# gpt-oss-20b vs MiniCPM-V 4.7 35B-A3B > gpt-oss-20b scores 69.9 (B) to MiniCPM-V 4.7 35B-A3B's 61 (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-minicpm-v-4-7-35b-a3b - Markdown: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-35b-a3b.md (~9,400 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 gpt-oss-20b scores 69.9 (B) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-V 4.7 35B-A3B leads on documentation and upkeep. Both do open-weight models you run yourself. - 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 - MiniCPM-V 4.7 35B-A3B: grade C, 61/100, rank #77 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minicpm-v-4-7-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/minicpm-v-4-7-35b-a3b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 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: - Risk, 85 against 55 - Agent interface, 90 against 70 Watch for: No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered ### MiniCPM-V 4.7 35B-A3B (C) Good for: Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence Ahead on: - Documentation and upkeep, 68 against 42 Watch for: No limitations section, safety results, agent-use guidance or security contact ## Why the edge ### Risk: gpt-oss-20b +30 - 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. - MiniCPM-V 4.7 35B-A3B (55): File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md. ### Documentation and upkeep: MiniCPM-V 4.7 35B-A3B +26 - 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. - MiniCPM-V 4.7 35B-A3B (68): Model card 8, it covers intended use, architecture and parameters but not limitations, context length, languages or a knowledge cut-off. Training data 0, none described. Last change 20, a commit on 3 November 2026. Responsiveness 20, OpenBMB answered 8 of 12 discussions. Version policy 10, earlier versions keep their repositories and the changes are dated and described. File health 10, the files agree. 8 + 0 + 20 + 20 + 10 + 10 = 68. ### Agent interface: gpt-oss-20b +20 - 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. - MiniCPM-V 4.7 35B-A3B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native. ## Score by category | Category | Weight | gpt-oss-20b | MiniCPM-V 4.7 35B-A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | 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 | 70 | gpt-oss-20b +20 | | Documentation and upkeep | 10% (10 this run) | 42 | 68 | MiniCPM-V 4.7 35B-A3B +26 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **69.9 · B** | **61 · C** | | ## Facts side by side | Fact | gpt-oss-20b | MiniCPM-V 4.7 35B-A3B | | --- | --- | --- | | 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 | 2026-11-03 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | 3.5/5 (4) | none | ## 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. **MiniCPM-V 4.7 35B-A3B.** A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it. ## 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 ### MiniCPM-V 4.7 35B-A3B 1. Start it with `llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja`, or `ollama run openbmb/minicpm-v4.7:35b-a3b` 2. For image input in llama.cpp add `mmproj-model-f16.gguf`, or the 4x projector for more visual detail at a higher cost 3. The GGUF files at the repository root have thinking off. Use the files in `thinking/` when you want the trace 4. With Transformers pass `enable_thinking` to `apply_chat_template`, and pass `downsample_mode` to both `apply_chat_template` and `generate` 5. Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends 6. Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format 7. Validate JSON output yourself, since structured output isn't documented ## Questions ### Which is better for AI agents, gpt-oss-20b or MiniCPM-V 4.7 35B-A3B? gpt-oss-20b scores 69.9 (B) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-V 4.7 35B-A3B leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-20b-vs-minicpm-v-4-7-35b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-20b", "b": "minicpm-v-4-7-35b-a3b"}`. 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