# Best LLM for Hermes Agent > 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-hermes Claude Opus 5.5 ranks first for Hermes Agent 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-preference tool-use and multi-step 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-preference tool-use and multi-step performance. **Best when:** Use for complex terminal-based automation where highest accuracy on reproducible tasks matters, as it scores 64.85% on Terminal-Bench 4.0. [1] **More tips:** - Deploy when human preference rankings for tool-use and multi-step tasks are critical, holding #1 rank with score 20.9 on LMArena's agentic arena. [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 Ties for #3 on Terminal-Bench 4.0 with 58.18% and ranks #4 on LMArena's agentic arena with score 13.5, showing balanced performance across terminal and multi-step tasks. **Best when:** Use for multi-step tool-use workflows where human preference rankings matter, scoring 13.5 and ranking #4 on LMArena's agentic arena. [6] ## 4. GPT-6 Astra https://modelmatchbook.com/models/openai/gpt-6-astra-20260903 Ties for #3 on Terminal-Bench 4.0 with 58.18% and ranks #2 on LMArena's agentic arena with score 14.7, excelling at multi-step tool-use per human preference. **Best when:** Prioritize for multi-step agentic workflows where human preference is key, ranking #2 with score 14.7 on LMArena's agentic arena. [7] ## 5. Claude Fable 5.1 https://modelmatchbook.com/models/anthropic/claude-fable-5.1-20260831 Ranks #5 on Terminal-Bench 4.0 with 57.88% and #3 on LMArena's agentic arena with score 14.2, offering strong multi-step performance with slightly lower terminal accuracy. **Best when:** Use for balanced agentic workloads, ranking #3 with score 14.2 on LMArena's agentic arena for tool-use and multi-step tasks. [8] ## 6. GLM 5.3 https://modelmatchbook.com/models/z-ai/glm-5.3-20260816 Open-weight model ranking #9 on Terminal-Bench 4.0 with 41.82% and #25 on LMArena's agentic arena with score 1.4, offering self-hostable option with significant capability gaps versus closed-weight leaders. **Best when:** Deploy as open-weight option for terminal tasks where 41.82% accuracy is acceptable, ranking #9 on Terminal-Bench 4.0. [4] **Watch out for:** - Expect weak multi-step tool-use performance per human preference, ranking #25 with score 1.4 on LMArena's agentic arena. [5] ## 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. [9] ## 8. Grok 4.7 https://modelmatchbook.com/models/x-ai/grok-4.7-20260916 Ranks #10 on Terminal-Bench 4.0 with 37.58% and #16 on LMArena's agentic arena with score 3.2, showing mid-tier performance on both terminal and multi-step tasks. **Best when:** Use for general agentic tasks where moderate performance suffices, ranking #16 with score 3.2 on LMArena's agentic arena. [10] ## FAQ **What is the best model for Hermes Agent?** 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-preference tool-use and multi-step 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 Hermes Agent?** GLM 5.3 is the highest-ranked open-weight model, at #6. Open-weight model ranking #9 on Terminal-Bench 4.0 with 41.82% and #25 on LMArena's agentic arena with score 1.4, offering self-hostable option with significant capability gaps versus closed-weight leaders. [4,5] ## 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 48 catalog models matched to this evaluation: Ranks #25 of 48 on LMArena's agentic arena (score 1.4), measuring tool-use and multi-step task performance from human preference." (LMArena agentic arena, https://lmarena.ai/leaderboard) 6. "Within the 48 catalog models matched to this evaluation: Ranks #4 of 48 on LMArena's agentic arena (score 13.5), 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 #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) 8. "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) 9. "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/) 10. "Within the 48 catalog models matched to this evaluation: Ranks #16 of 48 on LMArena's agentic arena (score 3.2), measuring tool-use and multi-step task performance from human preference." (LMArena agentic arena, https://lmarena.ai/leaderboard)