See which model wins at each price.
Benchmark scores plotted against cost and tokens per task, plus a local collector for your own agents' token use. Compare models on the work your agents actually do.
Capability and cost
Higher score, lower cost
113 options
Method & data
What the axes mean. Output tokens are answer plus reasoning generated per Intelligence Index task; they exclude input and cache traffic. Cost is the publisher’s estimated total task cost. The horizontal axes are logarithmic, so equal spacing represents equal proportional change.
Cohort and frontier. The source has 715 records. 118 are current configurations with a non-estimated score, output-token count, and cost breakdown, and 113 of those report a task cost above zero. The curve connects the configurations with the highest score at each resource budget; aicharts draws it from this cohort. No benchmark families are blended.
Index construction. The publisher’s 10-evaluation index weights agents 30% · coding 20% · scientific 20% · general 30%. These model-level results are separate from the coding-agent chart and its total-token counts.
Source. Publisher methodology · Source terms. Measurements come from the publisher’s public models leaderboard; the snapshot date is a retrieval date, not a model’s evaluation date.
About aicharts
Picking a model means guessing at a tradeoff between quality and price, because benchmark scores and prices live in different places. aicharts plots published benchmark scores against cost and tokens per task on one chart, so the strongest option at each budget is visible instead of implied. A local collector measures your own agents' token use, so the cost question covers your work, not only the models. aicharts is free and open source.
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.
Measure your own agents
The aicharts collector adds up the tokens, cost and time your coding agents use, per model and day. Prompts and transcripts stay on your machine. In development. How it works
Recent Intelligence Index listings
Intelligence Index v4.3.2. Card dates come from the Artificial Analysis listing and are not verified first-party release dates.
OpenAI GPT-6.1 Terra
Artificial Analysis Intelligence Index v4.3.2
Index33.5
Kimi Kimi K3.1 Pro
Artificial Analysis Intelligence Index v4.3.2
Index55.1
InclusionAI Ling 3.1 Flash 1019
Artificial Analysis Intelligence Index v4.3.2
Index43.9
MiniMax MiniMax-M3.1-Flash
Artificial Analysis Intelligence Index v4.3.2
Index42.7
First-party release radar
New releases found at first-party sources
31 labs · 37 first-party sourcesThese links come from each lab's own release pages, so a model can appear here before aggregators such as OpenRouter list it. The date shows when aicharts first found the page, which can differ from the official release date. Official dates and benchmark scores come from their own sources.
Release radar
New, awaiting complete benchmark coverage
31 incomplete · 3 with early DeepSWE · OpenRouter checked- Particle-1Particle AI · first observed
- GLM 5.3 FlashZ.ai · first observed Early DeepSWE 63.4% pass@1 · max · 4 runs · OpenRouter match
Models listed here do not yet have a complete result on the Artificial Analysis coding-agent chart. Where shown, an early DeepSWE v1.1 pass@1 comes straight from DataCurve's mini-swe-agent leaderboard, matched to the model through OpenRouter's catalog, or through Artificial Analysis when OpenRouter has no match. That result depends on DataCurve's harness, so it stays off the Artificial Analysis chart and model cards. Models with partial Artificial Analysis results can appear there, with missing metrics marked.
Recent models and notes
Compact Index listings and published analysis. Model scores come from Artificial Analysis Intelligence Index v4.3.2.
- NoteHighest AA Index and lowest cost pick different coding agentsThe cost frontier shows which configurations offer a higher AA Index only at a higher mean task cost.
- NoteWhy a coding-agent high score still needs a holdoutDan 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.
- NoteWhat Devin Fusion’s 39% saving measuresCognition’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%.
- ModelGPT-6.1 TerraOpenAI · Index 33.5 · $0.11 per task
- ModelKimi K3.1 ProKimi · Index 55.1 · $2.1 per task
- ModelLing 3.1 Flash 1019InclusionAI · Index 43.9 · $1.5 per task
- ModelMiniMax-M3.1-FlashMiniMax · Index 42.7 · $0.22 per task
- ModelDeepSeek V4.1 Flash 1017DeepSeek · Index 45.1 · $0.26 per task
Subscription vs API vs GPUs
One maxed ChatGPT Pro seat implies a monthly token volume. The calculator prices it five ways: the subscription sticker, OpenAI and DeepSeek API rates, GPUs you buy, and GPUs you rent.
