Claude Code A/B Test Silently Shrinks 'High' Effort to Former 'Low' Level

✍️ OpenClawRadar📅 Published: August 23, 2026🔗 Source
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Developers using Claude Code may have noticed a sudden dip in performance this week. According to a report from X user @argofowl, Anthropic is running a server-side A/B test that silently reduces the effective effort level for certain sessions. The change affects Claude Code version 2.1.236 and later, where the model now interprets "high" effort as 10 out of 100 — the exact value "low" used to use. Older versions and Opus 5 are unaffected, and the changelog doesn't mention it.

Key Details

  • Version affected: Claude Code 2.1.236+ (specifically noted after 2.1.237)
  • Change: The effort scale is shrunk server-side; "high" now maps to 10/100 instead of its previous higher value.
  • Scope: Enrolled sessions only — it's an A/B test, so not everyone sees it.
  • Detection: If "high" feels like "low" to you, you're in the test group.
  • No changelog entry: The change was made without any public documentation.

The reporter, @argofowl, spent an afternoon convinced their own tools were broken before discovering the issue. They expressed frustration with Anthropic's opaque testing practices: "holy fuck anthropic, you guys are unbearable sometimes."

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Analysis

This isn't a client-side fix — it's a server-side configuration that affects how the model interprets effort parameters. If you're on version 2.1.236 or later and notice degraded output quality when setting effort to "high", you may be part of this experiment. To avoid the test, you could pin to an older version (e.g., 2.1.235 or earlier) or wait for Anthropic to roll out or revert the change. Since it's an A/B test, your experience may vary.

For developers relying on consistent AI coding agent behavior, this kind of silent change is concerning. There's no official statement from Anthropic yet, and the change isn't mentioned in any public changelog. If you're hit, consider checking your Claude Code version and possibly downgrading to ensure stable performance.

📖 Read the full source: HN AI Agents

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👀 See Also