OpenClaw Model Performance Review: Codex 5.3 Leads, GLM Models Disappoint

Model Performance Rankings for OpenClaw

A developer tested multiple AI models with OpenClaw and shared detailed performance observations. The testing covered Codex, Google, Sonnet, Gemini, DeepSeek, and Z.ai's GLM models, focusing on practical usage experience rather than benchmarks.

Top Performing Models

• Codex 5.3 - Rated 9/10. The developer's favorite model, likely fine-tuned for OpenClaw with improved chat agent features. It understands user intent well, provides desired output consistently, and has minimal interruptions and bugs.
• Sonnet 4.6 - Rated 8/10. Second favorite due to speed and problem-solving ability. Offers sufficient experience when Codex 5.3 is unavailable, suitable for daily use.
• DeepSeek 3.2 Agent - Rated 7/10. Clearly customized for OpenClaw, feels like working with a native agent. Not as strong on coding as Sonnet, Opus, or Codex, but a solid alternative for daily use. API fees are noted as potentially high for a Chinese alternative.

Middle Tier Models

• Google 3.1 Pro (Low and High) - Rated 6/10. Tested with antigravity auth. Weak OpenClaw interaction, slow performance, not compelling for constant use. Would only consider if Sonnet and Codex were unavailable.

Disappointing Performers

• GLM 4.7 - Rated 5/10. Marketed as Sonnet alternative with cheap API fees and 3-4x Codex quota on pro accounts. However, it constantly gets stuck, replies late, and produces inconsistent output length even on simple tasks like mail checking. Burned 1 million tokens in a new session just to check 5 emails.
• GLM 5 - Rated 5/10. Benchmarks claim competition with Opus and Codex 5.3, but OpenClaw experience doesn't match. Uses 2-3x more tokens for same tasks, replies late, and provides coding answers at Sonnet 4.5 level. Needs optimization for OpenClaw specifically. Main advantage is price.
• Gemini 3 Flash - Rated 4/10. Only suitable for very simple tasks, not recommended for serious use.

The developer noted that choosing the right model is difficult due to obvious differences in experience, possibly from OpenClaw being unoptimized or model quality issues. They expressed disappointment with GLM models despite wanting to diversify beyond Codex, hoping for future fixes.