Compare two AI models or providers using live specifications, prices and operating conditions. Then use the focused provider comparisons below to connect the numbers with the application you plan to build.
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Choose model mode for an exact product comparison or provider mode to inspect each catalog’s range. Provider ranges combine different models and should not be interpreted as a single product quote.
The following initial reference compares Anthropic and OpenAI. Use the interactive selectors above or a focused comparison below to choose another pair.
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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These comparisons also cover practical alternatives to the OpenAI API. A suitable alternative must preserve the required task capabilities and operating controls, not simply accept a familiar client call.
Pricing differences
Start with the same input, expected output and request volume. Select the tier and deployment conditions that apply to the application, then distinguish input, cached input and generated output. A missing price is unknown; a documented zero is a separate case.
Add charges outside tokens: retrieval, hosted tools, media, storage or other operations used by the workflow. Compare the cost of a completed useful task, including failed attempts and correction passes.
Use the AI API cost calculator for an editable token workload. Record its assumptions before sharing an estimate so another reader can reproduce the comparison.
Limits and free tiers
A free offer, a funded balance and a rate limit answer different questions. Verify current eligibility on the actual account, inspect which model and operation it covers, and decide what happens when the prototype must stop.
Capacity belongs to the provider’s documented account scope and enforcement window. A request-per-second burst, a token budget and an asynchronous queue are not interchangeable measures. Use the provider limit guides and preserve the first rejection when diagnosing traffic.
Check free API access options for the related conditions and the selection wizard for a shortlist with explicit must-have filters.
Models and modalities
Compare exact identifiers and supported operations. Understanding an image does not imply generating one, and a provider that lists audio services does not make every text model suitable for live speech.
A context window is documented capacity. It does not establish the strongest model for an arbitrary task. Evaluate whether the candidate uses the relevant evidence, follows the required structure and handles a missing answer appropriately.
Use the model finder to narrow the catalog before comparing a pair. Keep unverified capabilities visible as unknown instead of turning a blank cell into an assumption.
Developer experience (SDKs, compatibility, docs)
OpenAI’s quickstart uses its native API and SDK conventions. A provider describing a compatible endpoint still needs its own credential, URL and supported-option checks. Official documentation.
Claude documents its own Messages interface, while routing services add provider-selection behavior. These are architectural differences that can matter more than sharing a Python package. Official documentation.
Save a minimal working request, an invalid-input case and a completion-state check for each integration. Preserve native errors behind a shared wrapper. Test tool actions and streaming cancellation before treating the migration as complete.
Which to pick for … (3 scenarios)
A first prototype. Choose a verified account offer and a model that supports the task directly. Keep the request small, the secret server-side and the experiment bounded by a clear budget.
A production assistant. Evaluate difficult real tasks and the surrounding failure handling. Select the model whose answers, latency and complete costs fit the product, then retain a reviewed fallback if needed.
An offline evaluation pipeline. Compare documented batch eligibility, pricing conditions and result collection. Choose a processing path that preserves item failures and lets you reconcile every completed job.
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Frequently asked questions
Does the table identify the strongest model?
Why are provider prices shown as ranges?
Can compatible providers use the same credential?
What does an unavailable value mean?
What should I save with a comparison?
Sources
- Oa Api documentation ↗
- Oa Price documentation ↗
- An Api documentation ↗
- Go Api documentation ↗
- Or Route documentation ↗
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