Head to head · Open-weight models you run yourself · October 2026 research run
Kronos vs Qwen3-VL-4B-Instruct
Kronos scores 55.2 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 1 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 3260 open weights comparisons
KronosYu Shi (shiyu-coder, NeoQuasar), Tsinghua University · Open-weight model
C55.2/100#86 of 110 open-weight models
Qwen3-VL-4B-InstructAlibaba (Qwen team) · Open-weight model
D52.6/100#90 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
- Documentation and upkeep, 61 against 31
Watch for
Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build
Best for An agent with 4 GB or more to spare that needs a local model to read images, documents and video
Ahead on
- Reach, 95 against 55
- Risk, 55 against 50
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | Kronos | Qwen3-VL-4B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 95 | Qwen3-VL-4B-Instruct +40 |
| Risk | 20%20 | 50 | 55 | Qwen3-VL-4B-Instruct +5 |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 61 | 31 | Kronos +30 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 55.2/100 | D 52.6/100 |
Facts side by side
| Fact | Kronos | Qwen3-VL-4B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-04-13 | 2025-11-01 |
| 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.
Qwen3-VL-4B-Instruct
A small vision-language cousin for reading screenshots, documents, charts and video, running in about 3.5 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. It has no thinking mode, and Qwen hasn't answered repository questions or published safety work, so add your own guardrails before letting it drive a GUI.
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
Qwen3-VL-4B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:4b-instruct - In Ollama the plain
qwen3-vl:4btag is the Thinking edition, so name4b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
Questions
Which is better for AI agents, Kronos or Qwen3-VL-4B-Instruct?
Kronos scores 55.2 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 1 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach and risk.
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Machine-readable
- This page as Markdown
/compare/kronos-vs-qwen3-vl-4b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/kronos.json·/api/v1/tools/qwen3-vl-4b-instruct.json - From a terminal
anchor compare kronos qwen3-vl-4b-instruct(the CLI) - Over MCP
compare_tools {"a": "kronos", "b": "qwen3-vl-4b-instruct"}at/mcp, no key