> For the complete documentation index, see [llms.txt](https://docs.cherryai.com.cn/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cherryai.com.cn/docs/en-us/other/model_rank/agent.md).
# Agent Ranking
This page shows [Arena AI](https://arena.ai/leaderboard/agent) Daily snapshot of the top 30 on the leaderboard, for a quick view of recent model performance. For the full leaderboard, filters, and latest changes, see the official Arena AI website.
This leaderboard evaluates models’ overall performance on multi-turn tool use / agent tasks, covering dimensions such as net improvement rate, confirmation success rate, upvote/downvote ratio, controllability, Bash recovery rate, and tool hallucination rate.
> **Data update time**: 2026-09-20 12:49:40 UTC / 2026-09-20 20:49:40 CST (Beijing time)
{% hint style="info" %}
The leaderboard reflects specific evaluations and user voting preferences, and does not necessarily mean the model will be better for your task. When choosing a model, also consider price, speed, context, tool calling, privacy, and regional availability.
{% endhint %}
## Top 30
| Ranking | Rank range | Model | Net Improvement Rate | Confirmation Success Rate | Upvote/Downvote Ratio | Controllability | Bash Recovery Rate | Tool Hallucination Rate | Number of Sessions |
| ------: | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | -------------------- | ------------------------- | --------------------- | --------------- | ------------------ | ----------------------- | -----------------: |
| 1 | 1-2 | Claude Fable 5.1 (Max) [1](https://www.anthropic.com/claude-fable-and-mythos-5-1) | 13.71%±1.72% | 19.83%±2.75% | 31.83%±6.86% | 3.88%±3.61% | 12.62%±0.76% | 0.37%±0.03% | 13,320 |
| 2 | 1-6 | GPT 6 Astra (Max) [1](https://openai.com/index/gpt-6-astra/) | 11.54%±2.10% | 17.70%±3.26% | 32.79%±8.37% | 0.47%±4.67% | 7.28%±1.11% | 0.37%±0.03% | 10,372 |
| 3 | 2-6 | Claude Opus 5 (High) [1](https://www.anthropic.com/news/claude-opus-5) | 10.25%±1.41% | 9.24%±2.94% | 18.66%±5.22% | 11.04%±2.75% | 11.98%±0.77% | 0.33%±0.04% | 24,794 |
| 4 | 2-6 | Claude Opus 5 (Max) [1](https://www.anthropic.com/news/claude-opus-5) | 10.16%±1.55% | 12.41%±3.04% | 18.15%±5.77% | 6.83%±3.05% | 13.08%±0.77% | 0.35%±0.04% | 19,934 |
| 5 | 2-8 | Claude Fable 5 (High) [1](https://www.anthropic.com/news/claude-fable-5-mythos-5) | 8.81%±1.25% | 5.97%±2.66% | 18.07%±4.46% | 10.60%±2.28% | 9.06%±1.28% | 0.37%±0.03% | 38,293 |
| 6 | 2-8 | Claude Opus 4.8 (High) [1](https://www.anthropic.com/news/claude-opus-4-8) | 8.19%±1.27% | 6.28%±2.47% | 16.23%±4.59% | 11.06%±2.22% | 7.49%±1.51% | 0.12%±0.31% | 39,731 |
| 7 | 5-13 | GPT 5.6 Sol (xHigh) [1](https://openai.com/index/gpt-5-6/) | 7.10%±1.28% | 3.74%±2.61% | 20.48%±4.62% | 6.62%±2.41% | 4.30%±0.93% | 0.37%±0.03% | 32,207 |
| 8 | 7-13 | Kimi K3 (Max) [1](https://platform.kimi.ai/docs/guide/kimi-k3-quickstart) | 6.22%±0.62% | 11.91%±1.28% | 11.79%±2.18% | 1.99%±1.28% | 5.03%±0.52% | 0.37%±0.03% | 108,615 |
| 9 | 5-16 | Claude Sonnet 5 (High) [1](https://www.anthropic.com/news/claude-sonnet-5) | 5.97%±1.62% | 2.88%±3.34% | 11.03%±5.77% | 8.39%±3.38% | 7.36%±1.06% | 0.18%±0.10% | 30,631 |
| 10 | 7-17 | GPT 5.5 (xHigh) [1](https://openai.com/index/introducing-gpt-5-5/) | 5.03%±0.92% | 0.61%±2.02% | 9.14%±3.18% | 6.61%±1.77% | 9.63%±1.25% | 0.37%±0.03% | 53,054 |
| 11 | 7-17 | Hy4 preview [1](https://hy.tencent.ai/research/hy4-preview) | 5.01%±1.02% | 9.85%±1.98% | 8.76%±3.77% | 1.23%±2.16% | 7.44%±0.56% | 0.23%±0.06% | 38,074 |
| 12 | 7-19 | Deepseek V4.1 Flash (Max) [1](https://www.deepseek.com/en/news/deepseek-v4-1-flash/) | 4.88%±1.33% | 13.35%±2.49% | 4.76%±4.56% | 1.63%±3.40% | 7.63%±0.71% | 0.29%±0.05% | 20,080 |
| 13 | 7-21 | Gemini 3.8 Flash (High) [1](https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/) | 4.71%±1.91% | 9.30%±3.70% | 13.34%±6.70% | 1.22%±4.83% | 1.91%±1.59% | 0.22%±0.19% | 12,534 |
| 14 | 9-18 | GLM 5.2 (Max) [1](https://huggingface.co/zai-org/GLM-5.2) | 4.37%±0.69% | 4.90%±1.50% | 9.76%±2.42% | 4.88%±1.31% | 1.93%±0.73% | 0.37%±0.03% | 76,766 |
| 15 | 9-20 | Muse Spark 1.3 (Max) [1](https://developer.meta.com/ai/models/muse-spark/) | 4.20%±0.86% | 10.31%±1.71% | 2.94%±2.94% | 1.47%±1.94% | 5.91%±0.77% | 0.37%±0.03% | 31,052 |
| 16 | 9-20 | DeepSeek V4 Pro (High) (0813) [1](https://api-docs.deepseek.com/updates/#date-2026-08-13) | 4.14%±0.79% | 6.81%±1.64% | 5.94%±2.80% | 1.37%±1.64% | 6.22%±0.59% | 0.36%±0.04% | 47,602 |
| 17 | 10-23 | Qwen3.8 Max [1](https://qwen.ai/blog?id=qwen3.8) | 3.30%±0.84% | 5.83%±1.82% | 4.20%±2.87% | 3.63%±1.75% | 4.11%±0.81% | 1.25%±0.38% | 31,489 |
| 18 | 13-23 | GLM 5.3 (Max) [1](https://z.ai/blog/glm-5.3) | 3.05%±0.62% | 8.48%±1.36% | 5.54%±2.12% | 1.86%±1.18% | 0.99%±0.58% | 0.37%±0.03% | 73,287 |
| 19 | 12-24 | Grok 4.5 [1](https://docs.x.ai/developers/models/grok-4.5) | 2.92%±0.99% | 1.88%±2.27% | 0.10%±3.33% | 5.86%±2.02% | 6.59%±1.22% | 0.37%±0.03% | 38,146 |
| 20 | 14-24 | GPT 5.5 [1](https://openai.com/index/introducing-gpt-5-5/) | 2.67%±0.81% | 2.01%±1.77% | 5.42%±2.73% | 3.20%±1.48% | 6.36%±1.19% | 0.37%±0.03% | 81,254 |
| 21 | 16-25 | Grok 4.6 (xHigh) [1](https://docs.x.ai/developers/models/grok-4.6) | 2.01%±1.05% | 2.55%±2.53% | 0.88%±3.38% | 4.74%±2.13% | 6.59%±0.91% | 0.37%±0.03% | 22,548 |
| 22 | 17-25 | Deepseek V4 Flash (High) (20260731) [1](https://api-docs.deepseek.com/updates/#date-2026-07-31) | 1.80%±0.73% | 3.25%±1.61% | 3.54%±2.53% | 0.41%±1.50% | 1.45%±0.60% | 0.34%±0.04% | 64,482 |
| 23 | 17-27 | GPT 5.6 Terra (xHigh) [1](https://openai.com/index/gpt-5-6/) | 1.44%±1.11% | 4.44%±2.54% | 3.28%±3.74% | 4.93%±2.18% | 3.05%±1.38% | 0.37%±0.03% | 20,301 |
| 24 | 19-26 | GPT 5.4 (High) [1](https://platform.openai.com/docs/models/gpt-5.4) | 1.26%±0.80% | 1.63%±1.83% | 0.41%±2.71% | 2.55%±1.59% | 4.62%±1.01% | 0.37%±0.03% | 80,406 |
| 25 | 21-26 | GLM 5.3 Flash [1](https://z.ai/blog/glm-5.3) | 1.15%±0.67% | 8.24%±1.47% | 1.61%±2.18% | 0.15%±1.40% | 4.63%±0.69% | 0.37%±0.03% | 43,164 |
| 26 | 23-31 | Qwen3.8 Flash Next [1](https://qwen.ai/blog?id=qwen3.8-flash-next) | 0.07%±0.76% | 8.92%±1.54% | 1.73%±2.61% | 4.70%±1.73% | 2.00%±0.48% | 0.87%±0.10% | 57,458 |
| 27 | 25-32 | GPT 5.6 Luna (xHigh) [1](https://openai.com/index/gpt-5-6/) | 0.44%±0.84% | 4.99%±1.99% | 3.39%±2.64% | 2.12%±1.78% | 3.70%±0.99% | 0.37%±0.03% | 29,186 |
| 28 | 26-32 | Gemini 3.7 Flash (High) [1](https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/) | 0.60%±0.65% | 2.88%±1.57% | 3.52%±2.03% | 0.28%±1.34% | 2.08%±0.70% | 0.03%±0.30% | 48,195 |
| 29 | 26-32 | Qwen 3.8 27B [1](https://huggingface.co/Qwen/Qwen3.8-27B) | 0.64%±0.72% | 4.31%±1.65% | 2.11%±2.36% | 0.08%±1.56% | 5.20%±0.69% | 0.30%±0.15% | 37,257 |
| 30 | 26-32 | DeepSeek V4 Pro [1](https://api-docs.deepseek.com/news/news260424) | 0.71%±0.93% | 1.71%±2.22% | 2.74%±3.16% | 2.13%±1.79% | 3.16%±0.85% | 0.15%±0.11% | 35,465 |
## How to read this table
* **Ranking / Rank range**: Relative ranking estimated by Arena AI based on agent task match voting; the ranking range indicates ranking fluctuations within the confidence interval.
* **Net Improvement Rate**: Net improvement over the baseline in multi-turn tool use / agent tasks.
* **Confirmation Success Rate**: The proportion of tasks that were successfully confirmed by users after completion (upvoted).
* **Upvote/Downvote Ratio**: The ratio of user 'upvotes' to 'downvotes' on answers, reflecting subjective satisfaction.
* **Controllability**: The model's ability to follow instructions and maintain stable outputs.
* **Bash Recovery Rate**: The proportion of cases where the model can recover after errors in Bash / command-line operations.
* **Tool Hallucination Rate**: The proportion of times the model hallucinates or calls nonexistent tools; lower is better.
* **Number of Sessions**：Sample size reference; with fewer samples, rankings are usually more volatile.
## Three things to confirm again when choosing a model
1. Whether the provider actually offers this model, and whether it is available in your region and account;
2. Whether the API price, rate limits, and context fit your task;
3. Run a small test with 3–5 real tasks; do not judge only by the overall leaderboard rank.
## Data source
Data comes from [Arena AI Official Agent Leaderboard](https://arena.ai/leaderboard/agent)，updated daily by GitHub Actions. For model pricing, licenses, and capabilities, refer to the official information from the model provider.
---
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