GPT-6 Luna vs Claude Haiku 4.5: API pricing and context limits
Luna's base token rates are 90% lower. Haiku can still be worth keeping when your tested Claude workflow saves enough review, retries, or migration work.
Luna's base token rates are ten times lower. Task quality and integration decide whether switching pays off.
GPT-6 Luna and Claude Haiku 4.5 are useful candidates to test for frequent, focused requests. Luna's base input, output, and cache-read rates are one tenth of Haiku's. That makes the starting token bill easy to compare. Deciding whether to switch providers takes more work: response quality, prompt behavior, tool handling, and integration effort all affect the cost of a result your application can actually use.
By TechCompare · Updated
Cost Comparison
Based on 100,000 input tokens (50% cached), 5,000 output tokens, and 100 requests.Prices are fetched live from OpenRouter and may include temporary promotional discounts not accounted for in our article and comparison figures.
Side-by-side specs
| Spec | GPT-6 Luna | Claude Haiku 4.5 |
|---|---|---|
| Standard input per 1M tokens | $0.10 up to 272K input (better on this spec) | $1.00 |
| Standard output per 1M tokens | $0.50 up to 272K input (better on this spec) | $5.00 |
| Cache reads per 1M tokens | $0.01 up to 272K input (better on this spec) | $0.10 |
| 20K input + 2K output, no cache hits | $0.003 (better on this spec) | $0.03 |
| 20K input + 2K output, 50% cached input | $0.0021 (better on this spec) | $0.021 |
| 100K input + 5K output, 50% cached input | $0.008 (better on this spec) | $0.08 |
| Context window | 1,050,000 tokens | 200,000 tokens |
| Maximum output | 128,000 tokens | 64,000 tokens |
| Thinking controls | Reasoning effort, including none | Optional manual extended thinking |
| Provider Batch API discount | 50% | 50% |
How they differ
Luna charges $0.10 input and $0.50 output per million tokens, compared with Haiku's $1 and $5. Cache reads cost $0.01 and $0.10. For 20,000 input and 2,000 output tokens, the uncached estimates are $0.003 and $0.03. With half the input read from cache, they become $0.0021 and $0.021. Both providers offer a 50% Batch API discount. Capacity also differs: Luna supports a 1,050,000-token context window and up to 128,000 output tokens, while Haiku supports 200,000 and 64,000. Haiku therefore can't take the same full 300,000-token input used in Luna's long-prompt examples. These are token-only estimates without cache-write or tool charges. The embedded calculator uses live OpenRouter rates, which can vary with the selected provider route.
Verdict
Evaluate both on the same real requests before moving traffic. Use a mix of ordinary cases and known failure cases, then measure valid results, response times, and total billed usage. Luna has the lower published rates and larger context capacity. Haiku needs a practical advantage in your application to offset its higher bill. Don't infer a cross-provider speed or quality winner from the names or rates, and don't assume the same text produces identical token counts.
Which should you pick?
Choose GPT-6 Luna
Choose Luna when it passes your checks on high-volume work and the savings cover the effort of integrating it. Try the prompts and tools your application actually uses, including messy inputs and edge cases. Its larger context window can reduce the need to split a large input, but first test whether the model finds the relevant information reliably and whether sending all that text improves the result.
Official Luna model referenceChoose Claude Haiku 4.5
Choose Haiku when your Claude integration already delivers reliable results and a provider change offers too little benefit after implementation and review costs. It's also a reasonable candidate when your evaluation favors its behavior on your specific prompts. Keep the full request within its context capacity, leaving room for generated output. Recheck any long conversation or large tool response before assuming that only the current user message matters.
Official Haiku model reference