Dataset
AI provider pricing index
AI provider pricing index is built for teams comparing AI providers from source-backed input, output, cache, context, and capability data. Use it to decide which model is cheap enough, capable enough, and well sourced enough for a workload. Keep the workload assumptions consistent across options, then inspect the cited prices and last-checked dates before committing budget.
Open the pricing index - Explore model prices and evidence →
The decision this page helps you make
Compare source-backed AI model token prices, context windows, confidence grades, and change history across providers. Pin contenders and inspect evidence.
The practical question is which model is cheap enough, capable enough, and well sourced enough for a workload. Use the same workload assumptions for every option so the comparison reflects billing differences instead of different inputs.
Start with these inputs
- Input/output rates: Normalized USD per 1M tokens.
- Evidence: Source links, confidence grades, last checked dates.
- Comparison: Pinned contenders, filters, and price-change context.
What the result includes
| Area | What ByteCosts shows |
|---|---|
| Input/output rates | Normalized USD per 1M tokens |
| Evidence | Source links, confidence grades, last checked dates |
| Comparison | Pinned contenders, filters, and price-change context |
How to use the result
- Run a realistic base case and a heavier-usage case before choosing a provider or plan.
- Compare alternatives with identical traffic, token, seat, runtime, and retry assumptions.
- Open the cited provider source before a purchase or production billing decision.
Formula
workloadCost = inputTokensM * inputPricePerMTok + outputTokensM * outputPricePerMTok, compared across models with the same workload shape.
Assumptions
- Rows are normalized to USD per million tokens where possible.
- Rows preserve unknown or null data instead of inventing missing fields.
- Source confidence and last-checked dates are part of the decision, not decoration.
- Volume discounts, batch pricing, and cache rates are separate fields when available.
Example scenario
Pin several candidate models, keep the same input/output mix, and compare list prices, source confidence, context window, and change history before selecting a provider.
How to read the example
| Step | Example input | What to inspect |
|---|---|---|
| Pinned models | Same workload across each candidate | Cost and capability spread |
| Evidence | Confidence grade and last-checked date | Budgeting trust level |
| Change context | Recent price events and source links | Refresh risk before rollout |
Interpretation guide
- Do not choose only on a single input-token rate; output and retries can dominate.
- Prefer source-backed rows for budgeting and treat weak-source rows as exploratory.
- Open provider and comparison pages for workload-specific context.
Limitations
AI provider pricing index is a planning tool, not a billing guarantee. It uses the visible assumptions and committed source-backed data available at the page's last update.
Check the cited provider page and your own production logs before signing a contract, changing price, or committing infrastructure spend.
Frequently asked questions
What should I enter first in AI provider pricing index?
Start with input/output rates: normalized usd per 1m tokens. Add optional adjustments only after the base case is understandable.
Is the result a guaranteed invoice forecast?
No. It is a planning estimate based on the visible workload assumptions and source-backed public prices. Taxes, negotiated discounts, undocumented limits, and production behavior can change the final invoice.
Where do the prices and assumptions come from?
ByteCosts keeps provider source links, confidence information, and last-checked dates attached to pricing records. User-entered workload assumptions remain separate from published vendor facts.
AI provider pricing index. ByteCosts. https://bytecosts.com/tools/ai-provider-pricing/