Haiku 5.5 vs Luna 6

Haiku 5.5 vs Luna 6 comes down to prompt length, API fit and the cost of getting a correct result. Here, “Luna 6” means OpenAI's GPT-6 Luna, model ID gpt-6-luna; Haiku is Anthropic's Claude Haiku 5.5, claude-haiku-5-5. Their direct standard input and output rates match for prompts up to 100K tokens. Beyond that, Luna's lower long-context bill becomes a reason to evaluate it first.

On OmniaKey's catalog quotes, Haiku is cheaper for short requests, while Luna is cheaper above Haiku's 100K threshold for equal billed token counts. Choose within the API your application supports, then compare accuracy and total cost on the same tasks. Price alone cannot identify the better coding model.

Haiku 5.5 vs Luna 6 at a glance

Sources: the Haiku model overview and GPT-6 Luna model page. Context is capacity, not included credit or proof of equal recall. The same medium label also does not imply equal reasoning work across providers.

Official API prices: equal below 100K, different above it

The table uses USD per million tokens, in the order uncached input / billed output / cache read. It compares Anthropic's standard direct API with OpenAI Standard processing, without Batch discounts or regional premiums.

Anthropic's pricing documentation explicitly includes cache reads and writes when calculating Haiku's prompt length. Once a request exceeds 100K input tokens, it uses the higher prices. OpenAI applies its higher rates to the full request above 272K input tokens. Exactly 100K and exactly 272K remain in the corresponding lower bands.

The difference matters for agents that repeatedly send repository files, tool definitions or conversation history. A large cache hit lowers the input charge but does not put Haiku back into its short-prompt tier. Count tokens for each model: equal text need not produce equal token counts.

Worked costs for short and long requests

Assume one uncached request with the stated input and billed output, including any reasoning billed as output. These examples exclude tools, cache writes, storage, taxes, retries and human review.

For the direct 150K example, Haiku costs 0.15 × 0.50 + 0.02 × 2.50 = 0.125 USD; Luna costs 0.15 × 0.10 + 0.02 × 0.50 = 0.025 USD. That is a fivefold rate-card difference for this token workload, not a fivefold quality or speed difference.

For a cached 150K prompt consisting of 140K cache-read tokens, 10K new input and 20K output, the direct totals are $0.062 for Haiku and $0.0124 for Luna. Haiku's calculation is 0.14 × 0.05 + 0.01 × 0.50 + 0.02 × 2.50 = 0.062. Luna's is 0.14 × 0.01 + 0.01 × 0.10 + 0.02 × 0.50 = 0.0124. The earlier cache-write cost is excluded.

OmniaKey prices use a separate rate card

The gateway columns above use the following catalog snapshot, also in USD per million tokens. These are the site's listed quotes; a catalog check is not a new inference test.

Haiku's listed quote is 80% of its Anthropic rate card; Luna has an explicit flat gateway quote. Luna's OmniaKey quote is higher than OpenAI's direct Standard rates. Do not apply a general provider discount badge to this model or assume gateway and direct prices match. Recheck the Haiku 5.5 model page and GPT-6 Luna model page before buying; prices and account access can change.

For this snapshot, Haiku is the cheaper gateway starting point when prompts remain at or below 100K. Above that threshold, Luna has lower input, output and cache-read rates. Actual accepted-task cost still depends on model-specific token usage and retries.

Coding, extraction and speed: what to evaluate

Anthropic's launch table reports these results for both models. They are vendor-published scores; we have not reproduced the evaluation harnesses, effort settings or runs.

Haiku scores higher on these three reported evaluations. That supports including it in a coding and tool-use pilot, but does not establish faster completion or cheaper accepted tasks. The harness and settings matter; the earlier price examples are separate cost scenarios.

For small coding changes, keep the repository revision, tools and acceptance tests fixed. Test a mechanical refactor, a failing-test repair and a constrained feature separately. Use Haiku first when the existing integration already speaks Anthropic Messages; use Luna first when it already speaks Responses. This is an integration-cost recommendation, not measured coding superiority.

For extraction and classification, both providers describe high-volume use cases. Validate field values and source evidence as well as JSON syntax. Include ambiguous inputs and an explicit unknown outcome so a cheap but wrong answer does not silently pass.

For long-document or repository work, Luna's direct price advantage above 100K is measurable from the rate card. Whether it finds the relevant evidence more reliably requires a retrieval test. A larger context window alone does not establish that result.

Anthropic's within-family “fastest” label and OpenAI's “Fast” label do not measure the same workload. Compare time to first token, full completion time and p95 latency on your route, with output length and reasoning settings recorded. The linked sources do not establish a matched cross-provider speed winner.

API and migration differences

Haiku's native route is Messages; Luna's reasoning-and-tools route is Responses. Changing only the model string will not translate request bodies, tool events or response blocks between them.

• Haiku: adaptive thinking is enabled by default. Use output_config.effort, parse content blocks by type, and remove Haiku 4.5 assistant prefills and old sampling settings according to the migration guide.
• Luna: Responses supports reasoning.effort values none, low, medium, high, xhigh and max. Its model page permits Chat Completions function calling only with reasoning_effort set to none; use Responses when combining reasoning and tools.

The Haiku guide's approximately 30% tokenizer increase is relative to Haiku 4.5, not to GPT-6 Luna. Recount each model's actual usage rather than adding that percentage to a cross-provider comparison. For full individual contracts, read our Haiku 5.5 review and GPT-6 Luna review.

Cache and Batch savings need the same assumptions

Haiku's five-minute cache writes cost $0.125 or $0.625 per million by prompt tier; its one-hour writes cost $0.20 or $1.00. Luna's listed cache-write rates are $0.125 or $0.25. A cached comparison that omits the initial write only describes later reads.

Both providers publish a 50% discount on applicable Batch input and output. OpenAI also lists Flex at half Standard rates. Those service modes have separate behavior and eligibility; neither their availability nor their discounts automatically carry over to a gateway. Compare Batch with Batch, and standard synchronous work with standard synchronous work. Sources: Anthropic pricing and OpenAI pricing.

A practical selection rule

Start with a proposed pilot of 30 representative tasks, including both short prompts and requests above 100K. This is a suggested sample, not a benchmark we ran. Pin the model IDs, provider route, prompts, tools, effort settings and retry limit. Keep the pass criteria identical, while recording each model's own billed tokens.

Calculate total attempt and review cost divided by accepted tasks, then inspect undetected errors separately. If both pass on short prompts, API fit and the applicable route price decide the starting point. For long prompts, evaluate Luna's lower rate against any difference in errors, latency and correction effort. Escalate difficult work to a stronger model when both fail the acceptance check.

Frequently asked questions

Is Luna 6 the same as GPT-6 Luna?

In this comparison, yes. The official OpenAI name is GPT-6 Luna and the API ID is gpt-6-luna; do not send luna-6 as the model ID.

Which is cheaper: Haiku 5.5 or Luna 6?

Direct standard rates match up to 100K input tokens; Luna's listed rates are lower above that. In the OmniaKey snapshot, Haiku costs less up to 100K and Luna less above it, for equal billed token categories.

Which is better for coding?

The sources reviewed do not establish a universal winner. Evaluate the same coding tasks with fixed tools and executable checks, and compare total cost per accepted change. A valid response or lower token price is not proof that the code is correct.

Can cached input avoid Haiku's 100K tier?

No. Anthropic counts cache-read and cache-write tokens toward prompt length. A 140K cache hit plus 10K new input is still a 150K prompt and uses the higher Haiku rates.