Agentic benchmarks measure how reliably models complete multi-step tasks autonomously across browser, OS, and terminal environments. This page snapshots a composite ranking across WebArena, OSWorld, AgentBench, and TerminalBench as of June 2026.
June 2026 Composite Ranking
| Rank | Model | WebArena | OSWorld | TerminalBench | AgentBench |
|---|---|---|---|---|---|
| 1 | GPT-5.6 Pro | ~62% | ~52% | ~85% | ~78% |
| 2 | Claude Mythos 5 | ~61% | ~51% | ~84% | ~80% |
| 3 | Claude Opus 4.7 | ~58% | ~48% | ~82% | ~76% |
| 4 | GPT-5.6 | ~52% | ~44% | ~76% | ~70% |
| 5 | DeepSeek V4 Pro | ~48% | ~40% | ~72% | ~65% |
| 6 | Claude Sonnet 4.6 | ~46% | ~38% | ~70% | ~62% |
| 7 | Qwen 3.7 | ~43% | ~35% | ~66% | ~58% |
| 8 | GLM-6 | ~38% | ~30% | ~58% | ~52% |
| 9 | Llama 4.5 Maverick | ~32% | ~26% | ~52% | ~46% |
Key Takeaways
- Agentic benchmarks remain the most discriminating frontier-model evaluations; spreads are 2x wider than on MMLU-Pro.
- GPT-5.6 Pro leads narrowly on browser-based agentic tasks; Claude Mythos 5 leads on long-horizon AgentBench.
- OSWorld's published human baseline is 72.36 percent. The official OSWorld-Verified board already had entries above 80 percent by spring 2026, so the estimates in this table understate the leaders. See the OSWorld leaderboard.
- Open-weight DeepSeek V4 Pro sits within 10 to 14 percentage points of the top closed models.
Methodology
Scores compiled from vendor disclosures and the public leaderboards for WebArena, OSWorld, AgentBench, and TerminalBench. Agentic benchmark evaluation is highly sensitive to scaffolding choices; numbers should be treated as directional. Updated monthly.
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