Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-V 4.7 35B-A3B vs Qwen-AgentWorld-35B-A3B
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 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
- Reach, 90 against 55
- Agent interface, 70 against 50
- Documentation and upkeep, 68 against 41
Watch for
No limitations section, safety results, agent-use guidance or security contact
Best for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Not a general assistant, and the card doesn't describe using it to call tools
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.
Reach MiniCPM-V 4.7 35B-A3B +35
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.
Qwen-AgentWorld-35B-A3B 55
Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.
Documentation and upkeep MiniCPM-V 4.7 35B-A3B +27
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.
Qwen-AgentWorld-35B-A3B 41
Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.
Agent interface MiniCPM-V 4.7 35B-A3B +20
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.
Qwen-AgentWorld-35B-A3B 50
Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.
Score by category
| Category | Weight this run | MiniCPM-V 4.7 35B-A3B | Qwen-AgentWorld-35B-A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 55 | MiniCPM-V 4.7 35B-A3B +35 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 50 | MiniCPM-V 4.7 35B-A3B +20 |
| Documentation and upkeep | 10%10 | 68 | 41 | MiniCPM-V 4.7 35B-A3B +27 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 52.1/100 |
Facts side by side
| Fact | MiniCPM-V 4.7 35B-A3B | Qwen-AgentWorld-35B-A3B |
|---|---|---|
| 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 | 2026-06-25 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
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.
Qwen-AgentWorld-35B-A3B
A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.
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
Qwen-AgentWorld-35B-A3B
- Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation
- With vLLM pass
--language-model-only, or startup fails on the missing vision weights - Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends
- Keep at least 128K of context, since the simulation depends on the history
- Thinking is on by default and comes back through the qwen3 reasoning parser
- A simulated observation is a prediction. Check anything that matters against the real environment
Questions
Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen-AgentWorld-35B-A3B?
MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 3 of 4 scored categories.
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- Qwen-AgentWorld-35B-A3B vs Step 3.7 Flash
- Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen-AgentWorld-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen-AgentWorld-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Qwen-AgentWorld-35B-A3B vs Baidu Unlimited-OCR-2
- Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-2B
- Qwen-AgentWorld-35B-A3B vs WeVisOne-VL-4B
- Qwen-AgentWorld-35B-A3B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/minicpm-v-4-7-35b-a3b-vs-qwen-agentworld-35b-a3b.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/qwen-agentworld-35b-a3b.json - From a terminal
anchor compare minicpm-v-4-7-35b-a3b qwen-agentworld-35b-a3b(the CLI) - Over MCP
compare_tools {"a": "minicpm-v-4-7-35b-a3b", "b": "qwen-agentworld-35b-a3b"}at/mcp, no key