# Best LLM for OpenClaw

> 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-openclaw

Claude Opus 5.5 ranks first for OpenClaw as of October 22, 2026. It 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. 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]

## 1. Claude Opus 5.5
https://modelmatchbook.com/models/anthropic/claude-opus-5.5-20260921

Claude Opus 5.5 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.

**Best when:** Its strongest result for this task is on Terminal-Bench, which measures completing difficult repository and terminal tasks through an agent harness. [1]

## 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. [2]

## 3. GPT-6.1 Sol
https://modelmatchbook.com/models/openai/gpt-6.1-sol-20260929

Ties Astra at 58.18% on Terminal-Bench 4.0 (#3 of 23) but ranks lower on LMArena's agentic arena (#4 of 48, score 13.5), showing solid terminal execution with slightly less polished multi-step preferences.

**Best when:** Use for Codex-based terminal workflows where 58.18% Terminal-Bench 4.0 performance with max reasoning effort supports reproducible task completion. [4]

## 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. [5]

## 5. Claude Fable 5.1
https://modelmatchbook.com/models/anthropic/claude-fable-5.1-20260831

Claude Fable 5.1 ranks #3 of 48 on LMArena's agentic arena (score 14.2), 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]

## 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. [3]

## 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. [7]

## 8. Grok 4.7
https://modelmatchbook.com/models/x-ai/grok-4.7-20260916

Ranks #16 on LMArena's agentic arena (score 3.2) with 37.58% on Terminal-Bench 4.0, showing moderate performance in both terminal execution and preference-based tool-use.

**Best when:** Consider for Grok Build environments where xhigh reasoning effort yields 37.58% Terminal-Bench 4.0 performance in reproducible terminal tasks. [8]

## FAQ

**What is the best model for OpenClaw?**

Claude Opus 5.5 ranks first as of October 22, 2026. Claude Opus 5.5 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. [1]

**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. [2]

**Which open-weight model ranks highest for OpenClaw?**

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. [3]

## 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 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/)
3. "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/)
4. "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/)
5. "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)
6. "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)
7. "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/)
8. "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/)