Claude Haiku 5.5 vs GPT-6 Luna

Haiku 5.5 vs Luna at a glance

Both models accept text and images and return text. Both offer adjustable reasoning effort. That makes them plausible candidates for summaries, classification and extracting fields; it does not make their answers or their surrounding products interchangeable.

Specifications and standard token rates were checked on October 8, 2026 against the Haiku model specification and Luna model specification. Context capacity is a model limit, not an upload allowance in HaikuChat. This workspace has smaller task limits.

Equal starting prices do not mean equal task costs

For an uncached request with 20,000 input tokens and 2,000 total billable output tokens, either model's base rates produce a $0.003 model-token estimate. This arithmetic assumes the same token counts; it is not a HaikuChat purchase price or a measured task result.

Long inputs change the comparison. Haiku's higher tier applies to the entire request above 100,000 input tokens, at $0.50 input and $2.50 output per million. Luna's documented increase begins above 272,000 input tokens, multiplying input/cache rates by two and output rates by 1.5.

At 120,000 input and 2,000 billable output tokens, those published rules give $0.065 for Haiku and $0.013 for Luna before caching, discounts, tools or regional adjustments. A statement such as “they cost the same” therefore needs an input-length qualification.

For a real task, record each model's own token counts, reasoning effort, retries and usable result rate. Different tokenizers and answer lengths make equal text different from equal billed tokens. See Haiku pricing details and Luna pricing details for the underlying tiers. HaikuChat credit packs describe the separate cost of using this workspace.

What the published benchmarks tell you

The October 7 Haiku release includes these comparisons:

These are publisher-reported results from the Haiku release evaluation, not tests run by HaikuChat. Read the evaluation setup, effort settings and tooling before treating the gaps as comparable to your own workload.

The figures support putting Haiku on a shortlist for further testing. They do not establish its accuracy on your invoices or the time it will take to answer your questions. Computer-use and terminal benchmarks also do not imply that this browser workspace operates your computer or executes commands.

Choose around the work you actually repeat

Summarizing notes: use the same meeting notes and require owners, deadlines and unresolved decisions. A concise answer that silently turns an undecided date into a commitment should fail, even if it reads smoothly. Prefer the model that preserves those distinctions with less editing.

Extracting fields: supply an invoice with a missing purchase-order number and request a fixed table. Check exact amounts, currencies, dates and whether the missing value stays missing. Formatting alone is not correctness. If Haiku returns a usable table, there is little value in switching just to match someone else's benchmark preference.

Long document collections: Luna's later price threshold is relevant when the input really crosses 100,000 tokens. First ask whether you need the entire collection. Retrieving the relevant passages can change the cost comparison more than choosing between the two names.

Existing workflows: if Luna already produces acceptable results in a tool you rely on, switching introduces a new evaluation and integration task. If you mainly want a straightforward Haiku chat, summary or table workflow, open HaikuChat and check its current connection status before starting.

A small comparison you can reproduce

Prepare ten examples drawn from your normal work. Include a straightforward case, an ambiguous case and one with genuinely missing information. Write the expected fields or essential facts before seeing either answer.

Keep the source material and requested output consistent, record effort settings, and score the outputs without their model labels. Track factual mistakes, useful completions, editing required, time to a complete answer and billed usage. Keep failed attempts in the denominator.

Choose Haiku when it meets the quality threshold and fits the workflow. Choose Luna when it handles your material better or its long-input economics matter. If both fail the same ambiguous task, compare a different capability tier in Haiku vs Sonnet or Haiku vs Opus, rather than repeatedly retrying the same request.

Questions about this comparison