Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 35B-A3B vs Qwen3-Coder-30B-A3B-Instruct
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 1 of 4 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on agent interface. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best 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 31
Watch for
No limitations section, safety results, agent-use guidance or security contact
Qwen3-Coder-30B-A3B-Instruct C
Best for Coding agents and local coding tools that want an open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.
Ahead on
- Agent interface, 75 against 70
Watch for
No safety results, prompt-injection guidance or security contact, though it's meant to run commands
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Documentation and upkeep MiniCPM-V 4.7 35B-A3B +37
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-Coder-30B-A3B-Instruct 31
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, the blog describes the mix in general terms without a cut-off. Last change 5, config.json was corrected on 3 December 2025, 336 days before the 4 November 2026 check. Responsiveness 0, counted by script on 4 November, none of 26 discussions has a reply from Alibaba's Qwen staff, and the one Hub-flagged member who replied is the independent quantiser bartowski. Version policy 5, a first release. File health 5, the files now agree with the card, but reports about the parser and an attention error wait without a reply. 11 + 5 + 5 + 0 + 5 + 5 = 31.
Agent interface Qwen3-Coder-30B-A3B-Instruct +5
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-Coder-30B-A3B-Instruct 75
Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, the card shows how tools are declared, the README names the parser for SGLang and vLLM, and the repository ships it. Trained tool calling 20, the template renders several calls a turn and the training used agent reinforcement learning. Structured output 0, not documented. Reasoning control 0, the model has no thinking. Context 10, 262,144 tokens native, with the YaRN extension not counted. Template, parser and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 35B-A3B | Qwen3-Coder-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 90 | even |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 75 | Qwen3-Coder-30B-A3B-Instruct +5 |
| Documentation and upkeep | 10%10 | 68 | 31 | MiniCPM-V 4.7 35B-A3B +37 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 55.7/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 35B-A3B | Qwen3-Coder-30B-A3B-Instruct |
|---|---|---|
| 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 | 2025-12-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (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-Coder-30B-A3B-Instruct
A coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and several providers host it. It runs commands for agents with no safety results or prompt-injection guidance, and Alibaba hasn't answered its discussions.
Before you call either
MiniCPM-V 4.7 35B-A3B
- Start it with
llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja, orollama run openbmb/minicpm-v4.7:35b-a3b - For image input in llama.cpp add
mmproj-model-f16.gguf, or the 4x projector for more visual detail at a higher cost - The GGUF files at the repository root have thinking off. Use the files in
thinking/when you want the trace - With Transformers pass
enable_thinkingtoapply_chat_template, and passdownsample_modeto bothapply_chat_templateandgenerate - Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
- Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format
- Validate JSON output yourself, since structured output isn't documented
Qwen3-Coder-30B-A3B-Instruct
- Quickest start is
ollama run qwen3-coder:30b, 19 GB - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder, 61 GB of weights fits the GPU share - Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
- Pass tool-call arguments in the history as objects, the template iterates them as a mapping
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
- For code completion use the fill-in-the-middle tokens
<|fim_prefix|>,<|fim_suffix|>and<|fim_middle|>from Qwen's README - Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection
Questions
Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3-Coder-30B-A3B-Instruct?
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 1 of 4 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-v-4-7-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm-v-4-7-35b-a3b.json·/api/v1/tools/qwen3-coder-30b-a3b-instruct.json - From a terminal
anchor compare minicpm-v-4-7-35b-a3b qwen3-coder-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-35b-a3b", "b": "qwen3-coder-30b-a3b-instruct"}at/mcp, no key