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There is no useful provider-wide answer to which API costs less. Compare the exact models you might use on the same workload, and count input, output, caching, batch processing, tools, and deployment route separately. Those details can change the bill substantially; a headline input-token rate cannot tell you the total cost.
What determines the total API cost?
Both providers price usage according to the selected model and how you use it. OpenAI’s pricing page separates input, cached input, cache writes, and output, and says API surfaces such as Responses and Chat Completions are not priced separately. Anthropic’s pricing documentation also breaks out model and token categories, alongside caching, batch, tools, and geography. Check the live rates before calculating: prices change, and no reliable current model-by-model price comparison is established here. OpenAI API pricing · Anthropic pricing documentation
For either provider, estimate monthly use by metered category and multiply each category by the selected model’s current rate. Include the cost of extra retries or tokens if one option does not meet your quality or latency needs at the same settings. This is a way to compare scenarios, not evidence that one provider is inherently cheaper.
Build an apples-to-apples comparison
Start with a real application workload rather than a generic “average request.” For each candidate model, record the assumptions below, then calculate a monthly total using the provider’s live pricing and applicable account or platform terms.
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| What to record | Why it affects the estimate |
|---|---|
| Exact model name and version | Rates are model-specific at both providers. Use the model you would actually deploy. |
| Monthly input and output tokens, separately | Input and generated output are distinct priced categories. Estimate typical and peak traffic rather than treating all tokens alike. |
| Cached tokens, cache writes and reads, and cache lifetime | Cached content may be billed differently from ordinary input. Include how often prompts are written and reused. |
| Share of work eligible for batch processing | Asynchronous work may qualify for different pricing. Confirm current terms and eligibility for the particular model and job. |
| Tool definitions, calls, and returned content | Tool schemas and tool-use or result content can add tokens; some server-side tools may have separate usage charges. |
| Deployment route, region, and billing path | First-party API and partner-operated cloud routes may differ in regional pricing, billing units, invoice visibility, and terms. |
| Quality and latency requirements | A lower per-request charge may not mean lower operating cost if the workload needs more tokens, retries, or a different model to meet its requirements. |
Account for caching and batch processing
Prompt caching
Anthropic’s pricing documentation describes five-minute cache writes at 1.25 times the base input rate and one-hour writes at 2 times the base input rate. Standard cache reads are generally 0.1 times the base input rate, with model-specific exceptions. Writes are charged when content is stored; reads are charged on subsequent cache hits. Apply these terms only to the eligible model and cache behavior shown in the live pricing documentation. Anthropic pricing documentation
OpenAI’s pricing page distinguishes cached input and cache writes. For a fair comparison, estimate how many tokens are eligible and reused for each provider, and verify current cache rates and requirements rather than assuming that cache handling is identical.
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Batch processing
Anthropic documents a 50% discount on input and output tokens through its Batch API. That is relevant only to work that can run asynchronously and qualifies under the current terms. Check the exact model and job before applying the discount. Verify any batch terms for OpenAI directly on its live pricing page as well; do not assume a provider’s discount applies to the other provider or to every request.
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Include tools and deployment route
Tools add more than the tool call
Anthropic says tool-use requests include tokens from tool definitions and tool-use or result content. Server-side tools can also add usage-based charges; its documentation gives web search as an example. Include both the tokens and any separate tool fees in the estimate. Do not count only the model’s visible response.
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Region and cloud platform can change billing
Anthropic documents a 1.1× multiplier for specified US-only inference settings on Claude 4.6 and later. Partner-operated platforms have their own regional pricing. Anthropic also describes hourly usage metering and monthly invoices for Claude Platform on AWS and Claude in Microsoft Foundry. If you compare a first-party API with a cloud-partner route, state the region, provider, invoice path, and any negotiated discounts as assumptions; the listed token rate alone may not capture the full difference.
Calculate a defensible monthly estimate
- Choose candidate models. Write down each exact model name and version, plus the deployment route and region you intend to use.
- Measure the workload. Estimate monthly requests and input and output tokens separately. Include normal and peak usage where it matters to capacity or service requirements.
- Separate special usage. Estimate cache writes and reads, eligible batch traffic, tool-related tokens, and separately metered server-side calls.
- Use live prices. Check each provider’s current pricing page for the selected model, usage categories, and route. Anthropic’s documentation directs readers to its live pricing page for current prices; OpenAI’s rates should likewise be checked before calculating.
- Apply only eligible terms. Use a cache rate, batch discount, or regional multiplier only when the model, workload, and deployment settings qualify.
- Compare the same outcome. Evaluate task success, quality, latency, and expected retries alongside estimated charges. If the workload needs different settings or produces different results, label those assumptions instead of presenting the totals as directly equivalent.
What a provider comparison can—and cannot—tell you
A useful result is a workload-specific estimate, such as “Model A costs this much under these token volumes, cache assumptions, tools, and region.” It is not a universal verdict about OpenAI or Anthropic. Rates are model-specific, feature terms can differ, and cloud-partner billing may not match first-party API billing. Recheck both live pricing pages whenever models, usage patterns, or deployment routes change.
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