# Kronos vs ThinkingCap (Qwen3.8 27B) > ThinkingCap scores 61 (C) to Kronos's 55.2 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b - Markdown: https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b.md (~6,550 tokens) - Slim: https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-25 ThinkingCap (Qwen3.8 27B) 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 documentation and upkeep. Both do open-weight models you run yourself. - Kronos: grade C, 55.2/100, rank #86 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/kronos.md · JSON https://www.anchorterminal.com/api/v1/tools/kronos.json - ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #54 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Kronos (C) Good 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 45 - Documentation and upkeep, 61 against 53 Watch for: Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build ### ThinkingCap (Qwen3.8 27B) (C) Good for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac. Ahead on: - Reach, 75 against 55 - Agent interface, 70 against 60 Watch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue ## Score by category | Category | Weight | Kronos | ThinkingCap (Qwen3.8 27B) | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 75 | ThinkingCap (Qwen3.8 27B) +20 | | Risk | 20% (20 this run) | 50 | 45 | Kronos +5 | | Agent interface | 10% (10 this run) | 60 | 70 | ThinkingCap (Qwen3.8 27B) +10 | | Documentation and upkeep | 10% (10 this run) | 61 | 53 | Kronos +8 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **55.2 · C** | **61 · C** | | ## Facts side by side | Fact | Kronos | ThinkingCap (Qwen3.8 27B) | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | BottleCap AI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | PolyForm-Small-Business-1.0.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-04-13 | 2026-10-06 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | 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 | 2.5/5 (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. **ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description. ## Before you call either ### Kronos 1. Clone https://github.com/shiyu-coder/Kronos and `pip install -r requirements.txt`, then load `Kronos.from_pretrained("NeoQuasar/Kronos-small")` and `KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")` and wrap them in KronosPredictor 2. Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing 3. 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 4. 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 5. 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 6. Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system ### ThinkingCap (Qwen3.8 27B) 1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap 2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB 3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder` 4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error 5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop 6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor ## Questions ### Which is better for AI agents, Kronos or ThinkingCap (Qwen3.8 27B)? ThinkingCap (Qwen3.8 27B) 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 documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kronos", "b": "thinkingcap-qwen3-8-27b"}`. 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