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

MiniCPM-V 4.7 35B-A3B vs MiniCPM5-2B

MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 0 of 4 scored categories. MiniCPM5-2B leads on agent interface and documentation and upkeep. 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

MiniCPM-V 4.7 35B-A3BOpenBMB · Open-weight model C61/100#69 of 135 open-weight models MiniCPM5-2BOpenBMB · Open-weight model C59.7/100#78 of 135 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

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

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

MiniCPM5-2B C

Best for On-device and laptop agents that want a 2 GB tool-calling helper with long context and open training data

Ahead on

  • Agent interface, 75 against 70
  • Documentation and upkeep, 92 against 68

Watch for

OpenBMB documents only a Think mode for the 2B, so every answer starts with reasoning

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 MiniCPM5-2B +24

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.

MiniCPM5-2B 92

Model card 17, everything but a knowledge cut-off. Training data 20, all eight training datasets are published on Hugging Face and named per stage with sizes. Last change 15, a template fix on 29 September 2026, 36 days ago, with nothing newer in OpenBMB's MiniCPM5 repositories on Hugging Face (builds last changed 12 September) or its GitHub repository (21 September). Responsiveness 20, OpenBMB members answered 16 of the 23 questions in the discussions, the newest reply on 29 September. Version policy 10, earlier models and checkpoints sit in their own repositories and the GitHub changelog dates each release. File health 10, config, tokenizer, template and card agree and no broken-file report waited on 21 October, though OpenBMB's GGUF, MLX and GPTQ repositories still carry the template from before the 29 September fix.

Agent interface MiniCPM5-2B +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.

MiniCPM5-2B 75

Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card and OpenBMB's cookbooks document the XML call format, the tools request and the minicpm5 parser in SGLang and vLLM. Trained tool calling 20, agent SFT on 500K samples and a template that renders several calls in one turn. Structured output 0, nothing documents asking for JSON. Reasoning control 0, a judgement call, because OpenBMB's own cookbooks call Think the only supported mode for the 2B even though the template accepts enable_thinking false. Context 10, 131,072 tokens native with no rope scaling.

Score by category

CategoryWeight this runMiniCPM-V 4.7 35B-A3BMiniCPM5-2BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259090even
Risk20%205555even
Agent interface10%107075MiniCPM5-2B +5
Documentation and upkeep10%106892MiniCPM5-2B +24
Negative events≤1500
TotalC 61/100C 59.7/100

Facts side by side

FactMiniCPM-V 4.7 35B-A3BMiniCPM5-2B
KindOpen-weight modelOpen-weight model
VendorOpenBMBOpenBMB
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-032026-09-29
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.

MiniCPM5-2B

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.

MiniCPM5-2B

A 2B dense model for local tool calling and long context, with published training data. OpenBMB's own 4-bit builds are under 1.6 GB, and the model loads in vLLM, SGLang, llama.cpp, Ollama and Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.

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

MiniCPM5-2B

  1. Smallest start is ollama run openbmb/minicpm5-2b (1.6 GB, q4_k_m)
  2. On vLLM 0.29.0 or later run vllm serve openbmb/MiniCPM5-2B --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser minicpm5, or tool calls come back as XML text
  3. On SGLang 0.5.16 or later add --tool-call-parser minicpm5 --reasoning-parser qwen3; SGLang is OpenBMB's recommended backend for tool calls
  4. Tool calls are XML, <function name=...><param name=...>, not Hermes JSON
  5. Sample with temperature 1.0, top_p 0.95 and min_p 0.0; llama.cpp's default min_p 0.05 can loop, and repetition_penalty 1.05 helps if it does
  6. Expect a <think> block before every answer and leave room in max_tokens; enable_thinking false is accepted by the template but OpenBMB doesn't support it for the 2B
  7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented
  8. Use the main repository's template; the GGUF, MLX and GPTQ copies predate the 29 September fix

Questions

Which is better for AI agents, MiniCPM-V 4.7 35B-A3B or MiniCPM5-2B?

MiniCPM-V 4.7 35B-A3B scores 61 (C) on agent readiness against MiniCPM5-2B's 59.7 (C), and leads in 0 of 4 scored categories. MiniCPM5-2B leads on agent interface and documentation and upkeep.

Other comparisons with MiniCPM-V 4.7 35B-A3B or MiniCPM5-2B

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