Claude and OpenAI offer different model interfaces and operating features. Compare the exact candidate and complete workflow, including cache behavior and tool execution, before deciding which integration is easier to maintain.
Side by side
This table compares each provider’s lowest documented baseline input-price model. Baseline prices use standard or short-context conditions and exclude separate free-tier and off-peak rows when paid standard rates exist. The full pricing reference preserves all documented conditions.
| Attribute | Claude Haiku 4.5 | gpt-5.6-luna |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Official identifier | claude-haiku-4-5-20251001 | gpt-5.6-luna |
| Input USD / 1M | $1 | $0.2 |
| Output USD / 1M | $5 | $1.2 |
| Cached input USD / 1M | $0.1 | $0.02 |
| Context tokens | 200,000 | 1,050,000 |
| Maximum output tokens | 64,000 | 128,000 |
| Modalities | text, image | text |
| OpenAI-compatible endpoint | Not documented | Yes |
| SDK languages | Python, JavaScript, Go, Java, Ruby, PHP, C# | Python, JavaScript, Go, Java, C# |
| Reference | Claude Haiku 4.5 | gpt-5.6-luna |
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Read the source and pricing condition beside a value before comparing it. The table describes published records; it does not establish which model will perform best on your task.
Pricing differences
Claude pricing distinguishes cache creation from reuse. OpenAI pricing also identifies cached input, but the conditions and accounting are not interchangeable. Official documentation.
OpenAI’s price reference separates model tokens from additional hosted-tool and media charges. A short final answer can therefore be only part of the completed workflow’s cost. Official documentation.
Build an estimate from actual repeated tasks. A document assistant may reuse instructions but replace the evidence on every request; a coding workflow may produce long corrections. Keep those shapes separate rather than claim one provider is always cheaper.
Anthropic pricing and OpenAI pricing explain the full charge categories. Use the cost calculator with the same observed workload for both.
Limits and free tiers
Claude has rate and spending controls whose scope depends on the organization and workspace. Check the applied account settings before planning a shared queue. Official documentation.
OpenAI capacity is also subject to usage tiers and model limits. A listed model or newly created key does not prove the account has the traffic allowance your application needs. Official documentation.
Free-access claims need their own evidence. Review the two provider free-tier pages and the actual account offer. A consumer subscription or an old promotion is insufficient evidence for either API budget.
Models and modalities
Claude’s model catalog is the source for its available variants and supported inputs. OpenAI’s catalog covers multiple task-specific model surfaces; choose the operation before comparing identifiers. Official documentation.
A text assistant, a speech interaction and an image-generation task need different output contracts. Do not give a general text comparison credit for a media feature that requires a separate model and implementation. Keep missing specifications explicitly unknown.
Developer experience (SDKs, compatibility, docs)
Claude’s Messages interface and OpenAI’s Responses interface expose different request and response structures. Their native clients keep those contracts visible. Official documentation.
Plan migration around the message roles, content blocks, tool-call sequence and completion state your application actually reads. The most useful portability layer is small enough to preserve provider-specific diagnostics. Test streaming cancellation and malformed tool arguments as well as a normal answer.
Which to pick for … (3 scenarios)
Document review. Shortlist both when the task is text analysis with cited source material. Compare whether each candidate finds the decisive passage, obeys the requested output format and handles missing evidence. Let that result determine the choice.
A tool-using application. Prefer the platform whose documented tool lifecycle matches your execution design. Preserve authorization in your application and judge how much custom orchestration the selected interface requires.
A repeated evaluation job. Compare supported batch and cache conditions with your job timing. Pick the complete processing path that produces usable results within the budget, including failed items and correction passes.
Choose with a saved evaluation set and an explicit operating budget. Anthropic setup and reference and OpenAI setup and reference link the first-request steps.
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Frequently asked questions
Is Claude always cheaper than OpenAI?
Are Messages and Responses payloads interchangeable?
Does the largest context value identify the stronger model?
Can I reuse a cached-input assumption across both?
What should settle a close choice?
Sources
- An Api documentation ↗
- An Price documentation ↗
- An Limit documentation ↗
- An Model documentation ↗
- Oa Api documentation ↗
- Oa Price documentation ↗
- Oa Limit documentation ↗
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