", "title": "GLM-5.3 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4.json", "title": "gpt-oss-120b vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint.json", "title": "gpt-oss-120b vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4.json", "title": "gpt-oss-20b vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint.json", "title": "gpt-oss-20b vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.json", "title": "Granite 4.2 3B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint.json", "title": "Granite 4.2 3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4.json", "title": "Hy3 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-recursive-flint.json", "title": "Hy3 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/hy3-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4.json", "title": "Inkling-Small vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-recursive-flint.json", "title": "Inkling-Small vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-recursive-flint" }, { "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-recursive-flint.json", "title": "Inkling vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/inkling-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-recursive-flint.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4.json", "title": "Kimi K3 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-recursive-flint.json", "title": "Kimi K3 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-recursive-flint" }, { "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-recursive-flint.json", "title": "Kolibri-1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.json", "title": "Kronos vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-recursive-flint.json", "title": "Kronos vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/kronos-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mistral-large-4.json", "title": "Laguna M.1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint.json", "title": "Laguna M.1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mistral-large-4.json", "title": "Laguna S 2.1 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.json", "title": "Laguna S 2.1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint" }, { "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-recursive-flint.json", "title": "Laguna XS 2.1 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-recursive-flint" }, { "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-recursive-flint.json", "title": "LBS-27B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4.json", "title": "LFM2.5-2.6B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.json", "title": "LFM2.5-2.6B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint" }, { "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-recursive-flint.json", "title": "Ling-3.0-tiny vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-recursive-flint.json", "title": "MiMo-V2.6 Distill Qwen 9B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4.json", "title": "MiniCPM-o 4.5 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-recursive-flint.json", "title": "MiniCPM-o 4.5 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4.json", "title": "MiniCPM5-2B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-recursive-flint.json", "title": "MiniCPM5-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4.json", "title": "MiniMax-M2.5 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-recursive-flint.json", "title": "MiniMax-M2.5 vs Recursive Flint", "url": 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"https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-2B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-32B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-4B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint.json", "title": "Qwen3-VL-8B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint.json", "title": "Qwopus3.8-27B-Flash-V2 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.json", "title": "Recursive Flint vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json", "title": "Recursive Flint vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b.json", "title": "Recursive Flint vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b.json", "title": "Recursive Flint vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2.json", "title": "Recursive Flint vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "recursive-flint", "key": "reach", "mistral-large-4": 50, "name": "Reach", "recursive-flint": 75, "weight": 25 }, { "by": 15, "edge": "mistral-large-4", "key": "risk", "mistral-large-4": 75, "name": "Risk", "recursive-flint": 60, "weight": 20 }, { "by": 5, "edge": "mistral-large-4", "key": "interface", "mistral-large-4": 95, "name": "Agent interface", "recursive-flint": 90, "weight": 10 }, { "by": 3, "edge": "recursive-flint", "key": "upkeep", "mistral-large-4": 64, "name": "Documentation and upkeep", "recursive-flint": 67, "weight": 10 } ], "summary": "Recursive Flint scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 4 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "mistral-large-4": "A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.", "recursive-flint": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint", "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.min.md" }, "markdown": "Recursive Flint scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 4 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json\n- Recursive Flint: grade C, 61/100, rank #51 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Mistral Large 4 (C)\n\nGood for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.\n\nAhead on:\n- Risk, 75 against 60\n- Agent interface, 95 against 90\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### Recursive Flint (C)\n\nGood for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.\n\nAhead on:\n- Reach, 75 against 50\n\nWatch for: No independent benchmark results yet, every published score is Recursive's\n\n\n## Score by category\n\n| Category | Weight | Mistral Large 4 | Recursive Flint | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 75 | Recursive Flint +25 |\n| Risk | 20% (20 this run) | 75 | 60 | Mistral Large 4 +15 |\n| Agent interface | 10% (10 this run) | 95 | 90 | Mistral Large 4 +5 |\n| Documentation and upkeep | 10% (10 this run) | 64 | 67 | Recursive Flint +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.6 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Mistral Large 4 | Recursive Flint |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | Recursive |\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 | 2026-10-21 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | none | 3.5/5 (4) |\n\n## Verdicts\n\n**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.\n\n**Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.\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### Recursive Flint\n\n1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp\n2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on\n3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out\n4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask\n5. Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call\n6. Message content must be a string, flatten content-part arrays first\n7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key\n\n## Questions\n\n### Which is better for AI agents, Mistral Large 4 or Recursive Flint?\n\nRecursive Flint scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 4 scored categories. Mistral Large 4 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-large-4\", \"b\": \"recursive-flint\"}`. From a terminal: `anchor compare mistral-large-4 recursive-flint`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json\n\n## Other comparisons with Mistral Large 4 or Recursive Flint\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 Recursive Flint](https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.md)\n- [BigBang-v1 vs Mistral Large 4](https://www.anchorterminal.com/compare/bigbang-v1-vs-mistral-large-4.md)\n- [BigBang-v1 vs Recursive Flint](https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.md)\n- [Carbon-X vs Mistral Large 4](https://www.anchorterminal.com/compare/carbon-x-vs-mistral-large-4.md)\n- [Carbon-X vs Recursive Flint](https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/glm-4-5-air-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint.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 Recursive Flint](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint.md)\n- [Hy3 vs Mistral Large 4](https://www.anchorterminal.com/compare/hy3-vs-mistral-large-4.md)\n- [Hy3 vs Recursive Flint](https://www.anchorterminal.com/compare/hy3-vs-recursive-flint.md)\n- [Inkling-Small vs Mistral Large 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