aicharts

📈 aicharts plots published AI benchmark scores against cost and tokens per task, and marks the best score at every budget. Compare models and coding agents using sourced results, with each benchmark on its own scale.

See the charts: https://aicharts.io

Ben Guo

aicharts plots published AI benchmark scores against cost and tokens per task. Each chart links its source data and methodology.

The homepage leads with an interactive Pareto frontier: compare model capability against output tokens or cost, then inspect the configuration behind each point. The original coding-agent charts have a focused home at /coding. The separate /benchmarks library covers coding, reasoning, research, memory, images, video, audio, and world models. Charted results, source guides, and emerging evaluations are labeled separately, and older research cohorts are labeled with their dates.

The site header links Charts (/), Benchmarks (/benchmarks), and Notes (/blog). Coding comparisons, model pages, the calculator, and source data are linked where relevant. Legacy root chart and atlas links resolve to the matching workspace; new shares use that workspace’s URL. The charts, benchmarks, calculator, data, model, and blog pages also have Markdown representations through Accept: text/markdown.

Compared with

Other places to compare models: Artificial Analysis runs the Intelligence Index and Coding Agent Index charted here, and its own leaderboards cover more than 250 models with speed and latency. Arena (formerly LMArena) ranks models by people’s votes in blind side-by-side chats. Epoch AI runs its own benchmarks and tracks how capabilities change over time. OpenRouter lists live API prices and which models its users send the most tokens to. aicharts puts published scores against cost per task, draws the best score at each budget, and keeps each benchmark on its own scale.

Checked Sep 28, 2026.

Current benchmark portfolio

The site is a static-data Next.js and TypeScript application. Its benchmark snapshots are committed to the repository, validated at build time, and refreshed automatically. Production never depends on an upstream data source being available during a request.

• Use the official, version-pinned Terminal-Bench 4.0 snapshot as the current coding standard, with exact model, agent, effort, trial, uncertainty, cost, token, duration, and source metadata.
• Explore native score rankings, cost trade-offs where reported, tables, and up to three selected configurations within one benchmark. Inspect exact values, uncertainty, source dates, and evaluation conditions, then share the configured view.