Which model should you choose?
Start with the provider already integrated into your application when both options meet your quality target. A migration should earn its engineering cost through measured improvements in quality, reliability or spend.
For equal uncached token quantities, short-input list prices tie. Haiku's higher schedule begins above 100,000 input tokens; Luna's begins above 272,000. A cheaper token rate alone does not establish a cheaper completed task: measure generated output, retries and provider-specific input counts.
Context and output limits
Check these limits before budgeting a request. The context window is not an allowance for input plus an unlimited output.
| Specification | Claude Haiku 5.5 | GPT-6 Luna |
|---|---|---|
| API model ID | claude-haiku-5-5 | gpt-6-luna |
| Context window | 1,000,000 tokens | 1,050,000 tokens |
| Maximum standard output | 128,000 tokens | 128,000 tokens |
| Local tokenizer support | Reference-only | Reference-only |
Calculate Haiku vs Luna API costs
Enter input tokens separately for each model, planned output tokens per call and calls per month. Defaults use 10,000 input tokens for each model, 1,000 output tokens and 10,000 calls: $0.0015 per call and $15 per month each.
This comparison uses Standard, uncached first-party requests. Output is a shared planning allowance, including billed reasoning where applicable. Cache, Batch, regional processing, tools, taxes and subscriptions are excluded. For caching scenarios, open the related provider calculators. Costs do not validate request-size limits.
Pricing by input tier
USD per million tokens, checked October 9, 2026. The full request selects a tier; cached input also contributes to its input length. Each price links to an official source.
Worked examples at the pricing boundaries
Equal input counts, 1,000 output tokens per request and 10,000 monthly calls. These examples illustrate tier changes, not equal-text measurements.
| Input tokens / model | Haiku / call | Haiku / month | Luna / call | Luna / month |
|---|---|---|---|---|
| 10,000 | $0.0015 | $15.00 | $0.0015 | $15.00 |
| 100,000 | $0.0105 | $105.00 | $0.0105 | $105.00 |
| 100,001 | $0.0525005 | $525.005 | $0.0105001 | $105.001 |
| 272,000 | $0.1385 | $1,385.00 | $0.0277 | $277.00 |
| 272,001 | $0.1385005 | $1,385.005 | $0.0551502 | $551.502 |
Performance and speed: test your actual tasks
This page does not report a head-to-head benchmark or declare a universal winner. Choose a fixed set of representative prompts and score both models against the same acceptance criteria: correct extraction, valid JSON, grounded answers or passing code checks.
Record time to first token, full response time, billed input and output, retry rate and cost per accepted result. Keep effort settings, tool access, output allowances, concurrency and region recorded. Repeat requests and compare median and tail latency rather than one response. Different provider benchmark conditions do not establish a fair ranking.
Compare token counts fairly
Send each provider the complete supported request, including system instructions, conversation history, tools and media. Use Anthropic's count_tokens estimate for Haiku and OpenAI's input-counting endpoint for Luna. Reconcile both with returned usage after execution.
Enter those model-specific counts in the calculator. Do not use a raw o200k_base count to choose either billing tier. If generated output differs substantially between models, calculate each measured output separately in the linked provider tools.
| Provider | Counting reference |
|---|---|
| Anthropic | Claude token counting |
| OpenAI | OpenAI input token counting |
Tiktokenizer Reference Sources
Tokenizer behavior and model limits change. Verify production decisions with current provider documentation: