{ "data": { "a": { "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" }, "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.", "b": { "slug": "gpt-oss-120b", "name": "gpt-oss-120b", "vendor": "OpenAI", "vendorUrl": "https://openai.com", "kind": "weights", "category": "open-weights", "summary": "gpt-oss-120b is OpenAI's open-weight mixture-of-experts reasoning model, 116.8B parameters with 5.1B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gpt-oss-120b", "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json", "repo": "https://huggingface.co/openai/gpt-oss-120b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openai/gpt-oss-120b" }, { "registry": "pypi", "name": "gpt-oss" }, { "registry": "pypi", "name": "openai-harmony" }, { "registry": "ollama", "name": "gpt-oss:120b" } ], "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 Apache 2.0 with a one-line usage policy asking users to comply with applicable law. OpenAI's API page for the model shows no price and rate limits of 0 at every tier. Running it yourself costs the hardware, 65.2 GB for the MXFP4 files and 65 GB for Ollama's gpt-oss:120b build (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/openai/gpt-oss-120b", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "apache-2.0", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2025-08-26", "graded": true, "anchor": { "graded": true, "score": 70.2, "grade": "BB", "agentReady": true, "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": 4, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 54, "interface": 90, "reach": 75, "risk": 95, "upkeep": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.", "bestFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support", "strengths": [ "Runs in Transformers, vLLM, SGLang, llama.cpp and Ollama, with LM Studio linked from the card", "Apache 2.0, no gate, and 11 inference providers serve it for agents without the hardware", "Safetensors only, a trained instruction hierarchy and an outside biosecurity evaluation", "Harmony tool format, JSON Schema output and three reasoning levels documented by OpenAI" ], "weaknesses": [ "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision", "One tool call at a time", "No OpenAI reply in the last 40 discussions, including open template and tokenizer reports", "No change to the files since August 2025 or to the model code since November 2025", "Reasoning can be lowered but not turned off" ], "agentNotes": [ "Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits", "On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs", "With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony", "Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise", "Set `Reasoning: low` in the system message for short tasks, medium is the default", "Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users", "Expect one tool call per turn and loop, don't wait for parallel calls" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70.2 } ], "editorialScores": { "capability": 54, "interface": 90, "reach": 75, "risk": 95, "upkeep": 45 }, "provenanceScore": 87, "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": "ollama run gpt-oss:120b\n# or, with vLLM's recipe:\nvllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gpt-oss:120b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gpt-oss-120b" }, "sameCompany": [ "openai-api", "openai-embeddings", "openai-guardrails", "openai-moderation", "openai-image-api", "openai-sora", "openai-speech-to-text", "gpt-live", "openai-realtime", "openai-agents-sdk", "openai-decisions-api", "openai-codex", "gpt-oss-20b" ], "weights": { "trainedBy": "OpenAI", "params": "116.83B", "active": "5.13B", "context": 131072, "goodFor": "Larger text reasoning model for agents and coding, with function calling, built-in browser and Python tools and three reasoning levels, in the harmony format.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b", "who": "publisher", "precision": "mxfp4", "sizeGB": 65.2, "memoryGB": 96, "format": "safetensors", "note": "MoE weights in MXFP4, the rest BF16. original/ and metal/ duplicate the weights. vLLM's gpt-oss recipe doesn't list gfx1151" }, { "name": "ollama gpt-oss:120b", "url": "https://ollama.com/library/gpt-oss:120b", "who": "ollama", "precision": "mxfp4", "sizeGB": 65, "memoryGB": 96, "format": "ollama", "runsOn": [ "Ollama" ], "note": "OpenAI's MXFP4 weights; the card links it. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" } ], "run": "ollama run gpt-oss:120b", "runBuild": "ollama gpt-oss:120b", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "OpenAI", "family": "gpt-oss", "url": "https://www.anchorterminal.com/open-weights/#openai", "others": [ { "name": "gpt-oss-20b", "url": "https://huggingface.co/openai/gpt-oss-20b", "params": "21B total, 3.6B active (MoE)", "listing": "https://www.anchorterminal.com/tools/gpt-oss-20b" }, { "name": "gpt-oss-safeguard-120b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-120b", "params": "117B total, 5.1B active (MoE)" }, { "name": "gpt-oss-safeguard-20b", "url": "https://huggingface.co/openai/gpt-oss-safeguard-20b", "params": "21B total, 3.6B active (MoE)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "OpenAI (named as publisher on the card; the openai-api listing's terms name OpenAI OpCo, LLC)", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, before OpenAI existed.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/openai/gpt-oss/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The LICENSE file is Apache 2.0 with the template's placeholder copyright line, so the legal entity is the one OpenAI's own terms name, read for the openai-api listing.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the gpt-oss GitHub repository's commit history and tags (v0.0.1 to v0.0.9); the Hugging Face repository's commits stop on 26 August 2025.", "openai.com/.well-known/security.txt answered on 21 October 2026 with a Bugcrowd contact, an acknowledgements page and a coordinated vulnerability disclosure policy. It has no Expires line, which RFC 9116 requires." ], "score": 87 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-120b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "DeepSeek", "b": "OpenAI", "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": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2025-12-01", "b": "2025-08-26", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.3/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "gpt-oss-120b scores 70.2 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.", "question": "Which is better for AI agents, DeepSeek-V3.2 or gpt-oss-120b?" } ], "goodFor": [ { "aheadOn": [ "Capability, 69 against 54" ], "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" }, { "aheadOn": [ "Reach, 75 against 60", "Risk, 95 against 50", "Agent interface, 90 against 60", "Documentation and upkeep, 45 against 34" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 2.3 out of 5" ], "goodFor": "Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support", "slug": "gpt-oss-120b", "watchFor": "Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json", "title": "Agents-A1-4B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b.json", "title": "Agents-A1-4B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json", "title": "Apertus v1.5 8B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.json", "title": "Apertus v1.5 8B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.json", "title": "Apodex-1.1-mini vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b.json", "title": "Apodex-1.1-mini vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.json", "title": "BigBang-v1 vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b.json", "title": "BigBang-v1 vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v3-2.json", "title": "Cosmos-Reason2-2B vs DeepSeek-V3.2", "url": 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Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-4b-instruct-2507.json", "title": "gpt-oss-120b vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-0-8b.json", "title": "gpt-oss-120b vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-122b-a10b.json", "title": "gpt-oss-120b vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-2b.json", "title": "gpt-oss-120b vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-397b-a17b.json", "title": "gpt-oss-120b vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-4b.json", "title": "gpt-oss-120b vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b.json", "title": "gpt-oss-120b vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-27b.json", "title": "gpt-oss-120b vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-6-35b-a3b.json", "title": "gpt-oss-120b vs Qwen3.6-35B-A3B", "url": 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"https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-4b-instruct.json", "title": "gpt-oss-120b vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct.json", "title": "gpt-oss-120b vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2.json", "title": "gpt-oss-120b vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b.json", "title": "gpt-oss-120b vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.json", "title": "gpt-oss-120b vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b.json", "title": "gpt-oss-120b vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "gpt-oss-120b vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b.json", "title": "gpt-oss-120b vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 15, "deepseek-v3-2": 69, "edge": "deepseek-v3-2", "gpt-oss-120b": 54, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 15, "deepseek-v3-2": 60, "edge": "gpt-oss-120b", "gpt-oss-120b": 75, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 45, "deepseek-v3-2": 50, "edge": "gpt-oss-120b", "gpt-oss-120b": 95, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 30, "deepseek-v3-2": 60, "edge": "gpt-oss-120b", "gpt-oss-120b": 90, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 11, "deepseek-v3-2": 34, "edge": "gpt-oss-120b", "gpt-oss-120b": 45, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "gpt-oss-120b scores 70.2 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "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.", "gpt-oss-120b": "A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b", "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b.min.md" }, "markdown": "gpt-oss-120b scores 70.2 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability. Both do open-weight models you run yourself.\n\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- gpt-oss-120b: grade BB, 70.2/100, rank #6 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.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### 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\nAhead on:\n- Capability, 69 against 54\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### gpt-oss-120b (BB)\n\nGood for: Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support\n\nAhead on:\n- Reach, 75 against 60\n- Risk, 95 against 50\n- Agent interface, 90 against 60\n- Documentation and upkeep, 45 against 34\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 2.3 out of 5\n\nWatch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision\n\n\n## Score by category\n\n| Category | Weight | DeepSeek-V3.2 | gpt-oss-120b | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 69 | 54 | DeepSeek-V3.2 +15 |\n| Reach | 25% (25 this run) | 60 | 75 | gpt-oss-120b +15 |\n| Risk | 20% (20 this run) | 50 | 95 | gpt-oss-120b +45 |\n| Agent interface | 10% (10 this run) | 60 | 90 | gpt-oss-120b +30 |\n| Documentation and upkeep | 10% (10 this run) | 34 | 45 | gpt-oss-120b +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.6 · C** | **70.2 · BB** | |\n\n## Facts side by side\n\n| Fact | DeepSeek-V3.2 | gpt-oss-120b |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | OpenAI |\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 | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-12-01 | 2025-08-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.3/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**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**gpt-oss-120b.** A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.\n\n## Before you call either\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### gpt-oss-120b\n\n1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits\n2. On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs\n3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony\n4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise\n5. Set `Reasoning: low` in the system message for short tasks, medium is the default\n6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users\n7. Expect one tool call per turn and loop, don't wait for parallel calls\n\n## Questions\n\n### Which is better for AI agents, DeepSeek-V3.2 or gpt-oss-120b?\n\ngpt-oss-120b scores 70.2 (BB) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 4 of 5 scored categories. DeepSeek-V3.2 leads on capability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v3-2\", \"b\": \"gpt-oss-120b\"}`. From a terminal: `anchor compare deepseek-v3-2 gpt-oss-120b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json and https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json\n\n## Other comparisons with DeepSeek-V3.2 or gpt-oss-120b\n\n- [Agents-A1-4B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md)\n- [Agents-A1-4B vs gpt-oss-120b](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b.md)\n- [Apertus v1.5 8B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.md)\n- [Apertus v1.5 8B vs gpt-oss-120b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.md)\n- [Apodex-1.1-mini vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.md)\n- [Apodex-1.1-mini vs gpt-oss-120b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b.md)\n- [BigBang-v1 vs 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27B)](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "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": "DeepSeek-V3.2 vs gpt-oss-120b", "url": "" } ], "description": "gpt-oss-120b scores 70.2 (BB) to DeepSeek-V3.2's 58.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "DeepSeek-V3.2 C 58.6", "gpt-oss-120b BB 70.2", "scores" ], "h1": "DeepSeek-V3.2 vs gpt-oss-120b", "image": "https://www.anchorterminal.com/assets/og/compare-deepseek-v3-2-vs-gpt-oss-120b.png", "path": "/compare/deepseek-v3-2-vs-gpt-oss-120b", "published": "2026-10-01", "section": "tools", "title": "DeepSeek-V3.2 vs gpt-oss-120b for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-120b" }, "tokens": { "markdown": 6000, "slim": 480 }, "version": 1 }