# gpt-oss-120b vs MiniCPM-V 4.7 35B-A3B > gpt-oss-120b scores 70.2 (BB) 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-120b-vs-minicpm-v-4-7-35b-a3b - Markdown: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-35b-a3b.md (~9,600 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-120b scores 70.2 (BB) 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 reach and documentation and upkeep. Both do open-weight models you run yourself. - gpt-oss-120b: grade BB, 70.2/100, rank #6 of 160 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 - 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-120b (BB) Good 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 Ahead on: - Risk, 95 against 55 - Agent interface, 90 against 70 Also in its favour: - Agent-ready, a grade of BB or better Watch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision ### 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: - Reach, 90 against 75 - Documentation and upkeep, 68 against 45 Watch for: No limitations section, safety results, agent-use guidance or security contact ## Why the edge ### Risk: gpt-oss-120b +40 - gpt-oss-120b (95): File safety earns 25, safetensors only, with metal/model.bin a raw format and not pickle, and nothing from the repository runs on load. Provenance earns 15, files in OpenAI's linked organisation with a hash for every file. Safety evaluation earns 20, published results with SecureBio as an outside evaluator. Agent guidance earns 15 for the trained instruction hierarchy with prompt-injection results. Licence risk keeps all 15. The security programme earns 5 for OpenAI's Bugcrowd programme and disclosure policy, and the advisories half scores 0 because GitHub couldn't be checked, not because it was found missing. - 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 +23 - gpt-oss-120b (45): The model card earns 20, with all seven items in OpenAI's arXiv card. Training data earns 10, a general description with a cut-off. Last change earns 5, since the last change to code that runs the model was the Metal reference on 1 November 2025, a year before this check, and the files date from August 2025. It drops to 0 after 1 November 2026 unless the files, template or reference code change. Responsiveness earns 0, with no OpenAI reply in the 40 latest discussions. Version policy earns 5 as a first release. File health earns 5, the files agree but template and tokenizer reports 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-120b +20 - gpt-oss-120b (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since OpenAI documents declarations and returned calls in harmony and tells vLLM users to parse with openai-harmony. Trained tool calling earns 10, because each call stops generation, so calls come one at a time. Structured output earns 15 for the documented Response Formats section with a JSON Schema. Reasoning control earns 10, three effort levels per request with the reasoning on the analysis channel. Context earns 10 for 131,072 trained tokens. - 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. ### Reach: MiniCPM-V 4.7 35B-A3B +15 - gpt-oss-120b (75): Memory earns 10, because the smallest 4-bit build, Ollama's 65 GB gpt-oss:120b, needs about 78 GB with the fifth added, which fits the reference machine but not 64 GB. Runtimes earn the full 25 from Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 10, since OpenAI ships one precision, MXFP4. Terms earn 15 for Apache 2.0 with a policy that asks only for lawful use. Download earns 10, with no gate or account. Hosted earns 5, since 11 inference providers serve it on Hugging Face even though OpenAI's own API doesn't. - MiniCPM-V 4.7 35B-A3B (90): Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90. ## Score by category | Category | Weight | gpt-oss-120b | MiniCPM-V 4.7 35B-A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 90 | MiniCPM-V 4.7 35B-A3B +15 | | Risk | 20% (20 this run) | 95 | 55 | gpt-oss-120b +40 | | Agent interface | 10% (10 this run) | 90 | 70 | gpt-oss-120b +20 | | Documentation and upkeep | 10% (10 this run) | 45 | 68 | MiniCPM-V 4.7 35B-A3B +23 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **70.2 · BB** | **61 · C** | | ## Facts side by side | Fact | gpt-oss-120b | 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-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. **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-120b 1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits 2. 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 3. 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 4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise 5. Set `Reasoning: low` in the system message for short tasks, medium is the default 6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users 7. Expect one tool call per turn and loop, don't wait for parallel calls ### 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-120b or MiniCPM-V 4.7 35B-A3B? gpt-oss-120b scores 70.2 (BB) 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 reach and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-minicpm-v-4-7-35b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-120b", "b": "minicpm-v-4-7-35b-a3b"}`. 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