Benchmarks / Microsoft STATE-Bench

Reported by Microsoft STATE-Bench

Microsoft STATE-Bench

Share of customer support tasks completed correctly, averaged over repeated runs.

Data older than 90 days: last updated 1 Jul 2026

Results dated
25 May 2026 to 29 May 2026
Results
5 configurations of 4 models
Unit
% of tasks
Licence
MIT License

Customer support task success (pass@1): GPT-5.4

4 results · % of tasks, higher is better. Choose a model to highlight it.Clear highlight

  1. 1 GPT-5.4 (high reasoning)OpenAI 57.6%
  2. 2 Claude Opus 4.7 (high reasoning)Anthropic 51.0%
  3. 3 DeepSeek-V4-Pro (0423)DeepSeek 45.6%
  4. 4 Kimi K2.6Moonshot AI 45.1%

Full results

STATE-Bench v0.7: customer support task success (pass@1), % of tasks, higher is better
#ModelCustomer support task success (pass@1)
% of tasks, higher is better
Price
$ per million tokens, in / out
1 GPT-5.4 (high reasoning)OpenAI · best of 2 settings
57.6%
$2.50 / $15
2 Claude Opus 4.7 (high reasoning)Anthropic
51.0%
$5 / $25
3 DeepSeek-V4-Pro (0423)DeepSeek
45.6%
$1.42 / $2.83
4 Kimi K2.6Moonshot AI
45.1%
$0.95 / $4

Swipe the table sideways for more columns.

Ranks follow the score as shown, so equal numbers share a rank. Each model is shown at its best setting; show every setting. Results as published by Microsoft STATE-Bench; we do not re-run them.

Progress over time

Customer support task success (pass@1), plotted at each model's release date, higher is better. Each model counts once, at its best setting. Scores are today's, not launch day's. Hover or tap a mark for its model.

Best score so far

Every model at its release date. The line moves each time a new release beats every earlier one, and each new best is named where there is room.

40%45%50%55%60% Apr 2026JulOct GPT-5.4

Best score by lab

Each lab's best so far, stepping whenever one of its releases beats its own earlier models.

OpenAIAnthropicDeepSeekMoonshot AI

40%45%50%55%60% Apr 2026JulOct OpenAIAnthropicDeepSeekMoonshot AI

What it measures

Share of customer support tasks completed correctly, averaged over repeated runs.

What it does not measure

Not the model alone: it runs inside STATE-Bench's agent loop against simulated users, and versions of the benchmark are not comparable.

4 results from Microsoft STATE-Bench not ranked here · show why

We rank a result only when we can tie it to a specific model you can use. These are left out:

  • Agent systems with memory, not models on their own: 4

STATE-Bench by Microsoft and STATE-Bench contributors, MIT License.