Claude Haiku 5.5 vs GPT-6 Luna: Pricing & API Guide

A developer-run API pricing guide compares Anthropic's Claude Haiku 5.5 and OpenAI's GPT-6 Luna, finding identical standard rates of $0.10 per million input tokens and $0.50 per million output tokens below their respective thresholds, but diverging sharply above them: Haiku reprices the whole request at $0.50 input and $2.50 output beyond 100K tokens, while Luna rises to $0.20 input and $0.75 output only past 272K tokens. The guide, which discloses its author develops the BeatAPI pricing service, argues that public benchmarks cannot pick a production winner for summarization, classification and extraction workloads, and that builders should validate each model against their own acceptance rules and tool loops.

Beat API Team · Specifications and prices checked October 9, 2026 For short summaries, classification, and extraction, evaluate both models against the same acceptance rules. For prompts between 100K and 272K tokens, Luna has the lower official token rates. For tool-driven work, first verify the endpoint and tool loop your application actually uses. A public benchmark can help select candidates, but it cannot decide the production winner for those three workloads. The Claude Haiku 5.5 vs GPT-6 Luna decision has four parts: Disclosure: We develop BeatAPI, which publicly lists both exact model IDs. This article distinguishes vendor specifications, a dated public retail listing, and illustrative calculations. We have not conducted a controlled quality or latency comparison, authenticated gateway canary, or billing-boundary experiment for this article. Both models target focused, high-volume work. Both accept text and image inputs and generate text. Haiku advertises a 1M-token context window; Luna advertises 1.05M. Both normally allow up to 128K output tokens. Those limits describe capacity, not retrieval accuracy or a guarantee that your route supports every feature. See the Haiku overview https://platform.claude.com/docs/en/models/haiku-5-5/overview and Luna model reference https://developers.openai.com/api/docs/models/gpt-6-luna . For application builders, the first comparison should look like this: | Job | What counts as accepted | Failure that a leaderboard will not catch | |---|---|---| | Summarize a customer thread | Correct commitments, dates, unresolved issues, source references | Turns a proposed deadline into an agreed deadline | | Classify a support ticket | Correct label under your taxonomy, appropriate abstention | Assigns a refund-policy exception to an ordinary billing queue | | Extract a document field | Correct value tied to an actual source span | Returns a plausible number from the wrong reporting period | | Choose a tool | Correct permitted tool and arguments, valid authorization context | Produces well-formed arguments for an action the user never authorized | | Search a long document | Correct evidence across relevant sections | Misses an exception separated from the main rule | Do not combine all of these into one average score and declare a winner. A model can be excellent at one field extraction while unreliable at interpreting commitments. Different acceptance rules imply different deployment decisions. The table below uses official standard API prices in USD per million tokens . It excludes Batch/Flex discounts, paid speed tiers, regional modifiers, and separately charged tools. Cache-writing entries compare published token rates; they do not imply identical cache lifetimes or cache-management behavior. | Token category | Haiku ≤100K | Haiku 100K | Luna ≤272K | Luna 272K | |---|---|---|---|---| | New input | $0.10 | $0.50 | $0.10 | $0.20 | | Cache read | $0.01 | $0.05 | $0.01 | $0.02 | | Cache write | $0.125, 5-minute | $0.625, 5-minute | $0.125 | $0.25 | | Output | $0.50 | $2.50 | $0.50 | $0.75 | The applicable tier reprices the full request rather than only the excess tokens. Anthropic explicitly includes cache reads and writes in Haiku's prompt length. Luna's model documentation specifies the whole-request increase above 272K input tokens. Sources: Anthropic pricing https://platform.claude.com/docs/en/about-claude/pricing , OpenAI pricing https://developers.openai.com/api/docs/pricing , and the Luna pricing conditions https://developers.openai.com/api/docs/models/gpt-6-luna . There are therefore three practical zones: Use model-specific token counts. The same document need not tokenize identically, and the models may generate different amounts of reasoning or visible output. “Same price” is a rate-card statement, not a bill prediction. The anonymous BeatAPI price endpoint https://api.beatapi.io/v1/pricing returned both claude-haiku-5-5 and gpt-6-luna on October 9: | Token category | BeatAPI Haiku: standard | Haiku: long context | BeatAPI Luna: standard | Luna: 272k plus | |---|---|---|---|---| | New input | $0.05 | $0.25 | $0.04 | $0.08 | | Cache read | $0.005 | $0.025 | $0.004 | $0.008 | | Cache write | $0.0625 | $0.3125 | $0.05 | $0.10 | | Output | $0.25 | $1.25 | $0.20 | $0.30 | Haiku's one-hour cache writes are separately listed at $0.10/$0.50. They are not part of the matched cache-writing row above. At short-context rates and equal token usage, Luna's listed price is 20% below Haiku's; conversely, Haiku costs 25% more than Luna. Between the vendor boundaries, the retail-rate ratio becomes 6.25×. Those percentages use different denominators—state which one you mean. This is verification of a public price book. We have not verified real cached requests, long-context settlement, output equivalence, uptime, or model-specific tool support through the gateway. The following retail calculations use the published tier rates with the vendors' boundaries as budgeting assumptions. Recheck current pricing https://beatapi.io/pricing before spending. If Haiku is a candidate for your workload, BeatAPI’s Claude Haiku 5.5 API page https://beatapi.io/claude-haiku-5-5-api provides its model ID, current token rates, and request examples for the first integration check. Start with a small sample of your own summaries or classification inputs, then apply the same acceptance rules to Luna; the price table alone is not a reason to choose Haiku. Use disjoint token buckets: cost = new input × input rate + cache read × read rate + cache write × write rate + billed output × output rate / 1,000,000 Output means the entire billed output count, including reasoning where applicable. Cache-hit scenarios below omit the earlier write expense. Equal token counts are an analytical assumption, not observed model behavior. | Assumed request | Official Haiku | Official Luna | BeatAPI Haiku | BeatAPI Luna | |---|---|---|---|---| | 2K new input + 500 output | $0.00045 | $0.00045 | $0.000225 | $0.00018 | | 100,000 new input + 2K output | $0.011 | $0.011 | $0.0055 | $0.0044 | | 100,001 new input + 2K output | $0.0550005 | $0.0110001 | $0.02750025 | $0.00440004 | | 150K new input + 2K output | $0.080 | $0.016 | $0.040 | $0.0064 | | 190K cache read + 10K new input + 2K output | $0.0195 | $0.0039 | $0.00975 | $0.00156 | | 272,001 new input + 2K output | $0.1410005 | $0.0559002 | $0.07050025 | $0.02236008 | | 400K new input + 5K output | $0.2125 | $0.08375 | $0.10625 | $0.0335 | These are reproducible arithmetic examples, not measured invoices. They exclude tools, validators, retries, and human review. The 200K cache-hit example illustrates an especially useful distinction: only 10K tokens are new, yet Haiku belongs to the higher tier because the cached prompt is still present. A dashboard showing only new input would hide that cause of the bill. The 400K example also shows why “Haiku costs 2.5× more above 272K” is incomplete. Input has that ratio, but output has a different ratio. The total is about 2.54× at official rates and 3.17× at the listed BeatAPI rates for this particular token mix. Take the 150K-input, 2K-output example. A direct Haiku request costs $0.080 at official rates; direct Luna costs $0.016. Suppose a different Haiku workflow uses three requests, each with 50K total input and 500 output , followed by a synthesis request with 5K total input and 1K output . The three extraction calls cost $0.01575; synthesis costs $0.001. Total: $0.01675 . At the listed BeatAPI rates, this becomes $0.008375 versus $0.0064 for direct Luna. The chunked Haiku workflow is cheaper than direct Haiku under these assumptions, but is still more expensive than direct Luna. It also changes the task: evidence crossing chunk boundaries may disappear, the synthesis adds another failure point, and total output differs from the direct request. Use chunking when the acceptance rubric permits independent extraction plus synthesis. Add overlap, repeated instructions, retrieval misses, retries, and final verification to the real budget. “Keep each chunk under 100K” is not sufficient to establish a better system. Let C H and C L be average total cost per incoming task for each candidate route, including failed attempts. Let s H and s L be their fractions of accepted tasks. The cost per accepted task is: Haiku: C H / s H Luna: C L / s L With nonzero acceptance rates, Haiku is cheaper per accepted result only if: s H / s L C H / C L For the short-request BeatAPI example, the cost ratio is 1.25. If Luna accepts 70% of tasks, Haiku must accept more than 87.5% to be cheaper per accepted result under these fixed token assumptions. If Luna already accepts 90%, Haiku cannot offset a 25% premium through acceptance rate alone: it would need more than 112.5% acceptance. For the 150K example, the retail cost ratio is 6.25. If Luna accepts 80%, an impossible 500% Haiku acceptance rate would be required to reverse that comparison through acceptance rate alone. These examples do not measure either model's actual acceptance rate. They explain why a general benchmark advantage cannot automatically justify a large task-specific price difference. Higher accuracy can still be worth paying for if errors have material costs, but then include those costs explicitly rather than calling the inference cheaper. A broader business estimate is: total cost = inference + tools + validation + rework + error cost For example, paying a few extra cents to prevent a document error that needs ten minutes of review can be rational. Conversely, a slightly better benchmark score has little value for a ticket classifier if both models already satisfy the operational acceptance threshold. Always report acceptance rate alongside cost per accepted result. Otherwise, rejecting most difficult tasks can make a route appear efficient while failing to serve users. Anthropic's Haiku announcement https://www.anthropic.com/claude-haiku-5-5 reports these shared rows: | Published evaluation | Haiku 5.5 | GPT-6 Luna | |---|---|---| | OSWorld 2.1, offline subset | 72.4% | 48.9% | | Terminal-Bench 4.0 | 39.2% | 16.4% | | FrontierCode 1.1 Main | 46.4% | 42.4% | | GDPval-AA v2.1 | 1620 | 1437 | These are vendor-published results, not our independent reproduction or a matched-budget experiment through BeatAPI. The OSWorld subset qualification matters. GDPval is an Elo-style score, not a success percentage. Neither result establishes your model's accuracy on a particular support taxonomy or document set. Use the table to include Haiku in an agent-work evaluation. Do not conclude that it will generate fewer tokens, need fewer retries, or finish sooner in your application. Those are separate measurements. A claim about being the fastest model within one vendor's range is also not a cross-vendor latency result. Reasoning settings with the same name do not guarantee equal compute or equal quality. A comparison at low on both systems is a useful configuration test. A second comparison tuning each system to the same acceptance target answers the production question more directly. Haiku uses the Messages request shape, with output config.effort . Luna's Responses request uses reasoning.effort . Luna's official model page recommends Responses for built-in tools and function calling; Chat Completions function calling is limited to reasoning effort: none . See the Luna reference https://developers.openai.com/api/docs/models/gpt-6-luna . For Haiku, the migration guide https://platform.claude.com/docs/en/models/haiku-5-5/migration-guide documents sampling-parameter restrictions, adaptive thinking, and assistant-prefill changes. Read answer blocks by type instead of assuming the first block is text. Reasoning can consume an output limit before the visible answer is complete. BeatAPI's gateway contract exposes /v1/messages and /v1/responses . Exposing those route names does not by itself establish feature parity with either vendor's hosted tools or guarantee a particular parameter will pass through unchanged. Validate the actual route with a small canary before enabling it for users. For a text-only classification pilot, use the same substantive prompt with separate adapters: Classify the ticket as billing, bug, feature, or needs review. Return only a JSON object with exactly label and evidence. Evidence must be a nonempty exact quote from the ticket. Use needs review when intent is missing or conflicting. Do not execute any action. Ticket: I was charged twice for the same invoice. Haiku request, sent to POST /v1/messages : { "model": "claude-haiku-5-5", "max tokens": 4096, "output config": {"effort": "low"}, "messages": { "role": "user", "content": "Classify the ticket as billing, bug, feature, or needs review. Return only a JSON object with exactly label and evidence. Evidence must be a nonempty exact quote from the ticket. Use needs review when intent is missing or conflicting. Do not execute any action. Ticket: I was charged twice for the same invoice." } } Luna request, sent to POST /v1/responses : { "model": "gpt-6-luna", "max output tokens": 4096, "reasoning": {"effort": "low"}, "input": "Classify the ticket as billing, bug, feature, or needs review. Return only a JSON object with exactly label and evidence. Evidence must be a nonempty exact quote from the ticket. Use needs review when intent is missing or conflicting. Do not execute any action. Ticket: I was charged twice for the same invoice." } These payloads are integration examples, not proof that either model obeys the requested format. Use your API key through the normal authenticated transport; never put credentials in a public article or evaluation artifact. Do not execute these requests merely to reproduce the offline budget tables—they are billable. Field shapes follow the vendor reasoning guidance https://developers.openai.com/api/docs/guides/reasoning and Haiku migration reference; authenticated gateway forwarding is untested here. For this tiny labeled fixture, the following gate rejects malformed output, an incorrect label, empty evidence, and invented quotes: python import json def accept raw text, ticket, expected label : try: answer = json.loads raw text except ValueError, TypeError : return False if not isinstance answer, dict or set answer = {"label", "evidence"}: return False return answer "label" == expected label and isinstance answer "evidence" , str and bool answer "evidence" .strip and answer "evidence" in ticket An exact quote is a grounding check, not proof of entailment. In the example, a quote about the invoice does not alone prove a duplicate charge. For a real extraction or summary task, the rubric must check that the cited passage supports the claim. The expected label comes from a held-out human-labeled test set; it is not available for arbitrary production tickets. This makes the boundary between evaluation and runtime validation explicit. Runtime schema checks can reject invalid format. They cannot magically know the correct answer. Use audited labels to estimate false acceptance and decide when manual review is needed. Before dispatching a model-generated call, check the tool name against an allowlist, validate argument types and ranges, confirm the referenced record exists, and preserve the application's authorization checks. Then evaluate whether the selected tool is semantically appropriate for the request. For a duplicate-charge ticket, “classify as billing” and “refund the payment” are different tasks. A perfect JSON refund call is still wrong if the application only requested classification. Use a read-only sandbox or mocked tool executor for the initial model comparison so you can grade the proposed actions independently of their execution. Build three separate held-out sets. Keep ordinary examples, ambiguous examples, and known failure cases in each. A small pilot is useful for fixing adapters and rubrics; it does not establish a very low production error rate. | Task family | Primary metric | Additional check | |---|---|---| | Summaries | Required-fact coverage with no unsupported commitments | Dates, named owners, unresolved conflicts, source support | | Classification | Per-class precision/recall and macro-F1 | Abstention rate, rare categories, misrouting severity | | Tool calls | Correct allowed tool and arguments | Authorization, abstention when inputs are missing, executor errors | Run the same task IDs through both models. Preserve prompts, source snapshots, schemas, output limits, model IDs, settings, and every attempt. Grade without model names where practical. Do not drop truncations or invalid JSON from the denominator. For each family, record: A useful final comparison has two views: the same nominal settings for both candidates, and the least expensive tested configuration that meets the same acceptance and latency targets. Keep the second view within the tested range; do not interpolate an unmeasured configuration into a claimed winner. You may end up choosing Luna for long-document extraction, Haiku for a particular short agent task, and either for a classifier. That is a valid result. The purpose is a defensible routing decision, not a universal ranking. The total bill combines per-token rates with the number of requests, prompt length, cache hits, and billed output. To audit a claimed cost gap, export each request's usage and tier; separate fresh input, cache reads, cache creation, and output. Compare the same source files, tools, stopping rule, and acceptance rubric in fresh workspaces. Otherwise, a model that reuses earlier generated files may appear cheaper for reasons unrelated to capability. An API-equivalent estimate from a subscription is also different from an actual API invoice. The worked budgets above hold token counts fixed; they explain pricing mechanics rather than reproduce a viral demo. This article has no controlled latency result. Measure time to first token for an interactive UI, then total time to an accepted result for an agent. Include tool waits, retries, and verification. A shorter answer can finish sooner while failing the task, and the same effort label need not mean the same compute budget. Their official short-context rates match for the categories compared here. At BeatAPI's checked short-context rates, Luna is 20% cheaper under equal token usage. Above 100K and through 272K, Haiku's higher tier widens that difference. Real task cost still depends on usage and acceptance. Test required-fact coverage and unsupported claims on your actual documents. A fluent summary that changes a commitment or drops an exception should fail, even if it is shorter and cheaper. Use your taxonomy, hard labels, rare classes, and an abstention policy. A broad knowledge benchmark is not evidence of correct routing in your support system. No. Anthropic explicitly counts cached input toward prompt length. The low number of newly supplied tokens does not describe the whole prompt. No. Count extraction, synthesis, overlap, retries, and verification. The worked chunking example remains more expensive than direct Luna and changes the information flow. Do not assume that. Keep endpoint-specific adapters and model-specific options. Verify tool support and output parsing on the exact route you will deploy. They can justify testing a candidate. To justify deployment, measure task-specific acceptance, latency, and rework. Use the cost-per-accepted-result inequality to assess the premium. The public retail listing was verified. The workload costs are arithmetic, and the request payloads are examples. No authenticated quality, latency, or boundary-settlement test is claimed. For Claude Haiku 5.5 vs GPT-6 Luna , start with one narrow workload, preserve its evidence, and measure what it costs to produce an answer you can actually use. Long-context pricing can choose the first candidate; a clear acceptance rubric should choose the production route.