{
  "data": {
    "a": {
      "slug": "nanbeige-4-2-3b",
      "name": "Nanbeige4.2-3B",
      "vendor": "Nanbeige LLM Lab (BOSS Zhipin)",
      "vendorUrl": "https://huggingface.co/Nanbeige",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nanbeige4.2-3B is a small agent model from BOSS Zhipin's Nanbeige lab, 4.2B parameters with layers reused in a loop, with tool calling, a thinking switch and a 256K context. Apache 2.0. Released SGLang and llama.cpp run it.",
      "url": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json",
      "dns": "nanbeige-4-2-3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B",
      "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B"
        },
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B-GPTQ-Int8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0, per the card's metadata. Running it yourself costs little: 5.2 GB in FP8 or 8-bit from Nanbeige, 8.3 GB in BF16, and 2.7 GB for bartowski's Q4_K_M conversion (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "small",
        "looped-transformer",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-09-11",
      "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": 1518,
        "categoryRank": 62,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 55,
          "upkeep": 72
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.",
        "bestFor": "Local agents that need tool calling and a long context on a laptop or a small GPU",
        "strengths": [
          "Trained for multi-step tool use, with parsers in released SGLang",
          "A 256K context in a model that fits 8 GB",
          "Apache 2.0, ungated",
          "FP8 and 8-bit builds from Nanbeige",
          "Nanbeige replied in most of the recent discussions"
        ],
        "weaknesses": [
          "Its own architecture: Transformers loads it only with the repository's code",
          "No GGUF from Nanbeige, so llama.cpp users rely on a third party's conversion",
          "The card's serve instructions still point at Nanbeige's forks",
          "No safety results on the card",
          "Training data described only in general terms",
          "Not changed since September"
        ],
        "agentNotes": [
          "Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`",
          "Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`",
          "Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat",
          "Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card",
          "For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's",
          "If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 85,
          "risk": 55,
          "upkeep": 72
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige",
        "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. Pass tools in the request, and\n# chat_template_kwargs {\"enable_thinking\": false} for a direct answer"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nanbeige-4-2-3b"
      },
      "sameCompany": [
        "nanbeige-4-3-33b"
      ],
      "weights": {
        "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)",
        "builtOn": "Nanbeige4.2-3B-Base, per the card and the metadata",
        "params": "4.2B",
        "context": 262144,
        "goodFor": "A small local agent for tool use, coding and office tasks, with thinking you can turn off and a long context.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang"
        ],
        "builds": [
          {
            "name": "Nanbeige4.2-3B-FP8",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 5.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Nanbeige's FP8 build"
          },
          {
            "name": "Nanbeige4.2-3B-GPTQ-Int8",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-GPTQ-Int8",
            "who": "publisher",
            "precision": "int8",
            "sizeGB": 5.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Nanbeige's 8-bit GPTQ build"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 2.68,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. Its base_model tag names Nanbeige/Nanbeige4.2-3B, from the Hub's model information on 10 November 2026"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 4.43,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. From the Hub's model information on 10 November 2026"
          },
          {
            "name": "Nanbeige4.2-3B (BF16 safetensors)",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.34,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Two shards"
          }
        ],
        "run": "python -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige",
        "runBuild": "Nanbeige4.2-3B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Nanbeige LLM Lab (BOSS Zhipin)",
        "family": "Nanbeige4.2",
        "url": "https://www.anchorterminal.com/open-weights/#nanbeige",
        "others": [
          {
            "name": "Nanbeige4.2-3B-Base",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base"
          }
        ],
        "olderCount": 7
      },
      "area": "models",
      "provenance": {
        "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed",
        "domain": "zhipin.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The publisher's identity, domain and security facts are carried from our Nanbeige4.3-33B listing: the lab's report for this model names Nanbeige LLM Lab, BOSS Zhipin, and the card's contact address is on kanzhun.com.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 29 commits from 21 July to 11 September 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.json"
    },
    "answer": "Nanbeige4.2-3B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint 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",
      "dns": "recursive-flint.grade.anchorterminal.com",
      "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). OpenRouter lists a free endpoint from Recursive, recursive/flint-v1.0:free, which wasn't serving on 3 November 2026 (checked 2026-11-03).",
      "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": "2026-11-03"
      },
      "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": 1518,
        "categoryRank": 44,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 67
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "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. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. 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 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",
          "Released support in llama.cpp, Ollama, LM Studio, Transformers v5.21 and vLLM v0.33.0"
        ],
        "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 on the card still needs one 141 GB GPU and Recursive's SGLang plugin, and released SGLang doesn't run it",
          "No safety results and no trained instruction hierarchy",
          "Recursive answered one of 31 discussions, on release day"
        ],
        "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",
          "Check OpenRouter's endpoints list before calling recursive/flint-v1.0:free, since its only endpoint (Recursive, BF16) showed 0 per cent uptime over the last day on 3 November 2026",
          "vLLM needs v0.33.0 or later and Transformers v5.21 or later; released SGLang has no Flint model"
        ],
        "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": 85,
          "risk": 60,
          "upkeep": 67
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "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",
          "vLLM",
          "Transformers"
        ],
        "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": "2026-11-03",
        "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": "2026-11-10T16:07:19.880436818Z"
        },
        "domain": {
          "domain": "recursive.com",
          "registered": "1995-08-24",
          "source": "https://rdap.verisign.com/com/v1/domain/recursive.com",
          "checkedAt": "2026-11-08T15:12:33.104118016Z"
        },
        "updatedAt": "2026-11-10T16:07:19.880436818Z"
      }
    },
    "edges": [
      {
        "by": 5,
        "category": "risk",
        "leads": "recursive-flint",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.",
          "b": "File safety 15 of 25, safetensors and GGUF only, but the card's documented BF16 route installs the wheel shipped in the repository. Provenance 15, the files are in the RecursiveSI organisation that Recursive's own engine downloads from, with a checksum on every file. Safety evaluation 0, none on the cards or in the announcement post. Agent guidance 10, the card and serving README say to enforce permissions around tool use and to strip tool tags from untrusted tool output, but there's no trained instruction hierarchy. Licence risk 15, plain Apache 2.0. Security programme 5, a reporting address on the card and a private reporting route for the engine, with no public advisories yet."
        },
        "scores": {
          "a": 55,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "recursive-flint",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.",
          "b": "Chat template 20, a Jinja file in the repository and inside both GGUFs. Tool-calling format 25, Recursive's serving README documents how tools go in, how calls come back and the flint parser. Trained tool calling 20, several calls a turn. Structured output 15, response_format with JSON Schema is documented. Reasoning control 0, the model decides when to reason and Recursive says there's no switch or budget. Context 10, 262,144 tokens with no position scaling."
        },
        "scores": {
          "a": 85,
          "b": 90
        }
      },
      {
        "by": 5,
        "category": "upkeep",
        "leads": "nanbeige-4-2-3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.",
          "b": "Model card 17 of 20, everything but languages. Training data 10, a general description of the mix with a June 2026 cut-off. Last change 20, released 13 days ago. Responsiveness 5, Recursive answered one of 31 discussions, on release day. Version policy 5, a first release. File health 10, the files agree with each other and the card, and no broken-file report waits."
        },
        "scores": {
          "a": 72,
          "b": 67
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Nanbeige LLM Lab (BOSS Zhipin)",
        "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": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-11",
        "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": "none",
        "b": "3.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nanbeige4.2-3B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on risk and agent interface.",
        "question": "Which is better for AI agents, Nanbeige4.2-3B or Recursive Flint?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 72 against 67"
        ],
        "also": null,
        "goodFor": "Local agents that need tool calling and a long context on a laptop or a small GPU",
        "slug": "nanbeige-4-2-3b",
        "watchFor": "Its own architecture: Transformers loads it only with the repository's code"
      },
      {
        "aheadOn": [
          "Risk, 60 against 55",
          "Agent interface, 90 against 85"
        ],
        "also": null,
        "goodFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a 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"
      }
    ],
    "history": {
      "nanbeige-4-2-3b": null,
      "recursive-flint": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint"
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b"
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      {
        "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/cola2-vs-nanbeige-4-2-3b.json",
        "title": "Cola2 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-recursive-flint"
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      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint.json",
        "title": "gpt-oss-20b vs Recursive Flint",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-recursive-flint.json",
        "title": "Hy3 vs Recursive Flint",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-recursive-flint.json",
        "title": "Inkling vs Recursive Flint",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nanbeige-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-2-3b"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-recursive-flint.json",
        "title": "Kronos vs Recursive Flint",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nanbeige-4-2-3b.json",
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint"
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint"
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint"
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      {
        "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"
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      {
        "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"
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      {
        "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-ripple-65b-preview.json",
        "title": "Recursive Flint vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.json",
        "title": "Recursive Flint vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview"
      },
      {
        "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-step-3-7-flash.json",
        "title": "Recursive Flint vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.json",
        "title": "Recursive Flint vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.json",
        "title": "Recursive Flint vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b.json",
        "title": "Recursive Flint vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 0,
        "edge": "",
        "key": "reach",
        "name": "Reach",
        "nanbeige-4-2-3b": 85,
        "recursive-flint": 85,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "recursive-flint",
        "key": "risk",
        "name": "Risk",
        "nanbeige-4-2-3b": 55,
        "recursive-flint": 60,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "recursive-flint",
        "key": "interface",
        "name": "Agent interface",
        "nanbeige-4-2-3b": 85,
        "recursive-flint": 90,
        "weight": 10
      },
      {
        "by": 5,
        "edge": "nanbeige-4-2-3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nanbeige-4-2-3b": 72,
        "recursive-flint": 67,
        "weight": 10
      }
    ],
    "summary": "Nanbeige4.2-3B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on risk and agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "nanbeige-4-2-3b": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.",
      "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. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off."
    }
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  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
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    "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.min.md"
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  "markdown": "Nanbeige4.2-3B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n- Recursive Flint: grade C, 61/100, rank #54 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Nanbeige4.2-3B (C)\n\nGood for: Local agents that need tool calling and a long context on a laptop or a small GPU\n\nAhead on:\n- Documentation and upkeep, 72 against 67\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\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 sparse coder with documented tool calls and a plain licence.\n\nAhead on:\n- Risk, 60 against 55\n- Agent interface, 90 against 85\n\nWatch for: No independent benchmark results yet, every published score is Recursive's\n\n\n## Why the edge\n\n### Risk: Recursive Flint +5\n\n- Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.\n- Recursive Flint (60): File safety 15 of 25, safetensors and GGUF only, but the card's documented BF16 route installs the wheel shipped in the repository. Provenance 15, the files are in the RecursiveSI organisation that Recursive's own engine downloads from, with a checksum on every file. Safety evaluation 0, none on the cards or in the announcement post. Agent guidance 10, the card and serving README say to enforce permissions around tool use and to strip tool tags from untrusted tool output, but there's no trained instruction hierarchy. Licence risk 15, plain Apache 2.0. Security programme 5, a reporting address on the card and a private reporting route for the engine, with no public advisories yet.\n\n### Agent interface: Recursive Flint +5\n\n- Nanbeige4.2-3B (85): Chat template 20, A Jinja chat template in tokenizer_config.json (7,961 characters), with enable_thinking, preserve_thinking, tools and tool_call_format switches. Tool-calling format 25, the card says passing tools switches on the tool template and recommends tool_call_format xml, with json for compatibility, and its SGLang and vLLM commands name the nanbeige tool-call parser, which released SGLang 0.5.23 has. Trained tool calling 20, the card describes fine-tuning on tool-use environments and agent scaffolds and reinforcement learning on agent tasks, and the template tells the model it may call one or more functions and renders every call in a turn. Structured output 0, Neither the card nor the files mention JSON or JSON Schema output, beyond json as a tool-call format. Reasoning control 10, the card documents enable_thinking, on by default and False per request, and preserve_thinking for earlier turns, and names the nanbeige reasoning parser, which returns the reasoning apart from the answer. Context 10, 262,144 tokens, which the card says the model supports and config.json gives as max_position_embeddings. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- Recursive Flint (90): Chat template 20, a Jinja file in the repository and inside both GGUFs. Tool-calling format 25, Recursive's serving README documents how tools go in, how calls come back and the flint parser. Trained tool calling 20, several calls a turn. Structured output 15, response_format with JSON Schema is documented. Reasoning control 0, the model decides when to reason and Recursive says there's no switch or budget. Context 10, 262,144 tokens with no position scaling.\n\n### Documentation and upkeep: Nanbeige4.2-3B +5\n\n- Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.\n- Recursive Flint (67): Model card 17 of 20, everything but languages. Training data 10, a general description of the mix with a June 2026 cut-off. Last change 20, released 13 days ago. Responsiveness 5, Recursive answered one of 31 discussions, on release day. Version policy 5, a first release. File health 10, the files agree with each other and the card, and no broken-file report waits.\n\n## Score by category\n\n| Category | Weight | Nanbeige4.2-3B | Recursive Flint | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 85 | even |\n| Risk | 20% (20 this run) | 55 | 60 | Recursive Flint +5 |\n| Agent interface | 10% (10 this run) | 85 | 90 | Recursive Flint +5 |\n| Documentation and upkeep | 10% (10 this run) | 72 | 67 | Nanbeige4.2-3B +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Nanbeige4.2-3B | Recursive Flint |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | 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 | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-11 | 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| Agent reviews | none | 3.5/5 (4) |\n\n## How the scores have moved\n\n### Nanbeige4.2-3B\n\nNo score change since it was listed.\n\n### Recursive Flint\n\n- no change on 3 November 2026: product\n\n## Verdicts\n\n**Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.\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. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. 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### Nanbeige4.2-3B\n\n1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`\n2. Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`\n3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat\n4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card\n5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's\n6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it\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\n8. Check OpenRouter's endpoints list before calling recursive/flint-v1.0:free, since its only endpoint (Recursive, BF16) showed 0 per cent uptime over the last day on 3 November 2026\n9. vLLM needs v0.33.0 or later and Transformers v5.21 or later; released SGLang has no Flint model\n\n## Questions\n\n### Which is better for AI agents, Nanbeige4.2-3B or Recursive Flint?\n\nNanbeige4.2-3B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-recursive-flint.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nanbeige-4-2-3b\", \"b\": \"recursive-flint\"}`. From a terminal: `anchor compare nanbeige-4-2-3b recursive-flint`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json\n\n## Other comparisons with Nanbeige4.2-3B or Recursive Flint\n\n- [Agents-A1-4B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b.md)\n- [Apodex-1.1-mini vs Recursive Flint](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.md)\n- [Arete-27B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.md)\n- [Arete-27B vs Recursive Flint](https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.md)\n- [BigBang-v1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-2-3b.md)\n- [BigBang-v1 vs Recursive Flint](https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.md)\n- [Carbon-X vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b.md)\n- [Carbon-X vs Recursive Flint](https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.md)\n- [Cola2 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b.md)\n- [Cola2 vs Recursive Flint](https://www.anchorterminal.com/compare/cola2-vs-recursive-flint.md)\n- [Cosmos-Reason2-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-2-3b.md)\n- [DeepSeek-V4-Flash-0731 vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-recursive-flint.md)\n- [Devstral Small 2 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nanbeige-4-2-3b.md)\n- [Devstral Small 2 vs Recursive Flint](https://www.anchorterminal.com/compare/devstral-small-2-vs-recursive-flint.md)\n- [DiffusionGemma 26B A4B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-2-3b.md)\n- [DiffusionGemma 26B A4B IT vs Recursive Flint](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-recursive-flint.md)\n- [EXAONE 4.5 33B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nanbeige-4-2-3b.md)\n- [EXAONE 4.5 33B vs Recursive Flint](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-recursive-flint.md)\n- [Gemma 4 12B IT vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/glm-5-3-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nanbeige-4-2-3b.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 Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nanbeige-4-2-3b.md)\n- [gpt-oss-20b vs Recursive Flint](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-recursive-flint.md)\n- [Granite 4.2 30B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nanbeige-4-2-3b.md)\n- [Granite 4.2 30B vs Recursive Flint](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-recursive-flint.md)\n- [Granite 4.2 3B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nanbeige-4-2-3b.md)\n- [Granite 4.2 3B vs Recursive Flint](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-recursive-flint.md)\n- [Granite 4.2 8B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nanbeige-4-2-3b.md)\n- [Granite 4.2 8B vs Recursive Flint](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-recursive-flint.md)\n- [Hy-Memory-30B-A3B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-2-3b.md)\n- [Hy-Memory-30B-A3B vs Recursive Flint](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint.md)\n- [Hy3 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/hy3-vs-nanbeige-4-2-3b.md)\n- [Hy3 vs Recursive Flint](https://www.anchorterminal.com/compare/hy3-vs-recursive-flint.md)\n- [Inkling-Small vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-2-3b.md)\n- [Inkling-Small vs Recursive Flint](https://www.anchorterminal.com/compare/inkling-small-vs-recursive-flint.md)\n- [Inkling vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-2-3b.md)\n- [Inkling vs Recursive Flint](https://www.anchorterminal.com/compare/inkling-vs-recursive-flint.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-2-3b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Recursive Flint](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-recursive-flint.md)\n- [Kimi K3.1 Pro vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.md)\n- [Kimi K3.1 Pro vs Recursive Flint](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint.md)\n- [Kimi K3 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nanbeige-4-2-3b.md)\n- [Kimi K3 vs Recursive Flint](https://www.anchorterminal.com/compare/kimi-k3-vs-recursive-flint.md)\n- [Kolibri-1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-2-3b.md)\n- [Kolibri-1 vs Recursive Flint](https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint.md)\n- [Kronos vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.md)\n- [Kronos vs Recursive Flint](https://www.anchorterminal.com/compare/kronos-vs-recursive-flint.md)\n- [Laguna M.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nanbeige-4-2-3b.md)\n- [Laguna M.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint.md)\n- [Laguna S 2.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nanbeige-4-2-3b.md)\n- [Laguna S 2.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.md)\n- [Laguna XS 2.1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nanbeige-4-2-3b.md)\n- [Laguna XS 2.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-recursive-flint.md)\n- [LBS-27B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-2-3b.md)\n- [LBS-27B vs Recursive Flint](https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.md)\n- [LFM2.5-2.6B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nanbeige-4-2-3b.md)\n- [LFM2.5-2.6B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.md)\n- [LFM3-1.7B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nanbeige-4-2-3b.md)\n- [LFM3-1.7B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-recursive-flint.md)\n- [LFM3-Audio-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nanbeige-4-2-3b.md)\n- [LFM3-Audio-2B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-recursive-flint.md)\n- [LFM3-Omni-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nanbeige-4-2-3b.md)\n- [LFM3-Omni-2B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-recursive-flint.md)\n- [LFM3-VL-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nanbeige-4-2-3b.md)\n- [LFM3-VL-2B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint.md)\n- [Ling-3.0-tiny vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-nanbeige-4-2-3b.md)\n- [Ling-3.0-tiny vs Recursive Flint](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.md)\n- [Ling-3.1-flash-1019 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nanbeige-4-2-3b.md)\n- [Ling-3.1-flash-1019 vs Recursive Flint](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-recursive-flint.md)\n- [LongCat-2.5 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-2-3b.md)\n- [LongCat-2.5 vs Recursive Flint](https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nanbeige-4-2-3b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Recursive Flint](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-recursive-flint.md)\n- [MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-2-3b.md)\n- [MiMo-V2.6-Flash-RL vs Recursive Flint](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint.md)\n- [MiniCPM-o 4.5 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nanbeige-4-2-3b.md)\n- [MiniCPM-o 4.5 vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-recursive-flint.md)\n- [MiniCPM-V 4.7 1B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nanbeige-4-2-3b.md)\n- [MiniCPM-V 4.7 1B vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.md)\n- [MiniCPM-V 4.7 35B-A3B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nanbeige-4-2-3b.md)\n- [MiniCPM-V 4.7 35B-A3B vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-recursive-flint.md)\n- [MiniCPM5-2B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-nanbeige-4-2-3b.md)\n- [MiniCPM5-2B vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm5-2b-vs-recursive-flint.md)\n- [MiniMax-M2.5 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-nanbeige-4-2-3b.md)\n- [MiniMax-M2.5 vs Recursive Flint](https://www.anchorterminal.com/compare/minimax-m2-5-vs-recursive-flint.md)\n- [MiniMax-M2.7 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-2-3b.md)\n- [MiniMax-M2.7 vs Recursive Flint](https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint.md)\n- [MiniMax-M3.1-Flash vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-2-3b.md)\n- [MiniMax-M3.1-Flash vs Recursive Flint](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-recursive-flint.md)\n- [Mistral Large 4 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-nanbeige-4-2-3b.md)\n- [Mistral Large 4 vs Recursive Flint](https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.md)\n- [Muse Glimmer 30B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nanbeige-4-2-3b.md)\n- [Muse Glimmer 30B vs Recursive Flint](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-recursive-flint.md)\n- [Nanbeige4.2-3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nanbeige-4-3-33b.md)\n- [Nanbeige4.2-3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Nanbeige4.2-3B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Nanbeige4.2-3B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-30b-a3b.md)\n- [Nanbeige4.2-3B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-4b.md)\n- [Nanbeige4.2-3B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Nanbeige4.2-3B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-super-120b-a12b.md)\n- [Nanbeige4.2-3B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Nanbeige4.2-3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-north-mini-code-1-0.md)\n- [Nanbeige4.2-3B vs occamy-1.0](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-occamy-1-0.md)\n- [Nanbeige4.2-3B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-olmo-3-7b-think.md)\n- [Nanbeige4.2-3B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-122b.md)\n- [Nanbeige4.2-3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-35b-a3b.md)\n- [Nanbeige4.2-3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b.md)\n- [Nanbeige4.2-3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-9b.md)\n- [Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-otel-2-0-llm-31b-it.md)\n- [Nanbeige4.2-3B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-qwen-agentworld-35b-a3b.md)\n- [Nanbeige4.2-3B vs 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