# Best LLM for OpenHands > Updated Oct 22, 2026 · evidence through Oct 22, 2026. Synthesized from cited community evidence and open benchmarks, not vendor marketing. Canonical page: https://modelmatchbook.com/best/agents-openhands Claude Opus 5.5 ranks first for OpenHands as of October 22, 2026. Leads both Terminal-Bench 4.0 and LMArena's agentic arena, scoring 64.85% on reproducible terminal tasks and ranking #1 with a 20.9 score on human-preferred tool-use performance. Claude Sonnet 5.5 is the next-ranked alternative. It scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks. [1,2,3] ## 1. Claude Opus 5.5 https://modelmatchbook.com/models/anthropic/claude-opus-5.5-20260921 Leads both Terminal-Bench 4.0 and LMArena's agentic arena, scoring 64.85% on reproducible terminal tasks and ranking #1 with a 20.9 score on human-preferred tool-use performance. **Best when:** Deploy when human preference on multi-step tool use matters, as its #1 rank with score 20.9 on LMArena's agentic arena shows leading tool-use capabilities. [2] ## 2. Claude Sonnet 5.5 https://modelmatchbook.com/models/anthropic/claude-sonnet-5.5-20260928 Claude Sonnet 5.5 scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks. **Best when:** Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness. [3] ## 3. GPT-6.1 Sol https://modelmatchbook.com/models/openai/gpt-6.1-sol-20260929 GPT-6.1 Sol scores 58.18% ± 3.11 (95% interval) on Terminal-Bench 4.0 (#3 of 23) at max reasoning effort, using Codex, measuring a model-and-agent configuration on reproducible terminal tasks. **Best when:** Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness. [5] ## 4. GPT-6 Astra https://modelmatchbook.com/models/openai/gpt-6-astra-20260903 GPT-6 Astra ranks #2 of 48 on LMArena's agentic arena (score 14.7), measuring tool-use and multi-step task performance from human preference. **Best when:** Its strongest result for this task is on LMArena Agent, which measures tool calling and multi-step control across a long task. [6] ## 5. Claude Fable 5.1 https://modelmatchbook.com/models/anthropic/claude-fable-5.1-20260831 Ranks #5 on Terminal-Bench 4.0 at 57.88% and #3 on LMArena's agentic arena at 14.2, trading some terminal task accuracy for higher human preference on tool-use workflows. **Best when:** Select for tool-use workflows where human preference matters, as its #3 rank with score 14.2 on LMArena's agentic arena shows strong multi-step task performance. [7] ## 6. GLM 5.3 https://modelmatchbook.com/models/z-ai/glm-5.3-20260816 GLM 5.3 scores 41.82% ± 3.23 (95% interval) on Terminal-Bench 4.0 (#9 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks. **Best when:** Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness. [4] ## 7. GLM 5.3 Flash https://modelmatchbook.com/models/z-ai/glm-5.3-flash-20260826 GLM 5.3 Flash scores 35.76% ± 3.54 (95% interval) on Terminal-Bench 4.0 (#12 of 23) at none reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks. **Best when:** Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness. [8] ## 8. Grok 4.7 https://modelmatchbook.com/models/x-ai/grok-4.7-20260916 Grok 4.7 scores 37.58% ± 3.54 (95% interval) on Terminal-Bench 4.0 (#10 of 23) at xhigh reasoning effort, using Grok Build, measuring a model-and-agent configuration on reproducible terminal tasks. **Best when:** Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness. [9] ## FAQ **What is the best model for OpenHands?** Claude Opus 5.5 ranks first as of October 22, 2026. Leads both Terminal-Bench 4.0 and LMArena's agentic arena, scoring 64.85% on reproducible terminal tasks and ranking #1 with a 20.9 score on human-preferred tool-use performance. [1,2] **What is the best alternative to Claude Opus 5.5?** Claude Sonnet 5.5 ranks second. Claude Sonnet 5.5 scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks. [3] **Which open-weight model ranks highest for OpenHands?** GLM 5.3 is the highest-ranked open-weight model, at #6. GLM 5.3 scores 41.82% ± 3.23 (95% interval) on Terminal-Bench 4.0 (#9 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks. [4] ## Sources 1. "Within the 23 catalog models matched to this evaluation: Scores 64.85% ± 3.11 (95% interval) on Terminal-Bench 4.0 (#1 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks." (Terminal-Bench, https://www.tbench.ai/) 2. "Within the 48 catalog models matched to this evaluation: Ranks #1 of 48 on LMArena's agentic arena (score 20.9), measuring tool-use and multi-step task performance from human preference." (LMArena agentic arena, https://lmarena.ai/leaderboard) 3. "Within the 23 catalog models matched to this evaluation: Scores 61.82% ± 2.94 (95% interval) on Terminal-Bench 4.0 (#2 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks." (Terminal-Bench, https://www.tbench.ai/) 4. "Within the 23 catalog models matched to this evaluation: Scores 41.82% ± 3.23 (95% interval) on Terminal-Bench 4.0 (#9 of 23) at max reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks." (Terminal-Bench, https://www.tbench.ai/) 5. "Within the 23 catalog models matched to this evaluation: Scores 58.18% ± 3.11 (95% interval) on Terminal-Bench 4.0 (#3 of 23) at max reasoning effort, using Codex, measuring a model-and-agent configuration on reproducible terminal tasks." (Terminal-Bench, https://www.tbench.ai/) 6. "Within the 48 catalog models matched to this evaluation: Ranks #2 of 48 on LMArena's agentic arena (score 14.7), measuring tool-use and multi-step task performance from human preference." (LMArena agentic arena, https://lmarena.ai/leaderboard) 7. "Within the 48 catalog models matched to this evaluation: Ranks #3 of 48 on LMArena's agentic arena (score 14.2), measuring tool-use and multi-step task performance from human preference." (LMArena agentic arena, https://lmarena.ai/leaderboard) 8. "Within the 23 catalog models matched to this evaluation: Scores 35.76% ± 3.54 (95% interval) on Terminal-Bench 4.0 (#12 of 23) at none reasoning effort, using Claude Code, measuring a model-and-agent configuration on reproducible terminal tasks." (Terminal-Bench, https://www.tbench.ai/) 9. "Within the 23 catalog models matched to this evaluation: Scores 37.58% ± 3.54 (95% interval) on Terminal-Bench 4.0 (#10 of 23) at xhigh reasoning effort, using Grok Build, measuring a model-and-agent configuration on reproducible terminal tasks." (Terminal-Bench, https://www.tbench.ai/)