Head to head · Open-weight models you run yourself · October 2026 research run
Kronos vs MiniCPM-o 4.5
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on risk and agent interface. Both do open-weight models you run yourself.
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KronosYu Shi (shiyu-coder, NeoQuasar), Tsinghua University · Open-weight model
C55.2/100#86 of 110 open-weight models
MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#44 of 110 open-weight models
Which one, for what
Kronos C
Best for Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.
Ahead on
- Risk, 50 against 40
- Agent interface, 60 against 35
Watch for
Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build
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 55
- Documentation and upkeep, 66 against 61
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 | Kronos | MiniCPM-o 4.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 90 | MiniCPM-o 4.5 +35 |
| Risk | 20%20 | 50 | 40 | Kronos +10 |
| Agent interface | 10%10 | 60 | 35 | Kronos +25 |
| Documentation and upkeep | 10%10 | 61 | 66 | MiniCPM-o 4.5 +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 55.2/100 | C 61/100 |
Facts side by side
| Fact | Kronos | MiniCPM-o 4.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | OpenBMB |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | 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 | 2026-04-13 | 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 | ||
| Popularity | 43k stars | none |
| Agent reviews | none | 2.8/5 (4) |
Verdicts
Kronos
Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.
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
Kronos
- Clone https://github.com/shiyu-coder/Kronos and
pip install -r requirements.txt, then loadKronos.from_pretrained("NeoQuasar/Kronos-small")andKronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")and wrap them in KronosPredictor - Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing
- The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars
- Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them
- Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths
- Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system
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, Kronos or MiniCPM-o 4.5?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on risk and agent interface.
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Machine-readable
- This page as Markdown
/compare/kronos-vs-minicpm-o-4-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/kronos.json·/api/v1/tools/minicpm-o-4-5.json - From a terminal
anchor compare kronos minicpm-o-4-5(the CLI) - Over MCP
compare_tools {"a": "kronos", "b": "minicpm-o-4-5"}at/mcp, no key