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AI model and agent benchmark analysis

Sourced analysis of AI model and agent benchmarks: what each evaluation measures, what the results show, and where the evidence stops. The first collection focuses on coding agents.

Explore the coding-agent chart

Articles

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Introducing aicharts

aicharts plots published AI benchmark scores against cost and tokens per task, and a local collector measures your own agents' token use.

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GPT-6 Sol scores 56.7 on the coding-agent chart at $2.99 a task

Codex · GPT-6 Sol (max) scores 56.7 on the coding-agent AA Index at $2.99 per task, seventh of 20 configurations, and GPT-6 Sol (max) scores 47.5 on the Intelligence Index at $1.06 per task. Each row sits on its own chart’s cost frontier.

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Grok 4.7 reaches 56 on AA Index inside Grok Build

In the snapshots shown below, Grok Build · Grok 4.7 (xhigh) scores 56 on the coding-agent AA Index and Grok 4.7 (xhigh) scores 46 on the Intelligence Index. Different harnesses, task sets, and costs sit behind the two numbers, and cheaper configurations score higher on both charts.

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What Devin Fusion’s 39% saving measures

Cognition’s 39% is one comparison: an Astra-led Fusion pair against Codex on the Artificial Analysis index, at a lower score. The same post’s other reported savings run from 11% to 46%.

Method

Sources

Each note starts with primary or first-party evidence. Material claims link directly to those sources.

Changing results

Leaderboard values are paired with their observation date and named configuration. They can change after publication.

Limits

Methodology limits and interpretation are kept near the results they qualify. Benchmark performance is not treated as a general production claim.