Head to head · Open-weight models you run yourself · October 2026 research run
Kimi K3 vs MiniCPM-o 4.5
Kimi K3 scores 62.4 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep. Both do open-weight models you run yourself.
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Kimi K3Moonshot AI · Open-weight model
B62.4/100#29 of 100 open-weight models
MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Which one, for what
Kimi K3 B
Best for Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work
Ahead on
- Risk, 65 against 40
- Agent interface, 65 against 35
Watch for
1,561 GB of files at its smallest publisher build, far beyond any single machine
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
- Reach, 90 against 50
- Documentation and upkeep, 66 against 41
Also in its favour
- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Score by category
| Category | Weight this run | Kimi K3 | MiniCPM-o 4.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 90 | MiniCPM-o 4.5 +40 |
| Risk | 20%20 | 65 | 40 | Kimi K3 +25 |
| Agent interface | 10%10 | 65 | 35 | Kimi K3 +30 |
| Documentation and upkeep | 10%10 | 41 | 66 | MiniCPM-o 4.5 +25 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 62.4/100 | C 61/100 |
Facts side by side
| Fact | Kimi K3 | MiniCPM-o 4.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | OpenBMB |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Kimi K3 License (modified MIT) | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-02 | 2026-10-16 |
| 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 | 2.3/5 (4) | 2.8/5 (4) |
Verdicts
Kimi K3
Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.
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.
Before you call either
Kimi K3
- Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000
- Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect
- Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable
- Thinking is always on; set reasoning_effort to low or high when max is too slow
- Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones
- Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear
- Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page
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
Questions
Which is better for AI agents, Kimi K3 or MiniCPM-o 4.5?
Kimi K3 scores 62.4 (B) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 2 of 4 scored categories. MiniCPM-o 4.5 leads on reach and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/kimi-k3-vs-minicpm-o-4-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/kimi-k3.json·/api/v1/tools/minicpm-o-4-5.json - From a terminal
anchor compare kimi-k3 minicpm-o-4-5(the CLI) - Over MCP
compare_tools {"a": "kimi-k3", "b": "minicpm-o-4-5"}at/mcp, no key