{
  "data": {
    "a": {
      "slug": "apodex-1-1-mini",
      "name": "Apodex-1.1-mini",
      "vendor": "Apodex AI",
      "vendorUrl": "https://www.apodex.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Apodex-1.1-mini is Apodex AI's open-weight mixture-of-experts model for long-horizon agent work with files, data, code and tools, a fine-tune of Qwen3.5-35B-A3B with about 36B parameters, about 3B active per token and a 262,144-token context, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/apodex-1-1-mini",
      "markdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json",
      "dns": "apodex-1-1-mini.grade.anchorterminal.com",
      "repo": "https://huggingface.co/apodex/Apodex-1.1-mini",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-FP8"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-GPTQ-Int4"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of Apodex's build repositories are not gated (checked 2026-10-21). Apodex's hosted API needs an account and key.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the repository's LICENSE file). Running it yourself costs the hardware, about 72 GB for the BF16 files or 22.6 GB for Apodex's Q4_K_M GGUF with its vision projector. Apodex's own API serves it as apodex-1.1-mini at $0.10 per million input tokens, $0.01 cached and $1.00 output, doubled for requests over 200K input tokens (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol. Apodex's hosted API bills by account (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/apodex/Apodex-1.1-mini",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-08-25",
      "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": 31,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 65,
          "upkeep": 38
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.",
        "bestFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence",
        "strengths": [
          "The card documents the tool-call format, the parsers for SGLang and vLLM and a full agent loop, with parallel calls",
          "Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF builds, the 4-bit ones in 32 GB",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 MoE",
          "Apache 2.0 through the chain, with the base's licence file carried and no gate",
          "Apodex's own API serves the same model",
          "Safetensors and GGUF only, with checksums on every weight file"
        ],
        "weaknesses": [
          "No safety results, and agent guidance only from the harness",
          "Apodex answered none of the four discussions",
          "The card states no parameters, architecture, limitations or knowledge cut-off",
          "The template adds an Apodex identity block when no system prompt is sent and keeps earlier reasoning by default",
          "Structured output isn't documented"
        ],
        "agentNotes": [
          "Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed",
          "For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file",
          "Pass tools through the API's tools parameter rather than in the system prompt, as the card asks",
          "The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768",
          "Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block",
          "The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions",
          "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "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": 65,
          "upkeep": 38
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek -c 32768\n# or, with the card's vLLM command:\nvllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice \\\n  --tool-call-parser qwen3_coder --reasoning-parser qwen3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"apodex/Apodex-1.1-mini\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/apodex-1-1-mini"
      },
      "weights": {
        "trainedBy": "Apodex AI",
        "builtOn": "Qwen/Qwen3.5-35B-A3B (Apache 2.0), a fine-tune, as declared in the metadata (base_model, relation finetune). Same config and the same parameter count, 35,951,822,704",
        "params": "35B (3B active)",
        "active": "3B",
        "context": 262144,
        "goodFor": "Mixture-of-experts agent model for long-horizon work with files, data, code and tools, fine-tuned from Qwen3.5-35B-A3B, with documented tool calling, parallel calls and thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Apodex-1.1-mini-NVFP4",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 24.3,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 (modelopt_mixed) needs NVIDIA Blackwell; the card serves it with SGLang"
          },
          {
            "name": "Apodex-1.1-mini-GPTQ-Int4",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GPTQ-Int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 24.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "SGLang"
            ],
            "note": "The card serves it with SGLang"
          },
          {
            "name": "apodex1.1-35b-Q4_K_M-MTP",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22.6,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "21.7 GB of weights with the multi-token prediction layer and a 0.9 GB F16 vision projector; the card passes the repository's chat_template.jinja to llama-server"
          },
          {
            "name": "Apodex-1.1-mini-FP8",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 37.5,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Apodex-1.1-mini",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3",
        "runBuild": "Apodex-1.1-mini",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Apodex AI (apodex)",
        "family": "Apodex-1",
        "url": "https://www.anchorterminal.com/open-weights/#apodex",
        "others": [
          {
            "name": "Apodex-1.0-mini",
            "url": "https://huggingface.co/apodex/Apodex-1.0-mini",
            "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)"
          },
          {
            "name": "Apodex-1.0-4B-SFT",
            "url": "https://huggingface.co/apodex/Apodex-1.0-4B-SFT",
            "params": "4B (4,659,865,088 by the Hub's safetensors count)"
          },
          {
            "name": "Apodex-1.0-2B-SFT",
            "url": "https://huggingface.co/apodex/Apodex-1.0-2B-SFT",
            "params": "2B (2,274,069,824 by the Hub's safetensors count)"
          },
          {
            "name": "Apodex-1.0-0.8B-SFT",
            "url": "https://huggingface.co/apodex/Apodex-1.0-0.8B-SFT",
            "params": "0.8B (873,438,784 by the Hub's safetensors count)"
          }
        ],
        "unrestrictedCopies": 2
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Apodex (the site's copyright line reads 2026 Apodex)",
        "domain": "apodex.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "apodex.com links to the apodex organisation on Hugging Face, and the card links back to apodex.com, apodex.ai, platform.apodex.ai and github.com/ApodexAI/FrontierAgent. Every commit is by one account, Eric-LRL-130, an apodex member.",
          "Nothing is graded as hosted, so the licence stands in for terms of service and no privacy policy is scored. Apodex's API has its own terms, not read here.",
          "No changelog for the weights. The FrontierAgent harness keeps a CHANGELOG.",
          "apodex.com and apodex.ai return 404 for /.well-known/security.txt. FrontierAgent's SECURITY.md gives a reporting address and timeline for the harness.",
          "unchecked: the domain's registration date."
        ],
        "score": 47
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.json",
      "live": {
        "slug": "apodex-1-1-mini",
        "securityTxt": {
          "url": "https://apodex.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:39.653671365Z"
        },
        "domain": {
          "domain": "apodex.com",
          "registered": "2018-10-10",
          "source": "https://rdap.verisign.com/com/v1/domain/apodex.com",
          "checkedAt": "2026-11-08T14:48:24.412328563Z"
        },
        "updatedAt": "2026-11-10T16:09:39.653671365Z"
      }
    },
    "answer": "Apodex-1.1-mini and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on reach and documentation and upkeep.",
    "b": {
      "slug": "devstral-small-2",
      "name": "Devstral Small 2",
      "vendor": "Mistral AI",
      "vendorUrl": "https://mistral.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Devstral Small 2 is Mistral's open-weight coding agent model, 24B parameters with image input, tool calling and a 256K context. Apache 2.0. It runs in vLLM, SGLang, Transformers, llama.cpp and Ollama, where the 4-bit build is 15 GB.",
      "url": "https://www.anchorterminal.com/tools/devstral-small-2",
      "markdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json",
      "dns": "devstral-small-2.grade.anchorterminal.com",
      "repo": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "mistralai/Devstral-Small-2-24B-Instruct-2512"
        },
        {
          "registry": "ollama",
          "name": "devstral-small-2"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 15 GB at Q4_K_M from Ollama's library, 26 GB in FP8 from Mistral, or 26 GB at Q8_0 from ggml-org. Mistral's API serves it too (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/mistralai/Devstral-Small-2-24B-Instruct-2512",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "coding",
        "agentic",
        "vision",
        "tool-calling",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "ollama",
        "self-hosted"
      ],
      "lastRelease": "2026-07-15",
      "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": 50,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 90,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.",
        "bestFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac",
        "strengths": [
          "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama",
          "15 GB at 4 bits from Ollama's library",
          "Apache 2.0, ungated, and on Mistral's API",
          "Tool calling documented, with test prompts and expected output",
          "A 256K context with image input"
        ],
        "weaknesses": [
          "No limitations, safety results, languages or data on the card",
          "No reasoning mode",
          "Tool calls one at a time, by the card's own tests",
          "Released in December 2025, and newer coding models have passed it",
          "Mistral ships FP8 only. The 4-bit builds are other people's"
        ],
        "agentNotes": [
          "`ollama run devstral-small-2` is the quickest start, at 15 GB",
          "On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`",
          "Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it",
          "Use a low temperature. The repository's default is 0.15",
          "Plan for one tool call per turn",
          "The same model is on our Mistral API listing if you'd rather not host 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": 65,
          "reach": 90,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 84,
        "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": "ollama run devstral-small-2\n# or: vllm serve mistralai/Devstral-Small-2-24B-Instruct-2512 --max-model-len 262144 --tool-call-parser mistral --enable-auto-tool-choice",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1, and Ollama at http://localhost:11434/v1.\n# Send the system prompt from CHAT_SYSTEM_PROMPT.txt and tools in the OpenAI function format"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/devstral-small-2"
      },
      "sameCompany": [
        "mistral-embeddings",
        "mistral-moderation",
        "mistral-voxtral-transcribe",
        "voxtral-mini-4b-realtime-2602",
        "mistral-ocr",
        "mistral-vibe-cli",
        "mistral-large-4"
      ],
      "sameModel": [
        "mistral-api"
      ],
      "weights": {
        "trainedBy": "Mistral AI",
        "builtOn": "Mistral-Small-3.1-24B-Base-2503, per the metadata",
        "params": "24B",
        "context": 262144,
        "goodFor": "A coding agent on one 24 GB GPU or a 32 GB Mac: exploring a repository, editing files and calling tools, with screenshots and diagrams as input.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ollama devstral-small-2 (Q4_K_M)",
            "url": "https://ollama.com/library/devstral-small-2",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 15,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "From Ollama's own library, 15 GB as its tags page lists it"
          },
          {
            "name": "Devstral-Small-2-24B-Instruct-2512 (FP8 safetensors)",
            "url": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 25.79,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "Mistral's release precision. The repository holds it in two layouts"
          },
          {
            "name": "ggml-org Devstral-Small-2-24B-Instruct-2512-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/Devstral-Small-2-24B-Instruct-2512-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 25.94,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 25.06 GB of Q8_0 weights plus the 0.88 GB F16 projector, from the Hub's model information on 10 November 2026"
          },
          {
            "name": "Ollama devstral-small-2 (Q8_0)",
            "url": "https://ollama.com/library/devstral-small-2",
            "who": "ollama",
            "precision": "q8_0",
            "sizeGB": 26,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "From Ollama's own library, 26 GB as its tags page lists it"
          }
        ],
        "run": "ollama run devstral-small-2",
        "runBuild": "Ollama devstral-small-2 (Q4_K_M)",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Mistral AI",
        "family": "Devstral",
        "url": "https://www.anchorterminal.com/open-weights/#mistral",
        "olderCount": 49
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Mistral AI (RCS Paris 952 418 325)",
        "domain": "mistral.ai",
        "domainRegistered": "2019-05-15",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512/commits/main",
        "securityTxt": "valid",
        "checked": "2026-11-10",
        "notes": [
          "The files sit in Mistral's verified mistralai organisation. Legal entity, domain and security.txt are carried from our Mistral Large 4 listing.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The Mistral API that serves this model has its own listing.",
          "The changelog is the commit history, 25 commits from 9 December 2025 to 15 July 2026."
        ],
        "score": 84
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/devstral-small-2.json"
    },
    "edges": [
      {
        "by": 20,
        "category": "interface",
        "leads": "apodex-1-1-mini",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.",
          "b": "Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65."
        },
        "scores": {
          "a": 85,
          "b": 65
        }
      },
      {
        "by": 8,
        "category": "upkeep",
        "leads": "devstral-small-2",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.",
          "b": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46."
        },
        "scores": {
          "a": 38,
          "b": 46
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "devstral-small-2",
        "name": "Reach",
        "reasons": {
          "a": "Memory 20, Apodex's Q4_K_M GGUF with its vision projector is 22.6 GB and needs about 27.1 GB with the fifth added, so 32 GB, as do its NVFP4 and GPTQ-Int4 builds. Runtimes 20, the Qwen3.5 MoE architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 5, Apodex's own API serves it.",
          "b": "Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90."
        },
        "scores": {
          "a": 85,
          "b": 90
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "apodex-1-1-mini",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.",
          "b": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60."
        },
        "scores": {
          "a": 65,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Apodex AI",
        "b": "Mistral AI",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-25",
        "b": "2026-07-15",
        "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": "3.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Apodex-1.1-mini and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on reach and documentation and upkeep.",
        "question": "Which is better for AI agents, Apodex-1.1-mini or Devstral Small 2?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 60",
          "Agent interface, 85 against 65"
        ],
        "also": null,
        "goodFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence",
        "slug": "apodex-1-1-mini",
        "watchFor": "No safety results, and agent guidance only from the harness"
      },
      {
        "aheadOn": [
          "Reach, 90 against 85",
          "Documentation and upkeep, 46 against 38"
        ],
        "also": null,
        "goodFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac",
        "slug": "devstral-small-2",
        "watchFor": "No limitations, safety results, languages or data on the card"
      }
    ],
    "history": null,
    "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-devstral-small-2"
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "apodex-1-1-mini": 85,
        "by": 5,
        "devstral-small-2": 90,
        "edge": "devstral-small-2",
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "apodex-1-1-mini": 65,
        "by": 5,
        "devstral-small-2": 60,
        "edge": "apodex-1-1-mini",
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "apodex-1-1-mini": 85,
        "by": 20,
        "devstral-small-2": 65,
        "edge": "apodex-1-1-mini",
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "apodex-1-1-mini": 38,
        "by": 8,
        "devstral-small-2": 46,
        "edge": "devstral-small-2",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Apodex-1.1-mini and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "apodex-1-1-mini": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.",
      "devstral-small-2": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data."
    }
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    "html": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2",
    "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2.json",
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  "markdown": "Apodex-1.1-mini and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Apodex-1.1-mini: grade C, 61/100, rank #41 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/apodex-1-1-mini.md · JSON https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json\n- Devstral Small 2: grade C, 61/100, rank #60 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/devstral-small-2.md · JSON https://www.anchorterminal.com/api/v1/tools/devstral-small-2.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### Apodex-1.1-mini (C)\n\nGood for: Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence\n\nAhead on:\n- Risk, 65 against 60\n- Agent interface, 85 against 65\n\nWatch for: No safety results, and agent guidance only from the harness\n\n### Devstral Small 2 (C)\n\nGood for: Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac\n\nAhead on:\n- Reach, 90 against 85\n- Documentation and upkeep, 46 against 38\n\nWatch for: No limitations, safety results, languages or data on the card\n\n\n## Why the edge\n\n### Agent interface: Apodex-1.1-mini +20\n\n- Apodex-1.1-mini (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.\n- Devstral Small 2 (65): Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.\n\n### Documentation and upkeep: Devstral Small 2 +8\n\n- Apodex-1.1-mini (38): Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.\n- Devstral Small 2 (46): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.\n\n### Reach: Devstral Small 2 +5\n\n- Apodex-1.1-mini (85): Memory 20, Apodex's Q4_K_M GGUF with its vision projector is 22.6 GB and needs about 27.1 GB with the fifth added, so 32 GB, as do its NVFP4 and GPTQ-Int4 builds. Runtimes 20, the Qwen3.5 MoE architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 5, Apodex's own API serves it.\n- Devstral Small 2 (90): Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.\n\n### Risk: Apodex-1.1-mini +5\n\n- Apodex-1.1-mini (65): File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.\n- Devstral Small 2 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n## Score by category\n\n| Category | Weight | Apodex-1.1-mini | Devstral Small 2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 90 | Devstral Small 2 +5 |\n| Risk | 20% (20 this run) | 65 | 60 | Apodex-1.1-mini +5 |\n| Agent interface | 10% (10 this run) | 85 | 65 | Apodex-1.1-mini +20 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 46 | Devstral Small 2 +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Apodex-1.1-mini | Devstral Small 2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Apodex AI | Mistral AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-25 | 2026-07-15 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.3/5 (4) | none |\n\n## Verdicts\n\n**Apodex-1.1-mini.** An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.\n\n**Devstral Small 2.** The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.\n\n## Before you call either\n\n### Apodex-1.1-mini\n\n1. Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed\n2. For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file\n3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks\n4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768\n5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block\n6. The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n7. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running\n9. Validate JSON output yourself, since structured output isn't documented\n\n### Devstral Small 2\n\n1. `ollama run devstral-small-2` is the quickest start, at 15 GB\n2. On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`\n3. Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it\n4. Use a low temperature. The repository's default is 0.15\n5. Plan for one tool call per turn\n6. The same model is on our Mistral API listing if you'd rather not host it\n\n## Questions\n\n### Which is better for AI agents, Apodex-1.1-mini or Devstral Small 2?\n\nApodex-1.1-mini and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on reach and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apodex-1-1-mini\", \"b\": \"devstral-small-2\"}`. From a terminal: `anchor compare apodex-1-1-mini devstral-small-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json and https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json\n\n## Other comparisons with Apodex-1.1-mini or Devstral Small 2\n\n- [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs Devstral Small 2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.md)\n- [Apertus v1.5 8B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.md)\n- [Apertus v1.5 8B vs Devstral Small 2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2.md)\n- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.md)\n- [Apodex-1.1-mini vs BigBang-v1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1.md)\n- [Apodex-1.1-mini vs 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