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

Antigravity CLI · Gemini 4 Argon (default) scores 63.8 on AA Index at $5.84 per task, third of 31 configurations. Every row above it costs at least 2.2x as much.

Codex · GPT-6.1 Sol (xhigh) scores 62.9 on AA Index at $1.04 per task, fourth of 31 configurations. Max scores 60.1 at $1.55.

Claude Code · Opus 5.5 (max) scores 66.0 on AA Index at $13.04 per task, second of 31 configurations. The next frontier point down gives up 2.2 points for 45% of the cost.

Claude Code · Sonnet 5.5 (max) scores 68.4 on AA Index at $14.19 per task, first of 31 configurations and the costliest row. The next frontier point down gives up 2.4 points for 92% of the cost.

Claude Opus 5.5 at max effort scores 57.6 on the Intelligence Index at $5.98 per task, first of 108 configurations.

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.

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.

Artificial Analysis records an Intelligence Index score of 46.3 at $0.133 per task for MiMo-V2.6-Pro in the snapshot retrieved Oct 10, 2026, 5:51 AM UTC.

Nine researchers held one coding-agent loop fixed and toggled planning, tools, and context management across 176 settings. Each component helped only under named conditions.

UC Berkeley and Arena researchers ran 21 model and harness pairs on two public suites. Success stayed close across harnesses; cost did not.

Specific Labs licensed private production codebases and scored eight model-and-harness pairs over 640 rollouts. The leading 38.8% is an aggregate that per-task results reorder.

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%.

Scientists accepted 70 of 920 proposed workflows. The leading configuration resolved 30 percent; cost and token frontiers show why that rate is incomplete.

French-Owen reports about $0.10 per run versus about $1 with earlier Sonnet-class models, from one person’s experiment.

Dan Luu’s FRE loop won a public regex suite and then failed a holdout. A high coding-agent score still needs cases the optimizer could not see.

SemiAnalysis’s faster catch-up describes era composites. Closed configurations still lead AA Index in this coding-agent snapshot.

The cost frontier shows which configurations offer a higher AA Index only at a higher mean task cost.

Epoch AI and METR hide the original source and grade a replacement on held-out tests under project-scale budgets.
Each note starts with primary or first-party evidence. Material claims link directly to those sources.
Leaderboard values are paired with their observation date and named configuration. They can change after publication.
Methodology limits and interpretation are kept near the results they qualify. Benchmark performance is not treated as a general production claim.