Head to head · Open-weight models you run yourself · October 2026 research run
Kronos vs Ornith-1.5-122B
Ornith-1.5-122B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
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 36
Watch for
Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build
Best for Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required
Ahead on
- Reach, 75 against 55
- Agent interface, 85 against 60
Watch for
No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Agent interface Ornith-1.5-122B +25
Kronos 60
Read for a forecasting model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. The card documents the DataFrame and timestamps it takes and the DataFrame it returns, and the publisher's KronosPredictor builds and parses them (25). It was trained on six K-line fields with calendar fields, one series per forecast (10). Output is a typed DataFrame indexed by the requested timestamps, the mean of the sampled paths (15). The input format is given only as code (10). Reasoning control and context score 0, with no reasoning and a 512-bar context (2,048 for Kronos-mini).
Ornith-1.5-122B 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.
Documentation and upkeep Kronos +25
Kronos 61
Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist.
Ornith-1.5-122B 36
Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.
Reach Ornith-1.5-122B +20
Kronos 55
Memory 30, the publisher's Kronos-mini with its tokeniser is 0.03 GB and even Kronos-base is 0.43 GB in FP32. Runtimes 0, it runs only through the publisher's own PyTorch package from GitHub, and none of the six named runtimes supports it. Builds 0, FP32 safetensors only. Terms 15, MIT with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider or publisher API serves it (provider mapping still empty on 4 November).
Ornith-1.5-122B 75
Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.
Score by category
| Category | Weight this run | Kronos | Ornith-1.5-122B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 75 | Ornith-1.5-122B +20 |
| Risk | 20%20 | 50 | 50 | even |
| Agent interface | 10%10 | 60 | 85 | Ornith-1.5-122B +25 |
| Documentation and upkeep | 10%10 | 61 | 36 | Kronos +25 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 55.2/100 | C 61/100 |
Facts side by side
| Fact | Kronos | Ornith-1.5-122B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-04-13 | 2026-10-24 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Popularity | 43k stars | none |
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.
Ornith-1.5-122B
The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.
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
Ornith-1.5-122B
- Start it with
ollama run ornith-1.5:122b-q4_K_M, orllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144 - Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model
- In vLLM pass
--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3, and in SGLang--tool-call-parser qwen3_coder --reasoning-parser qwen3, so tool calls and reasoning come back parsed - Thinking is on by default. Send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
- Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends
- Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, Kronos or Ornith-1.5-122B?
Ornith-1.5-122B scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 2 of 4 scored categories. Kronos leads on documentation and upkeep.
Other comparisons with Kronos or Ornith-1.5-122B
- Agents-A1-4B vs Kronos
- Agents-A1-4B vs Ornith-1.5-122B
- Apertus v1.5 8B vs Kronos
- Apertus v1.5 8B vs Ornith-1.5-122B
- Apodex-1.1-mini vs Kronos
- Apodex-1.1-mini vs Ornith-1.5-122B
- Arete-27B vs Kronos
- Arete-27B vs Ornith-1.5-122B
- BigBang-v1 vs Kronos
- BigBang-v1 vs Ornith-1.5-122B
- Carbon-X vs Kronos
- Carbon-X vs Ornith-1.5-122B
- Cosmos-Reason2-2B vs Kronos
- Cosmos-Reason2-2B vs Ornith-1.5-122B
- Darwin-36B-Opus vs Kronos
- Darwin-36B-Opus vs Ornith-1.5-122B
- DeepSeek-V3.2 vs Kronos
- DeepSeek-V3.2 vs Ornith-1.5-122B
- DeepSeek V4.1 Flash vs Kronos
- DeepSeek V4.1 Flash vs Ornith-1.5-122B
- DeepSeek-V4-Flash-0731 vs Kronos
- DeepSeek-V4-Flash-0731 vs Ornith-1.5-122B
- DiffusionGemma 26B A4B IT vs Kronos
- DiffusionGemma 26B A4B IT vs Ornith-1.5-122B
- Gemma 4 12B IT vs Kronos
- Gemma 4 12B IT vs Ornith-1.5-122B
- Gemma 4 26B A4B IT vs Kronos
- Gemma 4 26B A4B IT vs Ornith-1.5-122B
- Gemma 4 31B IT vs Kronos
- Gemma 4 31B IT vs Ornith-1.5-122B
- Gemma 4 E2B IT vs Kronos
- Gemma 4 E2B IT vs Ornith-1.5-122B
- Gemma 4 E4B IT vs Kronos
- Gemma 4 E4B IT vs Ornith-1.5-122B
- GLM-4.5-Air vs Kronos
- GLM-4.5-Air vs Ornith-1.5-122B
- GLM-4.7-Flash vs Kronos
- GLM-4.7-Flash vs Ornith-1.5-122B
- GLM-5.3-Flash vs Kronos
- GLM-5.3-Flash vs Ornith-1.5-122B
- GLM-5.3 vs Kronos
- GLM-5.3 vs Ornith-1.5-122B
- gpt-oss-120b vs Kronos
- gpt-oss-120b vs Ornith-1.5-122B
- gpt-oss-20b vs Kronos
- gpt-oss-20b vs Ornith-1.5-122B
- Granite 4.2 30B vs Kronos
- Granite 4.2 30B vs Ornith-1.5-122B
- Granite 4.2 3B vs Kronos
- Granite 4.2 3B vs Ornith-1.5-122B
- Granite 4.2 8B vs Kronos
- Granite 4.2 8B vs Ornith-1.5-122B
- Hy-Memory-30B-A3B vs Kronos
- Hy-Memory-30B-A3B vs Ornith-1.5-122B
- Hy3 vs Kronos
- Hy3 vs Ornith-1.5-122B
- Inkling-Small vs Kronos
- Inkling-Small vs Ornith-1.5-122B
- Inkling vs Kronos
- Inkling vs Ornith-1.5-122B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kronos
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-122B
- Kimi K3.1 Pro vs Kronos
- Kimi K3.1 Pro vs Ornith-1.5-122B
- Kimi K3 vs Kronos
- Kimi K3 vs Ornith-1.5-122B
- Kolibri-1 vs Kronos
- Kolibri-1 vs Ornith-1.5-122B
- Kronos vs Laguna M.1
- Kronos vs Laguna S 2.1
- Kronos vs Laguna XS 2.1
- Kronos vs LBS-27B
- Kronos vs LFM2.5-2.6B
- Kronos vs LFM3-1.7B
- Kronos vs LFM3-Audio-2B
- Kronos vs LFM3-Omni-2B
- Kronos vs LFM3-VL-2B
- Kronos vs Ling-3.0-tiny
- Kronos vs Ling-3.1-flash-1019
- Kronos vs MiMo-V2.6 Distill Qwen 9B
- Kronos vs MiniCPM-o 4.5
- Kronos vs MiniCPM-V 4.7 1B
- Kronos vs MiniCPM-V 4.7 35B-A3B
- Kronos vs MiniCPM5-2B
- Kronos vs MiniMax-M2.5
- Kronos vs MiniMax-M2.7
- Kronos vs MiniMax-M3.1-Flash
- Kronos vs Mistral Large 4
- Kronos vs Muse Glimmer 30B
- Kronos vs Nanbeige4.3-33B
- Kronos vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Kronos vs Nemotron 3.5 Lightning 30B A3B
- Kronos vs Nemotron 3 Nano 30B A3B
- Kronos vs Nemotron 3 Nano 4B
- Kronos vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Kronos vs Nemotron 3 Super 120B A12B
- Kronos vs Nemotron 3 Ultra 550B A55B
- Kronos vs occamy-1.0
- Kronos vs Olmo 3 7B Think
- Kronos vs Ornith-1.5-35B-A3B
- Kronos vs Ornith-1.5-397B
- Kronos vs Ornith-1.5-9B
- Kronos vs Qwen-AgentWorld-35B-A3B
- Kronos vs Qwen3-30B-A3B-Instruct-2507
- Kronos vs Qwen3-4B-Instruct-2507
- Kronos vs Qwen3.5-0.8B
- Kronos vs Qwen3.5-122B-A10B
- Kronos vs Qwen3.5-2B
- Kronos vs Qwen3.5-397B-A17B
- Kronos vs Qwen3.5-4B
- Kronos vs Qwen3.5-9B
- Kronos vs Qwen3.6-27B
- Kronos vs Qwen3.6-35B-A3B
- Kronos vs Qwen3.8-27B
- Kronos vs Qwen3.8-Flash-Next
- Kronos vs Qwen3-Coder-30B-A3B-Instruct
- Kronos vs Qwen3-Coder-Next
- Kronos vs Qwen3-Omni-30B-A3B-Instruct
- Kronos vs Qwen3-VL-2B-Instruct
- Kronos vs Qwen3-VL-30B-A3B-Instruct
- Kronos vs Qwen3-VL-32B-Instruct
- Kronos vs Qwen3-VL-4B-Instruct
- Kronos vs Qwen3-VL-8B-Instruct
- Kronos vs Qwopus3.8-27B-Flash-V2
- Kronos vs Recursive Flint
- Kronos vs Ripple-65B Preview
- Kronos vs SmolLM3-3B
- Kronos vs Spark-X2.6-4B
- Kronos vs Swift 1.5 Qwen3.8-27B
- Kronos vs Swift 1.5 Qwen3.8-Flash-Next
- Kronos vs ThinkingCap (Qwen3.8 27B)
- Kronos vs Baidu Unlimited-OCR-2
- Laguna M.1 vs Ornith-1.5-122B
- Laguna S 2.1 vs Ornith-1.5-122B
- Laguna XS 2.1 vs Ornith-1.5-122B
- LBS-27B vs Ornith-1.5-122B
- LFM2.5-2.6B vs Ornith-1.5-122B
- LFM3-1.7B vs Ornith-1.5-122B
- LFM3-Audio-2B vs Ornith-1.5-122B
- LFM3-Omni-2B vs Ornith-1.5-122B
- LFM3-VL-2B vs Ornith-1.5-122B
- Ling-3.0-tiny vs Ornith-1.5-122B
- Ling-3.1-flash-1019 vs Ornith-1.5-122B
- MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-122B
- MiniCPM-o 4.5 vs Ornith-1.5-122B
- MiniCPM-V 4.7 1B vs Ornith-1.5-122B
- MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B
- MiniCPM5-2B vs Ornith-1.5-122B
- MiniMax-M2.5 vs Ornith-1.5-122B
- MiniMax-M2.7 vs Ornith-1.5-122B
- MiniMax-M3.1-Flash vs Ornith-1.5-122B
- Mistral Large 4 vs Ornith-1.5-122B
- Muse Glimmer 30B vs Ornith-1.5-122B
- Nanbeige4.3-33B vs Ornith-1.5-122B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-122B
- Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-122B
- Nemotron 3 Nano 30B A3B vs Ornith-1.5-122B
- Nemotron 3 Nano 4B vs Ornith-1.5-122B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-122B
- Nemotron 3 Super 120B A12B vs Ornith-1.5-122B
- Nemotron 3 Ultra 550B A55B vs Ornith-1.5-122B
- occamy-1.0 vs Ornith-1.5-122B
- Olmo 3 7B Think vs Ornith-1.5-122B
- Ornith-1.5-122B vs Ornith-1.5-35B-A3B
- Ornith-1.5-122B vs Ornith-1.5-397B
- Ornith-1.5-122B vs Ornith-1.5-9B
- Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B
- Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507
- Ornith-1.5-122B vs Qwen3-4B-Instruct-2507
- Ornith-1.5-122B vs Qwen3.5-0.8B
- Ornith-1.5-122B vs Qwen3.5-122B-A10B
- Ornith-1.5-122B vs Qwen3.5-2B
- Ornith-1.5-122B vs Qwen3.5-397B-A17B
- Ornith-1.5-122B vs Qwen3.5-4B
- Ornith-1.5-122B vs Qwen3.5-9B
- Ornith-1.5-122B vs Qwen3.6-27B
- Ornith-1.5-122B vs Qwen3.6-35B-A3B
- Ornith-1.5-122B vs Qwen3.8-27B
- Ornith-1.5-122B vs Qwen3.8-Flash-Next
- Ornith-1.5-122B vs Qwen3-Coder-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-Coder-Next
- Ornith-1.5-122B vs Qwen3-Omni-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-2B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-32B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-4B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2
- Ornith-1.5-122B vs Recursive Flint
- Ornith-1.5-122B vs Ripple-65B Preview
- Ornith-1.5-122B vs SmolLM3-3B
- Ornith-1.5-122B vs Spark-X2.6-4B
- Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next
- Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-122B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/kronos-vs-ornith-1-5-122b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/kronos.json·/api/v1/tools/ornith-1-5-122b.json - From a terminal
anchor compare kronos ornith-1-5-122b(the CLI) - Over MCP
compare_tools {"a": "kronos", "b": "ornith-1-5-122b"}at/mcp, no key