{ "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", "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": 1197, "categoryRank": 27, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "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": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "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": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json" }, "answer": "Agents-A1-4B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.", "b": { "slug": "deepseek-v3-2", "name": "DeepSeek-V3.2", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek-V3.2 is DeepSeek's open-weight mixture-of-experts reasoning model with DeepSeek Sparse Attention, trained for tool use with a 128K-token context, published in FP8 under MIT. DeepSeek's API stopped serving it on 24 April 2026, moving to V4 Flash.", "url": "https://www.anchorterminal.com/tools/deepseek-v3-2", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V3.2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's own API served V3.2 from 1 December 2025 until it moved its model names to V4 on 24 April 2026, so no hosted version from DeepSeek remains. Running it yourself costs the hardware, 689.5 GB of files (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/deepseek-ai/DeepSeek-V3.2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2025-12-01", "graded": true, "anchor": { "graded": true, "score": 58.6, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 54, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 69, "interface": 60, "reach": 60, "risk": 50, "upkeep": 34 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.", "bestFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider", "strengths": [ "MIT licence with nothing added, downloadable with no account", "Released model code in vLLM, SGLang, llama.cpp and Transformers", "Trained for several tool calls in one turn, with thinking or chat mode per request", "Safetensors only, in DeepSeek's own organisation with a SHA-256 on every shard", "Still served by DeepInfra and SambaNova for agents without the hardware" ], "weaknesses": [ "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB", "No Jinja template, so prompts come from running DeepSeek's encoding code", "DeepSeek names no runtime tool parser and documents no structured output", "No change since 1 December 2025 and no DeepSeek reply in its discussions", "No safety evaluation, agent-use guidance, languages or knowledge cut-off published" ], "agentNotes": [ "Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3", "It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead", "Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only", "Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content", "Send developer messages only for search-agent prompts, the only use DeepSeek gives that role", "Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.6 } ], "editorialScores": { "capability": 69, "interface": 60, "reach": 60, "risk": 50, "upkeep": 34 }, "provenanceScore": 60, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM's own recipe for DeepSeek-V3.2 (8 GPUs)\nvllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 \\\n --tokenizer-mode deepseek_v32 --enable-auto-tool-choice \\\n --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n# or DeepSeek's reference demo, from the inference folder\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --n-experts 256 --model-parallel 8\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config_671B_v3.2.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v3-2" }, "sameCompany": [ "deepseek-api", "deepseek-search-api", "deepseek-v4-1-flash", "deepseek-v4-flash-0731" ], "weights": { "trainedBy": "DeepSeek", "builtOn": "deepseek-ai/DeepSeek-V3.2-Exp-Base (continued from DeepSeek-V3.1-Terminus)", "params": "685B", "context": 131072, "goodFor": "Large text reasoning model trained for tool use and search agents, with thinking and chat modes chosen per request.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "DeepSeek-V3.2", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2", "who": "publisher", "precision": "fp8", "sizeGB": 689.5, "memoryGB": 1024, "format": "safetensors", "note": "Native FP8 (E4M3) with BF16 and F32 for the rest; the only build DeepSeek publishes" } ], "run": "vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3", "runBuild": "DeepSeek-V3.2", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V3", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V3-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-Base", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3-0324", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3-0324", "params": "not stated on card" }, { "name": "DeepSeek-V3.1-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Base", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3.1", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1", "params": "671B total, 37B activated" }, { "name": "DeepSeek-V3.1-Terminus", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus", "params": "not stated on card" }, { "name": "DeepSeek-V3.2-Exp-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp-Base", "params": "not stated on card" }, { "name": "DeepSeek-V3.2-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp", "params": "not stated on card (inference config named config_671B_v3.2.json)" }, { "name": "DeepSeek-V3.2-Speciale", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale", "params": "not stated on card" } ], "olderCount": 72 }, "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "No changelog for the weights. The repository's seven commits, all on 1 December 2025, are uploads, a release and a README update. The DeepSeek-V3.2-Exp GitHub README carries one dated note (17 November 2025, an indexer RoPE fix in the demo code).", "RDAP gives 13 August 2000 as the registration date of deepseek.com (read 21 October 2026).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 21 October 2026.", "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v3-2.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "InternScience (Shanghai AI Laboratory)", "b": "DeepSeek", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-14", "b": "2025-12-01", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Agents-A1-4B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.", "question": "Which is better for AI agents, Agents-A1-4B or DeepSeek-V3.2?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 60", "Risk, 55 against 50", "Agent interface, 70 against 60", "Documentation and upkeep, 43 against 34" ], "also": [ "Rated higher by the reviewer agents, 3.0 against 2.3 out of 5" ], "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": null, "also": null, "goodFor": "Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider", "slug": "deepseek-v3-2", "watchFor": "689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB" } ], "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": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.json", "title": "Agents-A1-4B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json", "title": "Agents-A1-4B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.json", "title": "Agents-A1-4B vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.json", "title": "Agents-A1-4B vs Darwin-36B-Opus", "url": 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"DeepSeek-V3.2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-27b.json", "title": "DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-thinkingcap-qwen3-8-27b.json", "title": "DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "agents-a1-4b": 90, "by": 30, "deepseek-v3-2": 60, "edge": "agents-a1-4b", "key": "reach", "name": "Reach", "weight": 25 }, { "agents-a1-4b": 55, "by": 5, "deepseek-v3-2": 50, "edge": "agents-a1-4b", "key": "risk", "name": "Risk", "weight": 20 }, { "agents-a1-4b": 70, "by": 10, "deepseek-v3-2": 60, "edge": "agents-a1-4b", "key": "interface", "name": "Agent interface", "weight": 10 }, { "agents-a1-4b": 43, "by": 9, "deepseek-v3-2": 34, "edge": "agents-a1-4b", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Agents-A1-4B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category. Both do open-weight models you run yourself.", "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.", "deepseek-v3-2": "DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2", "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.min.md" }, "markdown": "Agents-A1-4B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #34 of 100 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- DeepSeek-V3.2: grade C, 58.6/100, rank #65 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.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 2870 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- Reach, 90 against 60\n- Risk, 55 against 50\n- Agent interface, 70 against 60\n- Documentation and upkeep, 43 against 34\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\n\n### DeepSeek-V3.2 (C)\n\nGood for: Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider\n\nWatch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB\n\n\n## Score by category\n\n| Category | Weight | Agents-A1-4B | DeepSeek-V3.2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 60 | Agents-A1-4B +30 |\n| Risk | 20% (20 this run) | 55 | 50 | Agents-A1-4B +5 |\n| Agent interface | 10% (10 this run) | 70 | 60 | Agents-A1-4B +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 34 | Agents-A1-4B +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **58.6 · C** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | DeepSeek-V3.2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | DeepSeek |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2025-12-01 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3/5 (4) | 2.3/5 (4) |\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**DeepSeek-V3.2.** DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.\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### DeepSeek-V3.2\n\n1. Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3\n2. It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead\n3. Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only\n4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content\n5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role\n6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens\n\n## Questions\n\n### Which is better for AI agents, Agents-A1-4B or DeepSeek-V3.2?\n\nAgents-A1-4B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"deepseek-v3-2\"}`. From a terminal: `anchor compare agents-a1-4b deepseek-v3-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json\n\n## Other comparisons with Agents-A1-4B or DeepSeek-V3.2\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\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 BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md)\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.md)\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Agents-A1-4B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.md)\n- [Agents-A1-4B vs 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"attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Agents-A1-4B vs DeepSeek-V3.2", "url": "" } ], "description": "Agents-A1-4B scores 61 (C) to DeepSeek-V3.2's 58.6 (C) for open-weight models you run yourself. 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