{ "data": { "a": { "slug": "agents-a1-4b", "name": "Agents-A1-4B", "vendor": "InternScience (Shanghai AI Laboratory)", "vendorUrl": "https://internscience.github.io/Agents-A1/", "kind": "weights", "category": "open-weights", "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/agents-a1-4b", "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json", "dns": "agents-a1-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/InternScience/Agents-A1-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "InternScience/Agents-A1-4B" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-FP8" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q8_0-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-F16-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 InternScience's build repositories for the 4B are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/InternScience/Agents-A1-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "self-hosted" ], "lastRelease": "2026-07-14", "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": 29, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence", "strengths": [ "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model", "FP8 and three GGUF precisions from the publisher", "Apache 2.0 through the chain, with no gate", "Trained for tool calling, several calls a turn, and 262,144 tokens of context", "An evaluation framework for its agent benchmarks is published on GitHub" ], "weaknesses": [ "The base isn't declared, and the technical report doesn't cover the 4B", "No safety results, agent guidance or security contact", "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14", "Tool calling and structured output aren't documented beyond a parser name", "InternScience answered one of three discussions, and the history was squashed" ], "agentNotes": [ "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector", "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work", "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14", "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 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": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "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 InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/agents-a1-4b" }, "weights": { "trainedBy": "InternScience (Shanghai AI Laboratory)", "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B", "params": "4.5B", "context": 262144, "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Agents-A1-4B-Q8_0", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 5.2, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" }, { "name": "Agents-A1-4B-Q4_K_M", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 3.4, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.7 GB of weights and the 0.7 GB vision projector" }, { "name": "Agents-A1-4B", "url": "https://huggingface.co/InternScience/Agents-A1-4B", "who": "publisher", "precision": "bf16", "sizeGB": 9.1, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "Agents-A1-4B-FP8", "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 7.8, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Agents-A1-4B-F16", "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF", "who": "publisher", "precision": "f16", "sizeGB": 9.1, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" } ], "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Agents-A1-4B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "InternScience (Shanghai AI Laboratory)", "family": "Agents-A1", "url": "https://www.anchorterminal.com/open-weights/#internscience", "others": [ { "name": "Agents-A1", "url": "https://huggingface.co/InternScience/Agents-A1", "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template", "domain": "internscience.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.", "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.", "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)." ], "score": 47 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json", "live": { "slug": "agents-a1-4b", "securityTxt": { "url": "https://internscience.github.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:07:46.042106985Z" }, "domain": { "domain": "github.io", "checkedAt": "2026-11-08T15:08:12.013832199Z" }, "updatedAt": "2026-11-10T16:07:46.042106985Z" } }, "answer": "Agents-A1-4B and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on risk.", "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": 5, "category": "risk", "leads": "devstral-small-2", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only and nothing from the repository runs. Provenance 15, the files are in the InternScience organisation its GitHub repository links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 through the chain from Qwen3.5-4B with no conditions added. Security programme 0, no policy, contact or security.txt.", "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": 55, "b": 60 } }, { "by": 5, "category": "interface", "leads": "agents-a1-4b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. 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": 70, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "InternScience (Shanghai AI Laboratory)", "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": "2026-07-14", "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": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Agents-A1-4B and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on risk.", "question": "Which is better for AI agents, Agents-A1-4B or Devstral Small 2?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 70 against 65" ], "also": null, "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence", "slug": "agents-a1-4b", "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B" }, { "aheadOn": [ "Risk, 60 against 55" ], "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": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json", "title": "Agents-A1-4B vs Apertus v1.5 8B", "url": 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"https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b.json", "title": "Devstral Small 2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "agents-a1-4b": 90, "by": 0, "devstral-small-2": 90, "edge": "", "key": "reach", "name": "Reach", "weight": 25 }, { "agents-a1-4b": 55, "by": 5, "devstral-small-2": 60, "edge": "devstral-small-2", "key": "risk", "name": "Risk", "weight": 20 }, { "agents-a1-4b": 70, "by": 5, "devstral-small-2": 65, "edge": "agents-a1-4b", "key": "interface", "name": "Agent interface", "weight": 10 }, { "agents-a1-4b": 43, "by": 3, "devstral-small-2": 46, "edge": "devstral-small-2", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Agents-A1-4B and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2", "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.min.md" }, "markdown": "Agents-A1-4B and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on risk. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #39 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.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### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Agent interface, 70 against 65\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\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- Risk, 60 against 55\n\nWatch for: No limitations, safety results, languages or data on the card\n\n\n## Why the edge\n\n### Risk: Devstral Small 2 +5\n\n- Agents-A1-4B (55): File safety 25, safetensors and GGUF only and nothing from the repository runs. Provenance 15, the files are in the InternScience organisation its GitHub repository links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 through the chain from Qwen3.5-4B with no conditions added. Security programme 0, no policy, contact or security.txt.\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### Agent interface: Agents-A1-4B +5\n\n- Agents-A1-4B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. 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## Score by category\n\n| Category | Weight | Agents-A1-4B | Devstral Small 2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 55 | 60 | Devstral Small 2 +5 |\n| Agent interface | 10% (10 this run) | 70 | 65 | Agents-A1-4B +5 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 46 | Devstral Small 2 +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | Devstral Small 2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | 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 | 2026-07-14 | 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 | 3/5 (4) | none |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\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### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. 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, Agents-A1-4B or Devstral Small 2?\n\nAgents-A1-4B and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"devstral-small-2\"}`. 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