{ "data": { "a": { "slug": "gpt-oss-20b", "name": "gpt-oss-20b", "vendor": "OpenAI", "vendorUrl": "https://openai.com", "kind": "weights", "category": "open-weights", "summary": "gpt-oss-20b is OpenAI's open-weight mixture-of-experts reasoning model, 21B parameters with 3.6B active and a 131,072-token context, published on Hugging Face in MXFP4 under Apache 2.0 and trained on the harmony prompt format.", "url": "https://www.anchorterminal.com/tools/gpt-oss-20b", "markdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json", "dns": "gpt-oss-20b.grade.anchorterminal.com", "repo": "https://huggingface.co/openai/gpt-oss-20b", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "openai/gpt-oss-20b" }, { "registry": "ollama", "name": "gpt-oss:20b" }, { "registry": "pypi", "name": "gpt-oss" }, { "registry": "pypi", "name": "openai-harmony" } ], "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 that asks users to comply with applicable law. Running it yourself costs the hardware, about 13.8 GB of weights, and OpenAI says it runs within 16 GB of memory (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-20b", "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": 69.9, "grade": "B", "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": 6, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 49, "interface": 90, "reach": 90, "risk": 85, "upkeep": 42 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": 0, "verdict": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.", "bestFor": "Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time", "strengths": [ "Model code in released Transformers, vLLM, SGLang and llama.cpp, plus a 14 GB build in Ollama's library", "Fits a 24 GB machine at OpenAI's own MXFP4 precision, the one every evaluation used", "Apache 2.0 with no gate, no account and a usage policy that only asks users to follow the law", "Harmony guide documents tool declarations, call parsing, JSON Schema output and three reasoning levels, with OpenAI's own parser", "Trained on an instruction hierarchy against prompt injection, with OpenAI's results for it published" ], "weaknesses": [ "No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered", "One tool call per message", "Reasoning can be lowered but not switched off", "Only one quantised build from OpenAI, and its weights and template haven't changed since August 2025", "Safety results for this size are OpenAI's own, and it trails o4-mini on the instruction hierarchy" ], "agentNotes": [ "Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel", "Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it", "Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel", "Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default", "Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message", "Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users", "Expect one tool call per turn; run several in sequence rather than in parallel" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69.9 } ], "editorialScores": { "capability": 49, "interface": 90, "reach": 90, "risk": 85, "upkeep": 42 }, "provenanceScore": 89, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run gpt-oss:20b\n# or with vLLM:\nvllm serve openai/gpt-oss-20b\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/gpt-oss-20b-GGUF:MXFP4 --jinja", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gpt-oss:20b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gpt-oss-20b" }, "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-agents-api", "openai-codex", "gpt-oss-120b" ], "weights": { "trainedBy": "OpenAI", "params": "20.91B", "active": "3.61B", "context": 131072, "goodFor": "Text reasoning model for agents and coding, with function calling, built-in browser and Python tools and low, medium or high reasoning, in the harmony format.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ggml-org gpt-oss-20b-GGUF (MXFP4)", "url": "https://huggingface.co/ggml-org/gpt-oss-20b-GGUF", "who": "llama.cpp", "precision": "mxfp4", "sizeGB": 12.11, "memoryGB": 16, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 12.11 GB of MXFP4 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "gpt-oss-20b", "url": "https://huggingface.co/openai/gpt-oss-20b", "who": "publisher", "precision": "mxfp4", "sizeGB": 13.8, "memoryGB": 24, "format": "safetensors", "note": "MoE weights in MXFP4, the rest BF16, the precision OpenAI evaluated. original/ and metal/ hold the same weights for OpenAI's reference code. vLLM's gpt-oss recipe lists MI300x, MI325x, MI355x and Radeon AI PRO R9700 on AMD, not gfx1151" }, { "name": "ollama gpt-oss:20b", "url": "https://ollama.com/library/gpt-oss:20b", "who": "ollama", "precision": "mxfp4", "sizeGB": 14, "memoryGB": 24, "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:20b", "runBuild": "ollama gpt-oss:20b", "checked": "2026-10-21", "minMemoryGB": 16 }, "family": { "lab": "OpenAI", "family": "gpt-oss", "url": "https://www.anchorterminal.com/open-weights/#openai", "others": [ { "name": "gpt-oss-120b", "url": "https://huggingface.co/openai/gpt-oss-120b", "params": "117B total, 5.1B active (MoE)", "listing": "https://www.anchorterminal.com/tools/gpt-oss-120b" }, { "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 in USAGE_POLICY and the model card; the LICENSE file's copyright line is the unfilled Apache template)", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, years before the company was founded in 2015, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/openai/gpt-oss-20b/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "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 repository's commit history, whose messages say what changed for some commits (the chat template on 7 and 8 August 2025, `\u003c|call|\u003e` as an end token on 13 August 2025) and not for others (README updates).", "openai.com/.well-known/security.txt answered 200, signed, with a Bugcrowd contact and a coordinated disclosure policy, but no Expires field, which RFC 9116 requires. Recorded as valid, as on the openai-api listing, since the data has no value for a file without one.", "openai.com/index/introducing-gpt-oss/ and openai.com/open-models/ answered 403 to our reader on 21 October 2026, so OpenAI's announcement wasn't read." ], "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gpt-oss-20b.json", "live": { "slug": "gpt-oss-20b", "versions": [ { "registry": "pypi", "name": "gpt-oss", "version": "0.0.9", "released": "2026-01-13", "seenAt": "2026-11-10T03:44:02.954422791Z" }, { "registry": "pypi", "name": "openai-harmony", "version": "0.0.8", "released": "2025-11-05", "seenAt": "2026-11-10T03:44:04.979015076Z" } ], "pypiWeekly": 8711, "securityTxt": { "url": "https://openai.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-11-10T16:07:46.029073933Z" }, "domain": { "domain": "openai.com", "registered": "2007-01-19", "source": "https://rdap.verisign.com/com/v1/domain/openai.com", "checkedAt": "2026-11-08T14:52:04.4095079Z" }, "pages": [ { "url": "https://huggingface.co/openai/gpt-oss-20b/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:36.692225327Z", "changedAt": "2026-11-10T16:23:36.692225327Z", "fingerprint": "13cf2e621847" } ], "updatedAt": "2026-11-10T16:23:36.692225327Z" } }, "answer": "gpt-oss-20b scores 69.9 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B leads on documentation and upkeep.", "b": { "slug": "hy-memory-30b-a3b", "name": "Hy-Memory-30B-A3B", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "open-weights", "summary": "Hy-Memory-30B-A3B is Tencent Hunyuan's open-weight memory writer, a 30B mixture of experts with about 3B active, trained to decide what an agent's memory framework keeps and updates inside Mem0, MemOS and Hy-Memory, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json", "dns": "hy-memory-30b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-GGUF" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main, FP8 and GGUF repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 60.1 GB for the BF16 files, 30.6 GB in FP8 or 18.2 GB for Tencent's Q4_K_M GGUF (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/tencent/Hy-Memory-30B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "agent-memory", "specialist", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-10-16", "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": 54, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 85, "risk": 60, "upkeep": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.", "bestFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card", "strengths": [ "Made for one job, deciding what a memory framework stores and updates, and tested in four frameworks", "Fits a 24 GB card from Tencent's own Q4_K_M GGUF", "Runs in llama.cpp, vLLM, SGLang and Transformers from their releases", "Plain Apache 2.0, ungated, safetensors and GGUF only", "Tencent replied to three of the four discussions" ], "weaknesses": [ "Not a general assistant or a tool caller", "No limitations, safety results or guidance on untrusted text in what it reads", "Training data isn't described, and the card gives no context length", "The results are on a 20% subsample, run by Tencent", "No inference provider, so it has to be run locally" ], "agentNotes": [ "Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user", "Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does", "Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does", "Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented", "Treat what it stores as unverified. It keeps what a conversation says, true or not" ], "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": 85, "risk": 60, "upkeep": 68 }, "provenanceScore": 75, "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": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja\n# or, with vLLM, as the card names it:\nvllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"hy-memory\", \"temperature\": 0, \"messages\": [{\"role\": \"system\", \"content\": \"Extract explicit user facts and preferences. Return only a JSON object with a memories list of strings.\"}, {\"role\": \"user\", \"content\": \"I live in Seattle and I am vegetarian.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"no_think\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/hy-memory-30b-a3b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy3", "wevisone-vl-2b", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent (Hunyuan)", "builtOn": "Not declared. The architecture is Tencent's hy_v3, as Hy3", "params": "30.1B", "active": "3B", "context": 262144, "goodFor": "The write path of an agent's memory framework. It reads a conversation and decides what to store and how to update it, in Mem0, MemOS or Hy-Memory, in place of a general model.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Hy-Memory-30B-A3B-Q4_K_M", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 18.24, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "The smallest file at 4 bits. Q3_K_M (14.45 GB) and the two files with experts at 1 or 2 bits are below the 4-bit line and are not listed" }, { "name": "Hy-Memory-30B-A3B-Q6_K", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q6_k", "sizeGB": 24.71, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B-FP8", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 30.58, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Hy-Memory-30B-A3B-Q8_0", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 31.99, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 60.13, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja", "runBuild": "Hy-Memory-30B-A3B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "Hy-Memory", "url": "https://www.anchorterminal.com/open-weights/#tencent", "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text, as on Hy3.", "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 repository's commit history, five commits on 15 and 16 October 2026, the uploads, the card and the licence.", "The Hy3 listing found no security.txt on Tencent's domains and records Tencent's Security Response Center as the reporting route. unchecked on 10 November." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.json" }, "edges": [ { "by": 26, "category": "upkeep", "leads": "hy-memory-30b-a3b", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 17, covering six of the seven points and missing a statement of supported languages. Training data earns 10, a general description with a June 2024 cut-off and no sources or mix. Last change earns 5, because the files haven't changed since August 2025 and the reference code and harmony parser last changed on 1 and 5 November 2025, still within a year on 1 November 2026, while this year's commits only touch the browser tool, CI and a version number. Responsiveness earns 0, with no OpenAI reply to any of the 40 most recent discussions and none at all since August 2025. Version policy earns 5 for a first release kept in its own repository. File health earns 5, since template reports and an install report wait without a reply.", "b": "Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68." }, "scores": { "a": 42, "b": 68 } }, { "by": 25, "category": "risk", "leads": "gpt-oss-20b", "name": "Risk", "reasons": { "a": "File safety earns 25, since the weights are safetensors and metal/model.bin is OpenAI's own raw Metal format rather than pickle, and nothing in the repository runs as code (a judgement call on the .bin name). Provenance earns 15, files in OpenAI's own organisation and a checksum on every file. Safety evaluation earns 10, because the published safety results for this size are OpenAI's own, and the outside review covered gpt-oss-120b. Agent guidance earns 15 for an instruction hierarchy the model was trained to follow, with prompt-injection results, though OpenAI reports it trails o4-mini there. Licence risk keeps 15, with nothing to deduct under Apache 2.0. The security programme earns 5 for OpenAI's security.txt with a Bugcrowd contact. Public advisories for the gpt-oss code couldn't be checked, so that half scores as absent.", "b": "File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60." }, "scores": { "a": 85, "b": 60 } }, { "by": 25, "category": "interface", "leads": "gpt-oss-20b", "name": "Agent interface", "reasons": { "a": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since OpenAI's harmony guide says how functions are declared and how calls come back, and OpenAI ships the openai-harmony parser. Trained tool calling earns 10, because each call ends the message and waits for its result, so it makes one call at a time. Structured output earns 15 for a JSON Schema given in the developer message, documented in the harmony guide. Reasoning control earns 10, with low, medium or high effort per request and the reasoning on its own channel, though it can't be switched off. Context earns 10 for 131,072 tokens, which OpenAI describes as trained in even though the config reaches it with YaRN.", "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65." }, "scores": { "a": 90, "b": 65 } }, { "by": 5, "category": "reach", "leads": "gpt-oss-20b", "name": "Reach", "reasons": { "a": "Memory earns 25, since OpenAI's own MXFP4 build is 13.76 GB and a fifth on top is 16.5 GB, just past the 16 GB band, and Ollama's 14 GB build lands in the same 24 GB band. Runtimes earn the full 25 from model code in released Transformers, vLLM and SGLang, the architecture in llama.cpp and the model in Ollama's library. Builds earn 10, because OpenAI ships one quantised precision, MXFP4, and the original and metal folders are the same weights for its reference code. Terms earn 15 for Apache 2.0, whose usage policy only asks users to follow the law. Download earns 10, since the repository isn't gated. Hosted earns 5, with five inference providers live for it on its Hugging Face page on 1 November 2026, while OpenAI's own API lists it with no usable rate limit and isn't counted.", "b": "Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85." }, "scores": { "a": 90, "b": 85 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "OpenAI", "b": "Tencent (Hy team)", "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": "2025-08-26", "b": "2026-10-16", "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": "3.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "gpt-oss-20b scores 69.9 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B leads on documentation and upkeep.", "question": "Which is better for AI agents, gpt-oss-20b or Hy-Memory-30B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 85", "Risk, 85 against 60", "Agent interface, 90 against 65" ], "also": null, "goodFor": "Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time", "slug": "gpt-oss-20b", "watchFor": "No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered" }, { "aheadOn": [ "Documentation and upkeep, 68 against 42" ], "also": null, "goodFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card", "slug": "hy-memory-30b-a3b", "watchFor": "Not a general assistant or a tool caller" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next.json", "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Hy-Memory-30B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint.json", "title": "Hy-Memory-30B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview.json", "title": "Hy-Memory-30B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b.json", "title": "Hy-Memory-30B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b.json", "title": "Hy-Memory-30B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.json", "title": "Hy-Memory-30B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2.json", "title": "Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json", "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.json", "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.json", "title": "Hy-Memory-30B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "gpt-oss-20b", "gpt-oss-20b": 90, "hy-memory-30b-a3b": 85, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 25, "edge": "gpt-oss-20b", "gpt-oss-20b": 85, "hy-memory-30b-a3b": 60, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 25, "edge": "gpt-oss-20b", "gpt-oss-20b": 90, "hy-memory-30b-a3b": 65, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 26, "edge": "hy-memory-30b-a3b", "gpt-oss-20b": 42, "hy-memory-30b-a3b": 68, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "gpt-oss-20b scores 69.9 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "gpt-oss-20b": "gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.", "hy-memory-30b-a3b": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy-memory-30b-a3b", "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy-memory-30b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy-memory-30b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy-memory-30b-a3b.min.md" }, "markdown": "gpt-oss-20b scores 69.9 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- gpt-oss-20b: grade B, 69.9/100, rank #8 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-20b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json\n- Hy-Memory-30B-A3B: grade C, 61/100, rank #68 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.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### gpt-oss-20b (B)\n\nGood for: Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time\n\nAhead on:\n- Reach, 90 against 85\n- Risk, 85 against 60\n- Agent interface, 90 against 65\n\nWatch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered\n\n### Hy-Memory-30B-A3B (C)\n\nGood for: Agents with an external memory framework that want a local model for the write path on a 24 GB card\n\nAhead on:\n- Documentation and upkeep, 68 against 42\n\nWatch for: Not a general assistant or a tool caller\n\n\n## Why the edge\n\n### Documentation and upkeep: Hy-Memory-30B-A3B +26\n\n- gpt-oss-20b (42): The model card earns 17, covering six of the seven points and missing a statement of supported languages. Training data earns 10, a general description with a June 2024 cut-off and no sources or mix. Last change earns 5, because the files haven't changed since August 2025 and the reference code and harmony parser last changed on 1 and 5 November 2025, still within a year on 1 November 2026, while this year's commits only touch the browser tool, CI and a version number. Responsiveness earns 0, with no OpenAI reply to any of the 40 most recent discussions and none at all since August 2025. Version policy earns 5 for a first release kept in its own repository. File health earns 5, since template reports and an install report wait without a reply.\n- Hy-Memory-30B-A3B (68): Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68.\n\n### Risk: gpt-oss-20b +25\n\n- gpt-oss-20b (85): File safety earns 25, since the weights are safetensors and metal/model.bin is OpenAI's own raw Metal format rather than pickle, and nothing in the repository runs as code (a judgement call on the .bin name). Provenance earns 15, files in OpenAI's own organisation and a checksum on every file. Safety evaluation earns 10, because the published safety results for this size are OpenAI's own, and the outside review covered gpt-oss-120b. Agent guidance earns 15 for an instruction hierarchy the model was trained to follow, with prompt-injection results, though OpenAI reports it trails o4-mini there. Licence risk keeps 15, with nothing to deduct under Apache 2.0. The security programme earns 5 for OpenAI's security.txt with a Bugcrowd contact. Public advisories for the gpt-oss code couldn't be checked, so that half scores as absent.\n- Hy-Memory-30B-A3B (60): File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n### Agent interface: gpt-oss-20b +25\n\n- gpt-oss-20b (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since OpenAI's harmony guide says how functions are declared and how calls come back, and OpenAI ships the openai-harmony parser. Trained tool calling earns 10, because each call ends the message and waits for its result, so it makes one call at a time. Structured output earns 15 for a JSON Schema given in the developer message, documented in the harmony guide. Reasoning control earns 10, with low, medium or high effort per request and the reasoning on its own channel, though it can't be switched off. Context earns 10 for 131,072 tokens, which OpenAI describes as trained in even though the config reaches it with YaRN.\n- Hy-Memory-30B-A3B (65): Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.\n\n### Reach: gpt-oss-20b +5\n\n- gpt-oss-20b (90): Memory earns 25, since OpenAI's own MXFP4 build is 13.76 GB and a fifth on top is 16.5 GB, just past the 16 GB band, and Ollama's 14 GB build lands in the same 24 GB band. Runtimes earn the full 25 from model code in released Transformers, vLLM and SGLang, the architecture in llama.cpp and the model in Ollama's library. Builds earn 10, because OpenAI ships one quantised precision, MXFP4, and the original and metal folders are the same weights for its reference code. Terms earn 15 for Apache 2.0, whose usage policy only asks users to follow the law. Download earns 10, since the repository isn't gated. Hosted earns 5, with five inference providers live for it on its Hugging Face page on 1 November 2026, while OpenAI's own API lists it with no usable rate limit and isn't counted.\n- Hy-Memory-30B-A3B (85): Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85.\n\n## Score by category\n\n| Category | Weight | gpt-oss-20b | Hy-Memory-30B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 85 | gpt-oss-20b +5 |\n| Risk | 20% (20 this run) | 85 | 60 | gpt-oss-20b +25 |\n| Agent interface | 10% (10 this run) | 90 | 65 | gpt-oss-20b +25 |\n| Documentation and upkeep | 10% (10 this run) | 42 | 68 | Hy-Memory-30B-A3B +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **69.9 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | gpt-oss-20b | Hy-Memory-30B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | OpenAI | Tencent (Hy team) |\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 | 2025-08-26 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.5/5 (4) | none |\n\n## Verdicts\n\n**gpt-oss-20b.** gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.\n\n**Hy-Memory-30B-A3B.** A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.\n\n## Before you call either\n\n### gpt-oss-20b\n\n1. Start it with `ollama run gpt-oss:20b` (the build that fits the reference machine), or `vllm serve openai/gpt-oss-20b` with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel\n2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it\n3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `\u003c|call|\u003e`, reasoning on the analysis channel\n4. Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default\n5. Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message\n6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users\n7. Expect one tool call per turn; run several in sequence rather than in parallel\n\n### Hy-Memory-30B-A3B\n\n1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user\n2. Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does\n3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does\n4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented\n5. Treat what it stores as unverified. It keeps what a conversation says, true or not\n\n## Questions\n\n### Which is better for AI agents, gpt-oss-20b or Hy-Memory-30B-A3B?\n\ngpt-oss-20b scores 69.9 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy-memory-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy-memory-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gpt-oss-20b\", \"b\": \"hy-memory-30b-a3b\"}`. From a terminal: `anchor compare gpt-oss-20b hy-memory-30b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-20b.json and https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json\n\n## Other comparisons with gpt-oss-20b or Hy-Memory-30B-A3B\n\n- [Agents-A1-4B vs gpt-oss-20b](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b.md)\n- [Agents-A1-4B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.md)\n- [Apertus v1.5 8B vs gpt-oss-20b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.md)\n- [Apertus v1.5 8B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.md)\n- [Apodex-1.1-mini vs gpt-oss-20b](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b.md)\n- [Apodex-1.1-mini vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md)\n- [Arete-27B vs gpt-oss-20b](https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b.md)\n- [Arete-27B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.md)\n- [BigBang-v1 vs gpt-oss-20b](https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b.md)\n- [BigBang-v1 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy-memory-30b-a3b.md)\n- [Carbon-X vs gpt-oss-20b](https://www.anchorterminal.com/compare/carbon-x-vs-gpt-oss-20b.md)\n- [Carbon-X vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b.md)\n- [Cola2 vs gpt-oss-20b](https://www.anchorterminal.com/compare/cola2-vs-gpt-oss-20b.md)\n- [Cola2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b.md)\n- [Cosmos-Reason2-2B vs gpt-oss-20b](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gpt-oss-20b.md)\n- [Cosmos-Reason2-2B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy-memory-30b-a3b.md)\n- [Darwin-36B-Opus vs gpt-oss-20b](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gpt-oss-20b.md)\n- [Darwin-36B-Opus vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy-memory-30b-a3b.md)\n- [DeepSeek-V3.2 vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gpt-oss-20b.md)\n- [DeepSeek-V3.2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy-memory-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b.md)\n- [DeepSeek V4.1 Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy-memory-30b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gpt-oss-20b.md)\n- [DeepSeek-V4-Flash-0731 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy-memory-30b-a3b.md)\n- [Devstral Small 2 vs gpt-oss-20b](https://www.anchorterminal.com/compare/devstral-small-2-vs-gpt-oss-20b.md)\n- [Devstral Small 2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b.md)\n- [DiffusionGemma 26B A4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gpt-oss-20b.md)\n- [DiffusionGemma 26B A4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-hy-memory-30b-a3b.md)\n- [EXAONE 4.5 33B vs gpt-oss-20b](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gpt-oss-20b.md)\n- [EXAONE 4.5 33B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 12B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 12B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 26B A4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 26B A4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 31B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 31B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 E2B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 E2B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 E4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 E4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy-memory-30b-a3b.md)\n- [GLM-4.5-Air vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b.md)\n- [GLM-4.5-Air vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b.md)\n- [GLM-4.7-Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-gpt-oss-20b.md)\n- [GLM-4.7-Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy-memory-30b-a3b.md)\n- [GLM-5.3-Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b.md)\n- [GLM-5.3-Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.md)\n- [GLM-5.3 vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-5-3-vs-gpt-oss-20b.md)\n- [GLM-5.3 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-hy-memory-30b-a3b.md)\n- [gpt-oss-120b vs gpt-oss-20b](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-gpt-oss-20b.md)\n- [gpt-oss-120b vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy-memory-30b-a3b.md)\n- [gpt-oss-20b vs Granite 4.2 30B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-granite-4-2-30b.md)\n- [gpt-oss-20b vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-granite-4-2-3b.md)\n- [gpt-oss-20b vs Granite 4.2 8B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-granite-4-2-8b.md)\n- [gpt-oss-20b vs Hy3](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy3.md)\n- [gpt-oss-20b vs Inkling](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling.md)\n- [gpt-oss-20b vs Inkling-Small](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling-small.md)\n- [gpt-oss-20b vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [gpt-oss-20b vs Kimi K3](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3.md)\n- [gpt-oss-20b vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3-1-pro.md)\n- [gpt-oss-20b vs Kolibri-1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kolibri-1.md)\n- [gpt-oss-20b vs Kronos](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kronos.md)\n- [gpt-oss-20b vs Laguna M.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-m-1.md)\n- [gpt-oss-20b vs Laguna S 2.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-s-2-1.md)\n- [gpt-oss-20b vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-laguna-xs-2-1.md)\n- [gpt-oss-20b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b.md)\n- [gpt-oss-20b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.md)\n- [gpt-oss-20b vs LFM3-1.7B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-1-7b.md)\n- [gpt-oss-20b vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-audio-2b.md)\n- [gpt-oss-20b vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-omni-2b.md)\n- [gpt-oss-20b vs LFM3-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm3-vl-2b.md)\n- [gpt-oss-20b vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-0-tiny.md)\n- [gpt-oss-20b vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-1-flash-1019.md)\n- [gpt-oss-20b vs LongCat-2.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-longcat-2-5.md)\n- [gpt-oss-20b vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [gpt-oss-20b vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl.md)\n- [gpt-oss-20b vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-o-4-5.md)\n- [gpt-oss-20b vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-1b.md)\n- [gpt-oss-20b vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-35b-a3b.md)\n- [gpt-oss-20b vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b.md)\n- [gpt-oss-20b vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-5.md)\n- [gpt-oss-20b vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m2-7.md)\n- [gpt-oss-20b vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minimax-m3-1-flash.md)\n- [gpt-oss-20b vs Mistral Large 4](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mistral-large-4.md)\n- [gpt-oss-20b vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-muse-glimmer-30b.md)\n- [gpt-oss-20b vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nanbeige-4-2-3b.md)\n- [gpt-oss-20b vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nanbeige-4-3-33b.md)\n- [gpt-oss-20b vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [gpt-oss-20b vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [gpt-oss-20b vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-30b-a3b.md)\n- [gpt-oss-20b vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-4b.md)\n- [gpt-oss-20b vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [gpt-oss-20b vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-super-120b-a12b.md)\n- [gpt-oss-20b vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-ultra-550b-a55b.md)\n- [gpt-oss-20b vs North Mini Code 1.0](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-north-mini-code-1-0.md)\n- [gpt-oss-20b vs occamy-1.0](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0.md)\n- [gpt-oss-20b vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-olmo-3-7b-think.md)\n- [gpt-oss-20b vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-122b.md)\n- [gpt-oss-20b vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-35b-a3b.md)\n- [gpt-oss-20b vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-397b.md)\n- [gpt-oss-20b vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b.md)\n- [gpt-oss-20b vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-otel-2-0-llm-31b-it.md)\n- [gpt-oss-20b vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen-agentworld-35b-a3b.md)\n- [gpt-oss-20b vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [gpt-oss-20b vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-4b-instruct-2507.md)\n- [gpt-oss-20b vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-0-8b.md)\n- [gpt-oss-20b vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-122b-a10b.md)\n- [gpt-oss-20b vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-2b.md)\n- [gpt-oss-20b vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-397b-a17b.md)\n- [gpt-oss-20b vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-4b.md)\n- [gpt-oss-20b vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-5-9b.md)\n- [gpt-oss-20b vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-27b.md)\n- [gpt-oss-20b vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-6-35b-a3b.md)\n- [gpt-oss-20b vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-27b.md)\n- [gpt-oss-20b vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-8-flash-next.md)\n- [gpt-oss-20b vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [gpt-oss-20b vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-next.md)\n- [gpt-oss-20b vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [gpt-oss-20b vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-vl-2b-instruct.md)\n- [gpt-oss-20b vs 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