Frequently asked questions

Switch the calculator to Budget → Tokens, enter $10, and choose a workload mix. TokenWatch shows how many tokens each priced offering can serve. A cheap rate does not guarantee the model has the quality, context window, or capabilities your task needs.

Search for the provider and model, enter your token volume, then set the input, cached-input, and output percentages. The result uses that provider offering’s current rates rather than a model-wide average.

Search for the model and compare every matching provider row. The cheapest provider can change with your token mix because input, output, cache-read, and cache-write rates differ.

The same underlying model can be hosted by several inference providers. Quantization, batch, fast, preview, and endpoint variants can also remain separate when they have different identities or prices.

Choose Monthly Volume, enter daily token usage, and select a workload preset or your own mix. TokenWatch multiplies the daily result by 30; retries, tool calls, and context growth still need to be represented in the volume you enter.

Blended $/M combines input, cached-input, and output prices using the selected workload percentages. It is a comparison rate, so it excludes the monthly multiplier and one-time cache-write amortization.

A provider can charge a lower cache-read rate when repeated prompt content is reused. TokenWatch applies that rate only to the cached-input share you enter.

A missing cache-read price is not free usage. TokenWatch falls back to the provider’s normal input rate for the cached-input share unless the source publishes a separate cache price.

Some providers charge once when prompt content enters the cache. Enter the cache-write token amount and spread that charge across the number of requests expected to reuse it.

ZDR means the provider says request content is not retained beyond processing. It is a data-retention property, not a general security or compliance certification; review the linked provider policy before sending sensitive data.

You can compare up to six offerings, copy a comparison card as an image, export the current result set as CSV, or share the URL hash that stores the calculator state.

The current catalog contains 1370 text offerings across 84 providers and records a generation timestamp. Direct-provider data takes precedence over OpenRouter, then maintained fallback sources. Promotions and provider pages can change between refreshes, so verify prices before a purchasing decision.

Choose Budget → Count, enter $10, and filter to a model or provider. Flat per-image offerings return a direct count; megapixel and image-token offerings need workload details before a final image count is meaningful.

Per-image pricing charges a flat amount for one generated image. Per-megapixel pricing scales with output area. Image-token pricing depends on the provider’s image-token calculation. The three units are not interchangeable.

A flat image rate, a megapixel rate, and an image-token rate measure different work. TokenWatch separates them rather than inventing assumptions about resolution or token use.

Providers may price sizes, quality tiers, aspect ratios, edit modes, or endpoint versions separately. The calculator keeps those variants visible so a cheaper option is not mistaken for an equivalent configuration.

Yes. Search or filter by model and provider, then compare rows that use the same billing unit and a comparable output configuration.

Resolution can raise cost directly under per-megapixel pricing and can select a more expensive variant under flat pricing. Use the variant filter and confirm the provider’s size limits.

Multiply the selected per-second rate by 30. The crawlable table shows that example for each model’s cheapest listed variant, while the calculator lets you change duration and filters.

Choose Budget → Seconds and enter $10. TokenWatch divides the budget by each selected variant’s per-second price.

Higher-resolution variants often have a higher per-second rate. TokenWatch shows the resolution attached to each price so a 720p rate is not presented as a 1080p or 4K rate.

Some endpoints include generated audio, some exclude it, and some do not state the audio mode. Use the audio filter and compare like-for-like variants.

A provider can publish separate prices for resolution, audio, generation mode, or endpoint variants. TokenWatch preserves those records and selects only the cheapest one for the crawlable summary table.

Yes. Filter by model, resolution, and audio, then compare the per-second and total-duration costs of the remaining rows.

A single 0-100 score from Artificial Analysis, an independent lab that runs the same evaluations on every model. It blends nine practical tests: real-world work tasks, terminal-based coding, tool use, scientific reasoning, knowledge, and long-context reasoning. Think of it as a general capability rating measured identically for everyone. A 60 is genuinely strong; solid workhorse models land in the mid-40s. It anchors the Reasoning & Knowledge and Chat & UI Quality tabs.

A 0-100 zoom-in on autonomous multi-step work: using tools, navigating a terminal, recovering from errors without a human in the loop. It is the primary metric of the Agentic Coding tab because it measures the exact skill bots and automation need. A model can chat beautifully and still score poorly here.

A 0-100 measure of writing and fixing real code, from AA's own coding evaluations. Distinct from the Agentic Index: coding is "can it produce correct code", agentic is "can it complete a whole task on its own". Shown together on the Agentic Coding tab because real coding bots need both.

A benchmark suite from an academic consortium that refreshes its questions every six months, so models cannot have memorized the answers from training data ("contamination") — a real problem for older benchmarks. Scores are objective right/wrong results, not opinions, and every model runs the same release. We publish the release date we use.

LiveBench's test of producing working code in JavaScript, TypeScript, and Python inside an agent harness — the model must iterate, run, and fix its own code. It anchors the Agentic Coding tab alongside the AA indices because it measures execution, not just code writing.

Theory-of-mind, spatial, logic-puzzle, and navigation-logic tasks — multi-step "think it through" problems with verifiable answers. Shown on the Reasoning & Knowledge tab; a good proxy for analysis and problem-solving workloads.

Competition and olympiad mathematics plus integral solving. It stresses precise symbolic reasoning where a near-miss is still wrong. Useful when your workload cannot tolerate plausible-but-wrong derivations (financial models, scientific code).

Joining tables, reformatting tabular data, and tracking events across records — the spreadsheet-and-database skills behind most knowledge-work automation. It anchors the Knowledge Work tab.

Whether the model does exactly what was asked — paraphrasing under constraints, simplifying without losing meaning, hitting story and summary requirements. The difference between "a good answer" and "the answer you specified"; critical for repeatable workflows.

Wordplay, connections, plot reconstruction, and typo detection — precision with language itself rather than world knowledge. Complements instruction following on the Knowledge Work tab.

A head-to-head vote: two models build a website or UI from the same prompt, humans pick the better result, and Elo scores accumulate like chess ratings. Around 1300 is decent; 1450+ is excellent. It anchors the Chat & UI Quality tab and appears on Agentic Coding because frontend output quality is part of shipping. The model detail view also shows each model's best category (website, 3D, dataviz, and so on).

The cheapest price for that model across tracked providers, as a blended rate per million tokens at the token mix from the Text calculator (cached-heavy by default). The benchmarks page recomputes this live from the mix you last used on the Text tab, since the cheapest provider can change with the mix.

Capability per dollar: the tab's primary score divided by the blended price, scaled so the best model in the current view equals 100. It answers "if I do not need the absolute best, what gives me the most capability per cent?" It is a relative ranking within the current tab and filters, not an absolute measure, and it is never compared across tabs.

No one has published that benchmark for that model yet, or the evaluation is newer than our data. New models typically receive scores within days to weeks of release. Only models purchasable through a tracked provider are listed — a score without a price cannot be comparison-shopped.

Pick the tab closest to your workload: agents or coding bots — Agentic Coding; analysis, research, or reasoning — Reasoning & Knowledge; documents, summaries, spreadsheets — Knowledge Work; anything visual — Chat & UI Quality. When two models are within a few points, treat them as tied; price and speed usually decide it.