TimeSage-MT - Code Correctness: leaderboard
TimeSage-MT (2026) evaluates LLM agents in multi-turn time-series analysis dialogues with verifiable, code-derived answers, here under its Code-Enabled Reasoning setting in which the model writes Python that a sandbox executes. This board is the code correctness dimension in percent: half credit for code that runs cleanly and half for the share of reference output numbers it reproduces; higher is better.
Metric: Code correctness (%): half for code that executes cleanly, half for the share of reference output numbers recovered within tolerance, averaged over the applicable tasks; Code-Enabled Reasoning setting (the model writes Python, a sandbox runs it, the model answers from stdout; 240 multi-turn time-series analysis tasks over 8 domains), temperature 0, reasoning effort high where supported and medium for GPT-5.3-Codex; higher is better. Source: arxiv.org. Saturation forecast: Around February 2027. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Sonnet 4.6 | 78.51 | #161 |
| 2 | GLM-5.1 | 78.06 | #179 |
| 3 | GPT-5.3 Codex | 77.79 | #116 |
| 4 | Qwen 3.5 122B A10B | 72.89 | #331 |
| 5 | Gemini 3 Flash (Preview) | 72.39 | #144 |
| 6 | MiniMax-M2.7 | 71.74 | #518 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=timesage-mt-code-correctness · How It Works · Data refreshed daily, snapshot 2026-11-13.