Agentic & Tool Use benchmark

# PostTrainBench leaderboard

> PostTrainBench results for 11 AI models, led by Claude Fable 5 at 41.8%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/posttrainbench
- Last updated: 2026-10-10
- Title: PostTrainBench Leaderboard (October 2026): Scores by Model

As of October 2026, Claude Fable 5 has the highest published PostTrainBench score on Noometry at 41.8%, out of 11 models with results.

Last verified October 10, 2026

## About PostTrainBench

A description with primary sources is being prepared for this benchmark.

- **Category:** [Agentic & Tool Use](https://noometry.com/best/agentic)
- **Introduced:** 2026
- **Unit:** Percent (random guessing ≈ 0%)
- **Official site:** [epoch.ai](https://epoch.ai/benchmarks)

## Top 11 models

Top models on PostTrainBench

1.  Claude Fable 5 41.8%
2.  GPT-5.6 Sol 36.2%
3.  Claude Opus 5 35%
4.  Claude Opus 4.8 33.8%
5.  Kimi K3 32%
6.  GLM-5.2 31.7%
7.  Claude Opus 4.7 28.6%
8.  GPT-5.5 27.2%
9.  Grok 4.5 23.4%
10.  Gemini 3.1 Pro Preview 22%
11.  GPT-5.4 19%
12.  0204060

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## All results

PostTrainBench results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [Claude Fable 5](https://noometry.com/models/claude-fable-5) | [Anthropic](https://noometry.com/providers/anthropic) | 41.8% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 2 | [GPT-5.6 Sol](https://noometry.com/models/gpt-5-6-sol) | [OpenAI](https://noometry.com/providers/openai) | 36.2% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 3 | [Claude Opus 5](https://noometry.com/models/claude-opus-5) | [Anthropic](https://noometry.com/providers/anthropic) | 35% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 4 | [Claude Opus 4.8](https://noometry.com/models/claude-opus-4-8) | [Anthropic](https://noometry.com/providers/anthropic) | 33.8% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 5 | [Kimi K3](https://noometry.com/models/kimi-k3) | [Moonshot AI](https://noometry.com/providers/moonshot) | 32% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 6 | [GLM-5.2](https://noometry.com/models/glm-5-2) | [Z.ai (Zhipu)](https://noometry.com/providers/zai) | 31.7% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 7 | [Claude Opus 4.7](https://noometry.com/models/claude-opus-4-7) | [Anthropic](https://noometry.com/providers/anthropic) | 28.6% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 8 | [GPT-5.5](https://noometry.com/models/gpt-5-5) | [OpenAI](https://noometry.com/providers/openai) | 27.2% | xhigh | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 9 | [Grok 4.5](https://noometry.com/models/grok-4-5) | [xAI](https://noometry.com/providers/xai) | 23.4% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 10 | [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 22% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 11 | [GPT-5.4](https://noometry.com/models/gpt-5-4) | [OpenAI](https://noometry.com/providers/openai) | 19% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

## Compare the leaders

-   [Claude Fable 5 vs GPT-5.6 Sol](https://noometry.com/compare/claude-fable-5-vs-gpt-5-6-sol)
-   [Claude Fable 5 vs Claude Opus 5](https://noometry.com/compare/claude-fable-5-vs-claude-opus-5)
-   [Claude Fable 5 vs Claude Opus 4.8](https://noometry.com/compare/claude-fable-5-vs-claude-opus-4-8)
-   [Claude Fable 5 vs Kimi K3](https://noometry.com/compare/claude-fable-5-vs-kimi-k3)
-   [GPT-5.6 Sol vs Claude Opus 5](https://noometry.com/compare/claude-opus-5-vs-gpt-5-6-sol)
-   [GPT-5.6 Sol vs Claude Opus 4.8](https://noometry.com/compare/claude-opus-4-8-vs-gpt-5-6-sol)

## Other agentic & tool use benchmarks

-   [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench)
-   [APEX-Agents](https://noometry.com/benchmarks/apex-agents)
-   [Berkeley Function Calling Leaderboard](https://noometry.com/benchmarks/bfcl)
-   [OSWorld 2.0](https://noometry.com/benchmarks/osworld-2)
-   [GDPval](https://noometry.com/benchmarks/gdpval)
-   [Remote Labor Index](https://noometry.com/benchmarks/remote-labor-index)
-   [TheAgentCompany](https://noometry.com/benchmarks/the-agent-company)
-   [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline)
-   [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking)
-   [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail)
-   [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom)
-   [Cybench](https://noometry.com/benchmarks/cybench)

## Frequently asked questions

### Which model has the highest PostTrainBench score?

As of October 2026, Claude Fable 5 has the highest published PostTrainBench score on Noometry at 41.8%, out of 11 models with results.

### What is the best open-weight model on PostTrainBench?

Kimi K3 has the highest PostTrainBench accuracy among open-weight models at 32%, ranking 5 of 11 overall.

### Cite this page

Noometry. (2026). PostTrainBench leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/posttrainbench

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/posttrainbench.md).