Artificial Analysis Coding Agent Index v2.1 refreshes its tests, and two Claude models tie at the top
Anthropic's Claude Fable 5.2 and Claude Opus 5.5 share first place with a composite score of 58 in the refreshed Coding Agent Index v2.1.
Artificial Analysis has released version 2.1 of its Coding Agent Index, with Anthropic's Claude Fable 5.2 and Claude Opus 5.5 sharing the top spot with a composite score of 58. The updated index swaps in FrontierCode v1.1 and Terminal-Bench 4.0 as part of its refreshed testing methodology.
Notably, Claude Opus 5.5 scored 61.7 on the previous version of the index, meaning the top number actually decreased with the new benchmarks. When test parameters change, the scoring expectations reset accordingly.
Updated benchmarks
The index evaluates combinations of AI models and agent harnesses—software scaffolding that enables AI to read files, run commands, and take actions across multiple steps. Version 2.1 spans approximately 290 tasks across three benchmarks, each carrying equal weight in the final composite score.
FrontierCode, now at v1.1 built by Cognition, covers software engineering work on real open-source repositories with 100 tasks. Terminal-Bench 4.0 from the Laude Institute measures agentic terminal use through 66 tasks. SWE-Atlas-QnA-AA tests repository question-answering capabilities.
Model performance context
Claude Fable 5.2, launched by Anthropic on November 9, 2026, is positioned for complex coding challenges. The tie for first place on the updated index directly tests this positioning. Claude Opus 5.5, which previously held the top score, now matches its newer sibling at 58 under the new benchmarks.
Because v2.1 runs on updated benchmarks, a lower composite score is not, on its own, evidence that Opus 5.5 got worse. Comparing 61.7 on one version of the test to 58 on another is a bit like comparing a 5K time to a trail run time.
Testing methodology significance
FrontierCode's use of maintainer merge decisions as the scoring standard pushes evaluation closer to real-world usefulness. The decision to score models and harnesses together suggests that tooling wrapped around a model can shape outcomes alongside the model itself.
Equal weighting across the three benchmarks means agents that excel in one area but struggle in others will have their weaknesses reflected in their composite score. This update represents an engineering milestone rather than a financial one, focusing on how AI labs and their customers measure progress.
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