# Kronos vs Spark-X2.6-4B

> Spark-X2.6-4B 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-spark-x2-6-4b
- Markdown: https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.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

Spark-X2.6-4B 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.

- 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
- Spark-X2.6-4B: grade C, 61/100, rank #52 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.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:
- Documentation and upkeep, 61 against 41

Watch for: Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build

### Spark-X2.6-4B (C)

Good for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.

Ahead on:
- Reach, 85 against 55
- Agent interface, 70 against 60

Watch for: No safety results and no agent or prompt-injection guidance


## Score by category

| Category | Weight | Kronos | Spark-X2.6-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 85 | Spark-X2.6-4B +30 |
| Risk | 20% (20 this run) | 50 | 50 | even |
| Agent interface | 10% (10 this run) | 60 | 70 | Spark-X2.6-4B +10 |
| Documentation and upkeep | 10% (10 this run) | 61 | 41 | Kronos +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.2 · C** | **61 · C** | |

## Facts side by side

| Fact | Kronos | Spark-X2.6-4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | SparkLLM (XHToken) |
| 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 | 2026-10-20 |
| 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 | 3/5 (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.

**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.

## 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

### Spark-X2.6-4B

1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)
2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs
3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low
4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object
7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights

## Questions

### Which is better for AI agents, Kronos or Spark-X2.6-4B?

Spark-X2.6-4B 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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kronos", "b": "spark-x2-6-4b"}`. From a terminal: `anchor compare kronos spark-x2-6-4b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kronos.json and https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json

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