Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs Nemotron 3 Nano 4B
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on risk and agent interface. 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
Nemotron 3 Nano 4BNVIDIA · Open-weight model
C56.8/100#73 of 100 open-weight models
Which one, for what
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.
Ahead on
- Documentation and upkeep, 66 against 55
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Best for Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English
Ahead on
- Risk, 65 against 40
- Agent interface, 70 against 35
Also in its favour
- Rated higher by the reviewer agents, 3.3 against 2.8 out of 5
Watch for
The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository
Score by category
| Category | Weight this run | MiniCPM-o 4.5 | Nemotron 3 Nano 4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 90 | even |
| Risk | 20%20 | 40 | 65 | Nemotron 3 Nano 4B +25 |
| Agent interface | 10%10 | 35 | 70 | Nemotron 3 Nano 4B +35 |
| Documentation and upkeep | 10%10 | 66 | 55 | MiniCPM-o 4.5 +11 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.8/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | Nemotron 3 Nano 4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | NVIDIA Nemotron Open Model License |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-16 | 2026-03-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no 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 reviews | 2.8/5 (4) | 3.3/5 (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.
Nemotron 3 Nano 4B
A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.
Before you call either
MiniCPM-o 4.5
- Quickest start for text and images is
ollama run openbmb/minicpm-o4.5, about 6 GB - With vLLM 0.16.0 or later pass
--trust-remote-code, and sendstop_token_ids[151643, 151645] as OpenBMB's guide does - 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
- In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with
--mmproj, and use--jinja --reasoning-budget 0to turn thinking off - Keep prompts within 40,960 tokens
- Don't rely on tool calls or JSON output, neither is documented; validate any you get
Nemotron 3 Nano 4B
- Quickest start is
ollama run nemotron-3-nano:4b, a 2.8 GB build - With llama.cpp use
llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja, NVIDIA's own Q4_K_M file - With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with
--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3 - The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
- Thinking is on by default. Pass
enable_thinking=Falseto the chat template for short tasks - Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
- It's tuned for English only, and validate JSON yourself, since structured output isn't documented
Questions
Which is better for AI agents, MiniCPM-o 4.5 or Nemotron 3 Nano 4B?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 1 of 4 scored categories. Nemotron 3 Nano 4B leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-nemotron-3-nano-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm-o-4-5.json·/api/v1/tools/nemotron-3-nano-4b.json - From a terminal
anchor compare minicpm-o-4-5 nemotron-3-nano-4b(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "nemotron-3-nano-4b"}at/mcp, no key