# gpt-oss-20b vs MiniCPM5-2B > gpt-oss-20b scores 69.9 (B) to MiniCPM5-2B's 60.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b - Markdown: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b.md (~5,950 tokens) - Slim: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 gpt-oss-20b scores 69.9 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on documentation and upkeep. Both do open-weight models you run yourself. - gpt-oss-20b: grade B, 69.9/100, rank #8 of 100 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 - MiniCPM5-2B: grade C, 60.2/100, rank #56 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm5-2b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### gpt-oss-20b (B) Good 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 Ahead on: - Capability, 49 against 27 - Risk, 85 against 55 - Agent interface, 90 against 75 Also in its favour: - Rated higher by the reviewer agents, 3.5 against 3.0 out of 5 Watch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered ### MiniCPM5-2B (C) Good for: On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data Ahead on: - Documentation and upkeep, 97 against 42 Watch for: OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning ## Score by category | Category | Weight | gpt-oss-20b | MiniCPM5-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 49 | 27 | gpt-oss-20b +22 | | Reach | 25% (25 this run) | 90 | 90 | even | | Risk | 20% (20 this run) | 85 | 55 | gpt-oss-20b +30 | | Agent interface | 10% (10 this run) | 90 | 75 | gpt-oss-20b +15 | | Documentation and upkeep | 10% (10 this run) | 42 | 97 | MiniCPM5-2B +55 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **69.9 · B** | **60.2 · C** | | ## Facts side by side | Fact | gpt-oss-20b | MiniCPM5-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenAI | OpenBMB | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-08-26 | 2026-09-29 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 3.5/5 (4) | 3/5 (4) | ## 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. **MiniCPM5-2B.** A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0. ## Before you call either ### gpt-oss-20b 1. 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 2. Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it 3. Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in `<|call|>`, reasoning on the analysis channel 4. Set effort with `Reasoning: high` (or low, medium) in the system message, or `reasoning_effort` in the template; medium is the default 5. Ask for JSON by putting a JSON Schema under `# Response Formats` in the developer message 6. Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users 7. Expect one tool call per turn; run several in sequence rather than in parallel ### MiniCPM5-2B 1. Smallest start is `ollama run openbmb/minicpm5-2b` (1.6 GB, q4_k_m) 2. On vLLM 0.29.0 or later run `vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5`, or tool calls come back as XML text 3. On SGLang 0.5.16 or later add `--tool-call-parser minicpm5 --reasoning-parser qwen3`; SGLang is OpenBMB's recommended backend for tool calls 4. Tool calls are XML, ``, not Hermes JSON 5. Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does 6. Expect a `` block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented 8. Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix ## Questions ### Which is better for AI agents, gpt-oss-20b or MiniCPM5-2B? gpt-oss-20b scores 69.9 (B) on agent readiness against MiniCPM5-2B's 60.2 (C), and leads in 3 of 5 scored categories. MiniCPM5-2B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm5-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-20b", "b": "minicpm5-2b"}`. 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