{
  "data": {
    "a": {
      "slug": "minimax-m3-1-flash",
      "name": "MiniMax-M3.1-Flash",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M3.1-Flash is MiniMax's open-weight mixture-of-experts model for coding, agent work, computer use and cybersecurity, about 428B parameters with 23B active, taking text, images and video with up to 1M tokens of context.",
      "url": "https://www.anchorterminal.com/tools/minimax-m3-1-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash",
      "license": "MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M3.1-Flash"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for MiniMaxAI/MiniMax-M3.1-Flash (checked 2026-10-24).",
      "pricing": "free",
      "pricingNotes": "Free to download. The MiniMax Community License allows commercial use if the product shows \"Built with MiniMax M3.1-Flash\" and MiniMax gets a one-time notice by email; products earning more than $20M a year need MiniMax's prior written authorisation. Running it yourself costs the hardware, 250.1 GB of NVFP4 and MXFP8 weights plus a 2.3 GB DSpark draft. MiniMax's API sells the same-named model at $0.15 in and $0.60 out per million tokens (checked 2026-10-24).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-24).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-24"
      },
      "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "video",
        "reasoning",
        "tool-calling",
        "coding",
        "nvfp4",
        "mxfp8",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1210,
        "categoryRank": 77,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 64,
          "interface": 70,
          "reach": 40,
          "risk": 35,
          "upkeep": 41
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-24"
        },
        "negative": 0,
        "verdict": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.",
        "bestFor": "Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.",
        "strengths": [
          "Quantisation-aware NVFP4 and MXFP8 release, about 250 GB for 428B parameters, with a DSpark draft included",
          "1,048,576 tokens of context in the config, with sparse attention in every layer",
          "Several tool calls in one block, and reasoning_effort from low to max per request",
          "Text, image and video input in one model",
          "No gate or account, files in MiniMax's own organisation with a SHA-256 for every shard"
        ],
        "weaknesses": [
          "No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did",
          "About 300 GB with a fifth added, more than any 128 GB or 256 GB machine",
          "Commercial use needs a Built with line and a notice to MiniMax, and authorisation above $20M yearly revenue",
          "No safety evaluation, agent guidance or security contact, alongside strong claimed offensive security capability",
          "The card omits limitations, languages, training data and a knowledge cut-off, and no discussion has a MiniMax reply"
        ],
        "agentNotes": [
          "Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue",
          "On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144",
          "Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code",
          "--block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128",
          "Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95",
          "On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off",
          "For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage",
          "Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "capability": 64,
          "interface": 70,
          "reach": 40,
          "risk": 35,
          "upkeep": 41
        },
        "provenanceScore": 55,
        "checked": {
          "categories": {
            "capability": "2026-10-24",
            "interface": "2026-10-24",
            "reach": "2026-10-24",
            "risk": "2026-10-24",
            "upkeep": "2026-10-24"
          },
          "last": "2026-10-24",
          "oldest": "2026-10-24",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM's recipe, 8 x H100 or H200 (nightly image; no pip release supports it on 24 October 2026)\ndocker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface \\\n  vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash \\\n  --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager \\\n  --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin \\\n  --max-model-len 262144 --reasoning-parser minimax_m3 \\\n  --enable-auto-tool-choice --tool-call-parser minimax_m3",
        "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M3.1-Flash\", \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"reasoning_effort\": \"medium\"}, \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m3-1-flash"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-5",
        "minimax-m2-7"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "builtOn": "MiniMaxAI/MiniMax-M3 (the card says M3.1-Flash builds on M3 and keeps its mixture-of-experts architecture; not declared as base_model in the metadata; M3 carries the same MiniMax Community License)",
        "params": "428B",
        "context": 1048576,
        "goodFor": "Mixture-of-experts model for coding, agent harnesses, computer use and security work, with images and video in, about 1M tokens of context and parallel tool calls, on a 4 to 8 GPU server.",
        "runtimes": [
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "MiniMax-M3.1-Flash",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 250.12,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Quantisation-aware trained release, routed experts in NVFP4 (group 16) and dense and attention layers in MXFP8, 250,116,419,880 bytes in 48 shards summed from the Hub's file list, plus a 2.30 GB DSpark draft. The only build MiniMax publishes. vLLM and SGLang ran it only from pre-release images on 24 October 2026"
          }
        ],
        "run": "docker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin --max-model-len 262144 --reasoning-parser minimax_m3 --enable-auto-tool-choice --tool-call-parser minimax_m3",
        "runBuild": "MiniMax-M3.1-Flash",
        "checked": "2026-10-24",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M3",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M3",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3",
            "params": "~428B total, ~23B activated"
          }
        ],
        "olderCount": 7
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax (named in the licence's copyright line; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-24",
        "notes": [
          "The licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api and minimax-m2-7 listings.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The repository's history is one commit, \"Init\", on 23 October 2026.",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 24 October 2026, and MiniMax has no public GitHub repository for M3.1 Flash, so there is no security.txt or SECURITY.md.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links MiniMax's site, API and GitHub organisation, so the repository is the publisher's own."
        ],
        "score": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.json",
      "live": {
        "slug": "minimax-m3-1-flash",
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-25T15:28:08.450653621Z"
        },
        "updatedAt": "2026-10-25T15:28:08.450653621Z"
      }
    },
    "answer": "Recursive Flint scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category.",
    "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": 1210,
        "categoryRank": 42,
        "methodology": "0.6",
        "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": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 90,
          "reach": 75,
          "risk": 60,
          "upkeep": 67
        },
        "provenanceScore": 40,
        "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": 40
      },
      "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": "2026-10-25T15:27:43.508462289Z"
        },
        "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": "2026-10-25T15:27:43.508462289Z"
      }
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "MiniMax",
        "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": "MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-23",
        "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": "2.3/5 (4)",
        "b": "3.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Recursive Flint scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, MiniMax-M3.1-Flash or Recursive Flint?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.",
        "slug": "minimax-m3-1-flash",
        "watchFor": "No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did"
      },
      {
        "aheadOn": [
          "Reach, 75 against 40",
          "Risk, 60 against 35",
          "Agent interface, 90 against 70",
          "Documentation and upkeep, 67 against 41"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.5 against 2.3 out of 5"
        ],
        "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-minimax-m3-1-flash.json",
        "title": "Agents-A1-4B vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Apertus v1.5 8B vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Apodex-1.1-mini vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "BigBang-v1 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Carbon-X vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Cosmos-Reason2-2B vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Darwin-36B-Opus vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "DeepSeek-V3.2 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "DeepSeek V4.1 Flash vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "DeepSeek-V4-Flash-0731 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "DiffusionGemma 26B A4B IT vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Gemma 4 12B IT vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Gemma 4 26B A4B IT vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Gemma 4 31B IT vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Gemma 4 E2B IT vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Gemma 4 E4B IT vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "GLM-4.5-Air vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "GLM-4.7-Flash vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "GLM-5.3-Flash vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "GLM-5.3 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "gpt-oss-120b vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "gpt-oss-20b vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Granite 4.2 3B vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Hy3 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Inkling-Small vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "Inkling vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m3-1-flash"
      },
      {
        "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-minimax-m3-1-flash.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-minimax-m3-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-recursive-flint.json",
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        "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-minimax-m3-1-flash.json",
        "title": "Kimi K3 vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m3-1-flash"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.json",
        "title": "Nemotron 3 Super 120B A12B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-recursive-flint.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint.json",
        "title": "occamy-1.0 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint.json",
        "title": "Olmo 3 7B Think vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint.json",
        "title": "Ornith-1.5-35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint.json",
        "title": "Ornith-1.5-397B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint.json",
        "title": "Ornith-1.5-9B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-recursive-flint.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-recursive-flint.json",
        "title": "Qwen3-4B-Instruct-2507 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint.json",
        "title": "Qwen3.5-0.8B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint.json",
        "title": "Qwen3.5-122B-A10B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-recursive-flint.json",
        "title": "Qwen3.5-2B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint.json",
        "title": "Qwen3.5-397B-A17B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-recursive-flint.json",
        "title": "Qwen3.5-4B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint.json",
        "title": "Qwen3.5-9B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint.json",
        "title": "Qwen3.6-27B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint.json",
        "title": "Qwen3.6-35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint.json",
        "title": "Qwen3.8-27B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint.json",
        "title": "Qwen3.8-Flash-Next vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint.json",
        "title": "Qwen3-Coder-Next vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint",
        "url": "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"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "recursive-flint",
        "key": "reach",
        "minimax-m3-1-flash": 40,
        "name": "Reach",
        "recursive-flint": 75,
        "weight": 25
      },
      {
        "by": 25,
        "edge": "recursive-flint",
        "key": "risk",
        "minimax-m3-1-flash": 35,
        "name": "Risk",
        "recursive-flint": 60,
        "weight": 20
      },
      {
        "by": 20,
        "edge": "recursive-flint",
        "key": "interface",
        "minimax-m3-1-flash": 70,
        "name": "Agent interface",
        "recursive-flint": 90,
        "weight": 10
      },
      {
        "by": 26,
        "edge": "recursive-flint",
        "key": "upkeep",
        "minimax-m3-1-flash": 41,
        "name": "Documentation and upkeep",
        "recursive-flint": 67,
        "weight": 10
      }
    ],
    "summary": "Recursive Flint scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "minimax-m3-1-flash": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.",
      "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/minimax-m3-1-flash-vs-recursive-flint",
    "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint.min.md"
  },
  "markdown": "Recursive Flint scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- MiniMax-M3.1-Flash: grade D, 50.5/100, rank #99 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m3-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json\n- Recursive Flint: grade C, 61/100, rank #50 of 110 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 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiniMax-M3.1-Flash (D)\n\nGood for: Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.\n\nWatch for: No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did\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 40\n- Risk, 60 against 35\n- Agent interface, 90 against 70\n- Documentation and upkeep, 67 against 41\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.3 out of 5\n\nWatch for: No independent benchmark results yet, every published score is Recursive's\n\n\n## Score by category\n\n| Category | Weight | MiniMax-M3.1-Flash | Recursive Flint | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 75 | Recursive Flint +35 |\n| Risk | 20% (20 this run) | 35 | 60 | Recursive Flint +25 |\n| Agent interface | 10% (10 this run) | 70 | 90 | Recursive Flint +20 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 67 | Recursive Flint +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M3.1-Flash | Recursive Flint |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | 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 | MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-23 | 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 | 2.3/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**MiniMax-M3.1-Flash.** A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.\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### MiniMax-M3.1-Flash\n\n1. Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue\n2. On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144\n3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code\n4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128\n5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95\n6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off\n7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage\n8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort\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, MiniMax-M3.1-Flash or Recursive Flint?\n\nRecursive Flint scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m3-1-flash\", \"b\": \"recursive-flint\"}`. From a terminal: `anchor compare minimax-m3-1-flash recursive-flint`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json\n\n## Other comparisons with MiniMax-M3.1-Flash or Recursive Flint\n\n- [Agents-A1-4B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m3-1-flash.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 MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m3-1-flash.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 MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m3-1-flash.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 MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m3-1-flash.md)\n- [BigBang-v1 vs Recursive Flint](https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.md)\n- [Carbon-X vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/carbon-x-vs-minimax-m3-1-flash.md)\n- [Carbon-X vs Recursive Flint](https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.md)\n- [Cosmos-Reason2-2B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m3-1-flash.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 MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m3-1-flash.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 MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m3-1-flash.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 MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m3-1-flash.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 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 Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint.md)\n- [DiffusionGemma 26B A4B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m3-1-flash.md)\n- 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