How TokenPico compares AI costs and performance

Compare like with like

A model, a provider endpoint and an offer are separate facts. The initial public catalog is a 2026-10-24 directory observation. It does not certify lifecycle, endpoint configuration, quantization, deployment region or current availability.

The cost equation

Monthly token cost = requests × (uncached input × input rate + cache read × read rate + cache write × write rate + output × output rate). Rates are stored as decimal USD per token; display rates are normalized to one million tokens. Decimal arithmetic is shared across Compare and Calculator.

Conditional and missing prices

Unknown rates are not zero. A missing cache rate prevents an estimate when that cache category is used. Conditional catalog rates are excluded from estimates until exact per-request thresholds and UTC periods are implemented and reviewed. No verified provider offer exists in this initial dataset, so there is no lowest-price recommendation.

Counter accuracy

The browser runs gpt-tokenizer with o200k_base in a Web Worker. This is exact text tokenization for that encoding only; it is a proxy estimate for models whose matching tokenizer has not been verified. It excludes API wrappers and multimodal overhead. Claude is always labeled an estimate. Input is limited to 1 MiB in total; this is an initial guardrail, not a validated limit for every device.

Context is a budget

The context planner adds separately encoded text fields and files, then reserves output space. The capacity is user selected and must be checked against the exact endpoint. Token IDs correspond to the encoding that actually runs.

Choose capability before price

Every model in API Compare shows its catalog-reported inputs, tool calling and structured-output capabilities next to price. These are interface requirements, not independently tested quality ratings. An absent catalog flag is unknown, not a measured failure. Image input means understanding images, not generating images.

Evaluation standards & original sources

Choose an evaluation that matches your task. The sources below are independent evaluation projects, not endorsements of TokenPico. These links explain the standards; they do not assert that every model in our catalog appears on each leaderboard.

How a score can enter this comparison

Require an exact model/version match, benchmark version or problem date range, reasoning settings, agent harness and tools where relevant, inference/token budget, score scale, result date, original source and permission to republish. Vendor-reported results and independent runs must be labeled separately. Never inherit a family score or mix different benchmark scales into an invented overall score.

Quality score: not imported means no authorized, configuration-matched result is attached here. It does not mean zero, poor quality or absent testing elsewhere. No quality sorting, price-per-score or best-model recommendations are enabled without comparable evidence.

Artificial Analysis provides a versioned methodology and a data API with attribution and licensing conditions. Public visibility alone is not republication permission. API attribution and licensing ↗

Source references checked October 25, 2026. Test releases and coverage can change; consult the original result before choosing a model. Validate shortlisted models on representative tasks of your own.

Performance is not one score

Coding, reasoning and human preference are different measurements. We do not publish an invented intelligence score. No third-party benchmark values, speed measurements or popularity numbers have been imported. OpenRouter usage would describe that platform and a stated UTC period, not global popularity.

Subscriptions and media

A subscription is not an API offer. Country, channel, taxes and actual annual upfront payment require separate checkout evidence. Image and video prices require matched generation specifications and original units. Pending candidates are not ranked.