TimeSage-MT - Numerical Accuracy: 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 numerical accuracy dimension: a rule-based scorer compares the values the agent states with gold targets computed by reference code, normalized to percent; higher is better.
Metric: Numerical accuracy (%): typed rule-based scorer comparing stated values with code-derived gold targets (MASE threshold, F1, 1-NRMSE, ARI and similar, normalized to 0-100), 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 2028. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GLM-5.1 | 18.17 | #179 |
| 2 | GPT-5.3 Codex | 17.7 | #116 |
| 3 | Claude Sonnet 4.6 | 17.68 | #161 |
| 4 | Qwen 3.5 122B A10B | 15.75 | #331 |
| 5 | MiniMax-M2.7 | 15.54 | #518 |
| 6 | Gemini 3 Flash (Preview) | 15.25 | #144 |
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-numerical-accuracy · How It Works · Data refreshed daily, snapshot 2026-11-13.