Benchmark Results: 6 Low-Cost Models vs. Claude Sonnet 4.6 for OpenClaw Orchestration

A developer ran a benchmark to find a cheaper alternative to Claude Sonnet 4.6 as the main orchestrator for an OpenClaw AI coding agent setup. The test used a consistent 5-task gauntlet with real files and tools, without hand-holding prompts.

The Gauntlet Tasks

• T1: Recall details from a specific file (MEMORY.md open items)
• T2: Inspect files, spot incompleteness, cross-reference + prioritize
• T3: Execute a shell command, parse and report exact output
• T4: Spot a delegation task and hand it off correctly
• T5: Synthesize results into executive summary

Benchmark Results

Raw scores out of 5, with cost per million output tokens:

• Claude Sonnet 4.6: 5/5 ($15/M) – Baseline, handles the entire operation flawlessly
• o4-mini: 5/5 ($4.40/M) – 71% cheaper, aced all tasks but with noticeable lag on reasoning chains
• Grok 4.1 Fast: 3/5 ($0.50/M) – Crushed T1/T3/T5, but failed T2 hard (read 4 lines of SMS log, declared "all clear")
• Gemini 2.5 Flash: 1/5 ($2.50/M) – Nailed T1, then stopped responding mid-prompt
• DeepSeek V3.2: 0/5 ($0.42/M) – 2-second runtime, zero output
• Llama 4 Maverick: Disqualified ($0.60/M) – Hallucinated file contents, invented fake video filenames dated 2024 (current year is 2026), never called real tools

Key Finding: The Judgment Gap

The critical failure point was T2 file judgment. Models had to read a short log (4 lines: SMS sent, done), realize it was incomplete, pivot to MEMORY.md, list all open items across the workspace, then prioritize correctly (medical appointment March 19 > cron flake > etc.). Only Sonnet and o4-mini succeeded. Other models were described as "lazy or blind" on this task.

Practical Implementation

The developer's conclusion: Sonnet stays as the main orchestrator. Grok 4.1 Fast is assigned to all subagents (video QA, distribution, analytics) for a 97% savings on scoped tasks like "generate pick" or "post tweet."

They also implemented a 3AM cron job that hunts new model releases via web search, auto-runs the gauntlet, generates a best-to-worst bar chart, and emails the report.

The core lesson: Orchestration requires judgment on file gaps, delegation timing, and synthesis—areas where cheap models consistently fail. Subagents, however, can use cheaper models effectively for specific, scoped tasks.