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Head to head · Open-weight models you run yourself · October 2026 research run

MiniCPM-V 4.7 35B-A3B vs Qwen3-4B-Instruct-2507

MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 1 of 4 scored categories. Qwen3-4B-Instruct-2507 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 6359 open weights comparisons

MiniCPM-V 4.7 35B-A3BOpenBMB · Open-weight model C61/100#77 of 160 open-weight models Qwen3-4B-Instruct-2507Alibaba (Qwen team) · Open-weight model C54.5/100#130 of 160 open-weight models

Which one, for what

MiniCPM-V 4.7 35B-A3B C

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 26

Watch for

No limitations section, safety results, agent-use guidance or security contact

Qwen3-4B-Instruct-2507 C

Best for Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on

  • Agent interface, 75 against 70

Watch for

No safety results, prompt-injection guidance or security contact

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 +42

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.

Qwen3-4B-Instruct-2507 26

Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files last changed on 17 September 2025 (Hub lastModified on 4 November), more than a year ago. Responsiveness 0, counted by script on 4 November, Qwen answered none of the 20 questions and reports. Version policy 5, the older Qwen3-4B stays in its own repository without a note. File health 5, the files agree with the card in substance but reports wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26.

Agent interface Qwen3-4B-Instruct-2507 +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.

Qwen3-4B-Instruct-2507 75

Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.

Score by category

CategoryWeight this runMiniCPM-V 4.7 35B-A3BQwen3-4B-Instruct-2507Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259090even
Risk20%205555even
Agent interface10%107075Qwen3-4B-Instruct-2507 +5
Documentation and upkeep10%106826MiniCPM-V 4.7 35B-A3B +42
Negative events≤1500
TotalC 61/100C 54.5/100

Facts side by side

FactMiniCPM-V 4.7 35B-A3BQwen3-4B-Instruct-2507
KindOpen-weight modelOpen-weight model
VendorOpenBMBAlibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-032025-09-17
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3/5 (4)

How the scores have moved

MiniCPM-V 4.7 35B-A3B

No score change since it was listed.

Qwen3-4B-Instruct-2507

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.

Qwen3-4B-Instruct-2507

A small dense instruct model for instruction following, short tool-calling jobs and long documents, with no thinking step. Its 4-bit builds need about 3 GB under Apache 2.0, but no live inference provider was found for it on 4 November 2026. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published.

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

Qwen3-4B-Instruct-2507

  1. Quickest start is ollama run qwen3:4b-instruct, 2.5 GB. Plain qwen3:4b is the Thinking-2507 edition, not this one
  2. On our reference machine use BF16 with vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes
  3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
  4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
  5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
  6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
  7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

Questions

Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or Qwen3-4B-Instruct-2507?

MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 1 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on agent interface.

Other comparisons with MiniCPM-V 4.7 35B-A3B or Qwen3-4B-Instruct-2507

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