Head to head · Open-weight models you run yourself · October 2026 research run
Devstral Small 2 vs Kronos
Devstral Small 2 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 3 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 6359 open weights comparisons
Which one, for what
Best for Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac
Ahead on
- Reach, 90 against 55
- Risk, 60 against 50
- Agent interface, 65 against 60
Watch for
No limitations, safety results, languages or data on the card
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 46
Watch for
Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build
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.
Reach Devstral Small 2 +35
Devstral Small 2 90
Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.
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).
Documentation and upkeep Kronos +15
Devstral Small 2 46
Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.
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.
Risk Devstral Small 2 +10
Devstral Small 2 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.
Kronos 50
File safety 15, safetensors only, but the only documented loading path is the publisher's Python package in place of a runtime. Provenance 15, the files are in the NeoQuasar account the publisher's README links, with SHA-256 hashes. Safety evaluation 0, none published. Agent guidance 5, the README's general cautions that the fine-tuning pipeline isn't a production trading system. Licence risk 15, plain MIT; the broken licence link is charged to file health. Security programme 0, no reporting route found.
Agent interface Devstral Small 2 +5
Devstral Small 2 65
Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.
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).
Score by category
| Category | Weight this run | Devstral Small 2 | Kronos | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 55 | Devstral Small 2 +35 |
| Risk | 20%20 | 60 | 50 | Devstral Small 2 +10 |
| Agent interface | 10%10 | 65 | 60 | Devstral Small 2 +5 |
| Documentation and upkeep | 10%10 | 46 | 61 | Kronos +15 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 55.2/100 |
Facts side by side
| Fact | Devstral Small 2 | Kronos |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Mistral AI | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-15 | 2026-04-13 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Popularity | none | 43k stars |
Verdicts
Devstral Small 2
The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.
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.
Before you call either
Devstral Small 2
ollama run devstral-small-2is the quickest start, at 15 GB- On vLLM pass
--tool-call-parser mistral --enable-auto-tool-choice - Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it
- Use a low temperature. The repository's default is 0.15
- Plan for one tool call per turn
- The same model is on our Mistral API listing if you'd rather not host it
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
Questions
Which is better for AI agents, Devstral Small 2 or Kronos?
Devstral Small 2 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 3 of 4 scored categories. Kronos leads on documentation and upkeep.
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Machine-readable
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/compare/devstral-small-2-vs-kronos.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/devstral-small-2.json·/api/v1/tools/kronos.json - From a terminal
anchor compare devstral-small-2 kronos(the CLI) - Over MCP
compare_tools {"a": "devstral-small-2", "b": "kronos"}at/mcp, no key