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

MiniCPM-o 4.5 vs MiniCPM5-2B

MiniCPM-o 4.5 and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

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MiniCPM-o 4.5OpenBMB · Open-weight model C61/100#42 of 100 open-weight models MiniCPM5-2BOpenBMB · Open-weight model C60.2/100#56 of 100 open-weight models

Which one, for what

MiniCPM-o 4.5 C

Best for Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.

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

Watch for

Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni

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

  • Risk, 55 against 40
  • Agent interface, 75 against 35
  • Documentation and upkeep, 97 against 66

Watch for

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

Score by category

CategoryWeight this runMiniCPM-o 4.5MiniCPM5-2BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259090even
Risk20%204055MiniCPM5-2B +15
Agent interface10%103575MiniCPM5-2B +40
Documentation and upkeep10%106697MiniCPM5-2B +31
Negative events≤1500
TotalC 61/100C 60.2/100

Facts side by side

FactMiniCPM-o 4.5MiniCPM5-2B
KindOpen-weight modelOpen-weight model
VendorOpenBMBOpenBMB
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cardsApache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-162026-09-29
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews2.8/5 (4)3/5 (4)

Verdicts

MiniCPM-o 4.5

A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.

MiniCPM5-2B

A tiny dense cousin for local tool calling and long context, with published training data and an active publisher. OpenBMB's own 4-bit builds need about 2 GB, so it runs on a laptop or phone in vLLM, SGLang, llama.cpp, Ollama or Transformers. The main risks are always-on thinking, no safety results and use conditions beyond Apache 2.0.

Before you call either

MiniCPM-o 4.5

  1. Quickest start for text and images is ollama run openbmb/minicpm-o4.5, about 6 GB
  2. With vLLM 0.16.0 or later pass --trust-remote-code, and send stop_token_ids [151643, 151645] as OpenBMB's guide does
  3. vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni
  4. In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with --mmproj, and use --jinja --reasoning-budget 0 to turn thinking off
  5. Keep prompts within 40,960 tokens
  6. Don't rely on tool calls or JSON output, neither is documented; validate any you get

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-o 4.5 or MiniCPM5-2B?

MiniCPM-o 4.5 and MiniCPM5-2B score within a point of each other on agent readiness, 61 (C) and 60.2 (C). MiniCPM5-2B leads on risk, agent interface and documentation and upkeep.

Other comparisons with MiniCPM-o 4.5 or MiniCPM5-2B

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