# MiniCPM-V 4.7 35B-A3B vs Qwen3.5-2B > MiniCPM-V 4.7 35B-A3B scores 61 (C) to Qwen3.5-2B's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-2b - Markdown: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-2b.md (~9,150 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 MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself. - 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 - Qwen3.5-2B: grade D, 50.5/100, rank #144 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-2b.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 ### 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 80 - Agent interface, 70 against 60 - Documentation and upkeep, 68 against 37 Watch for: No limitations section, safety results, agent-use guidance or security contact ### Qwen3.5-2B (D) Good for: Builders who want a cheap local helper or fine-tuning base for narrow tasks and image reading on a laptop, small GPU or CPU Watch for: No quantised build from Alibaba, and only one inference provider ## Why the edge ### Documentation and upkeep: MiniCPM-V 4.7 35B-A3B +31 - 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. - Qwen3.5-2B (37): The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops. Training data is a general description without a cut-off (5). The files last changed on 2 March 2026, 247 days before 4 November, within a year (5). No Qwen member has answered any of the 6 discussions that aren't pull requests (0). Qwen3-1.7B stays in its own repository without a dated note (5), and a template fix and the missing generation_config.json wait unanswered (5). 17+5+5+0+5+5 = 37. ### Reach: MiniCPM-V 4.7 35B-A3B +10 - 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. - Qwen3.5-2B (80): Memory earns the full 30, since LM Studio's Q4_K_M GGUF with its vision projector is 1.94 GB, about 2.3 GB with the fifth added (Ollama's Q4_K_M build was 1.9 GB on 21 October). Alibaba publishes no quantised 2B, so builds scores 0. Runtimes reach the 25 cap, with llama.cpp b11963 and Ollama through the build it pins re-read today. Apache 2.0 with nothing added gives terms 15 and the ungated download 10. Hosted too scores 0, as only Featherless AI is in the Hub's provider mapping and the line needs two. 30+25+0+15+10+0 = 80. ### Agent interface: MiniCPM-V 4.7 35B-A3B +10 - 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. - Qwen3.5-2B (60): The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the format to the template. Reasoning control scores 0, since the card shows how to turn thinking on but not where the reasoning comes back. 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. 20+10+20+0+0+10 = 60. ## Score by category | Category | Weight | MiniCPM-V 4.7 35B-A3B | Qwen3.5-2B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 80 | MiniCPM-V 4.7 35B-A3B +10 | | Risk | 20% (20 this run) | 55 | 55 | even | | Agent interface | 10% (10 this run) | 70 | 60 | MiniCPM-V 4.7 35B-A3B +10 | | Documentation and upkeep | 10% (10 this run) | 68 | 37 | MiniCPM-V 4.7 35B-A3B +31 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **50.5 · D** | | ## Facts side by side | Fact | MiniCPM-V 4.7 35B-A3B | Qwen3.5-2B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenBMB | Alibaba (Qwen team) | | 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 | 2026-11-03 | 2026-03-02 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 3/5 (4) | ## Verdicts **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. **Qwen3.5-2B.** A small vision-language model for prototypes, fine-tuning and narrow local steps such as tool calls, tagging or reading an image. It runs in about 2 GB at 4 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions. ## Before you call either ### 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 ### Qwen3.5-2B 1. Smallest start is `ollama run qwen3.5:2b-q4_K_M` (1.9 GB); plain `qwen3.5:2b` pulls the 2.7 GB Q8_0 build 2. Full precision `vllm serve Qwen/Qwen3.5-2B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder` 3. Thinking is off by default; if you turn it on with `"chat_template_kwargs": {"enable_thinking": true}`, stream and cut off runaway output, since the card warns of thinking loops, and add `--reasoning-parser qwen3` in vLLM or SGLang to split the reasoning out 4. There's no generation_config.json, so pass the card's sampling yourself, for example `temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0` for text without thinking 5. Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests 6. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3.5-2B? MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3.5-2B's 50.5 (D), and leads in 3 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-5-2b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minicpm-v-4-7-35b-a3b", "b": "qwen3-5-2b"}`. 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