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For prompts in the standard short-prompt tier, Claude Haiku 5.5 and GPT-6 Luna list the same API rates: $0.10 per million input tokens and $0.50 per million output tokens. That changes for longer prompts: Anthropic lists higher Haiku rates when a prompt exceeds 100,000 tokens, while OpenAI has a separate GPT-6 Luna long-context tier with different rates and tier rules. Both document a 128,000-token maximum output; their context windows are 1 million tokens for Haiku 5.5 and 1,050,000 for GPT-6 Luna. The right cost comparison depends on prompt length, cache use, output volume, and whether asynchronous batch processing works for your application—not just the headline short-prompt price.
What is the price difference between Claude Haiku 5.5 and GPT-6 Luna?
At the listed standard short-context rates, there is no difference in basic input or output price: each model costs $0.10 per million input tokens and $0.50 per million output tokens. The published prices diverge for longer prompts and cached tokens. The table summarizes each provider’s listed API rates; the tier rules are not identical, so do not treat the long-context columns as a like-for-like threshold comparison.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| API cost item | Claude Haiku 5.5 | GPT-6 Luna |
|---|---|---|
| Standard input, short prompt | $0.10 per million tokens for prompts up to 100,000 tokens | $0.10 per million tokens at standard short-context rates |
| Standard output, short prompt | $0.50 per million tokens for prompts up to 100,000 tokens | $0.50 per million tokens at standard short-context rates |
| Input, long prompt | $0.50 per million tokens when the prompt exceeds 100,000 tokens | $0.20 per million tokens in the documented long-context tier; the cited model page distinguishes tiers but does not state a cutoff here |
| Output, long prompt | $2.50 per million tokens when the prompt exceeds 100,000 tokens | $0.75 per million tokens in the documented long-context tier; the cited model page distinguishes tiers but does not state a cutoff here |
| Cached input/read | $0.01 per million tokens up to 100,000 prompt tokens; $0.05 above that | $0.01 per million tokens at short context; $0.02 at long context |
| Cache writes | The model page lists 5-minute and 1-hour write prices. For prompts up to 100,000 tokens, rates are listed up to $0.125 and $0.20 per million tokens, respectively; higher long-prompt rates apply | $0.125 per million tokens at short context; $0.25 at long context |
| Batch processing | 50% discount on input and output | Listed at 50% of Standard rates |
These are USD per million tokens from provider documentation checked in 2026. Anthropic’s Haiku 5.5 model documentation and pricing page describe the Haiku rates and cache terms. OpenAI’s GPT-6 Luna model page and API pricing page document its standard and long-context tiers. Rates and availability can change, so check the provider pages when planning a deployment.
For a request that stays in the standard short-prompt tier, the token rates alone do not distinguish these models. For example, one million uncached input tokens would be $0.10 and one million output tokens would be $0.50 at those rates, before cache treatment, batch pricing, tools, or other billable features. This is a rate illustration, not a claim about the total cost of a particular task.
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What triggers Haiku 5.5’s higher prompt price?
Anthropic states that if a Haiku 5.5 request’s prompt exceeds 100,000 tokens, the higher price applies to that request. The rule includes input billed as cache reads or cache writes, and requests are priced independently. In practical terms, a large cached prefix does not exempt a request from the higher tier if the prompt crosses the stated threshold.
This request-level rule matters when modeling repeated calls. A system that sends a long conversation history or large reusable context on each call should not estimate Haiku charges by applying the lower rate to cached portions and the higher rate only to uncached tokens. The documented tier applies to the request when its prompt exceeds 100,000 tokens, including cache reads or writes; price each request under the relevant rule.
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OpenAI documents separate short- and long-context pricing, but the rates and cutoff are not identical to Anthropic’s stated 100,000-token trigger. Use OpenAI’s current pricing definition to determine whether a particular request qualifies for its long-context tier; do not assume that Haiku’s 100,000-token rule applies to GPT-6 Luna.
The practical comparison is therefore conditional: when a request is in GPT-6 Luna’s documented long-context tier, its listed rates are $0.20 per million input tokens and $0.75 per million output tokens. Haiku’s higher tier is $0.50 input and $2.50 output per million tokens when the prompt exceeds 100,000 tokens. These figures do not, by themselves, establish which model will be cheaper for every long-context workload: each model’s eligibility rule, cached-token mix, and generated output must be applied to the actual requests.
How do I estimate API costs for my workload?
Estimate each billing category separately rather than multiplying all tokens by one blended rate. Count uncached input, cached input reads, cache writes, and generated output; then apply the correct context tier and processing mode for each provider. The formula below gives a basic estimate before any separately billed tools, service tiers, or other features.
Estimated token cost = (uncached input tokens ÷ 1,000,000 × input rate) + (cached input tokens ÷ 1,000,000 × cache-read rate) + (cache-write tokens ÷ 1,000,000 × cache-write rate) + (output tokens ÷ 1,000,000 × output rate).
- Measure representative requests. Record prompt sizes, cacheable portions, output lengths, and how often a prefix is reused. Use the workload you expect in production, not an unusually short demonstration prompt.
- Classify each request by provider tier. Apply Anthropic’s over-100,000-token prompt rule to Haiku requests. For GPT-6 Luna, consult OpenAI’s current definition of its long-context tier rather than inferring its cutoff from Haiku’s rule.
- Separate cache reads from cache writes. A cache read and creating a cache entry have different rates. For Haiku, include the documented cache duration—5 minutes or 1 hour—when selecting the applicable write price.
- Apply processing mode. If the work can run asynchronously, compare the documented 50% batch discount with the operational cost of waiting for batch completion. Do not use batch pricing for an interaction that needs an immediate response.
- Add other billable features and validate. Tools, service tier, or other API features may affect the actual total. Compare the estimate with the provider’s current pricing documentation before setting a budget.
A short-prompt calculation
Suppose a hypothetical workload uses 1,000,000 uncached input tokens, 100,000 cached input tokens, and 50,000 output tokens, with no cache writes, tools, or batch discount. At the listed short-prompt rates, either model’s simple estimate is $0.126: $0.10 for uncached input, $0.001 for cached reads, and $0.025 for output. This is an arithmetic example, not a benchmark or a forecast; a real request is billed according to its provider’s request-level tier rules and other applicable charges.
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The example separates categories so the calculation is easy to inspect: 100,000 cached tokens are one-tenth of a million, and at $0.01 per million that portion costs $0.001. The output portion is 0.05 million tokens multiplied by $0.50, or $0.025. If those tokens belong to a request that qualifies for a different tier, or if the cache write itself is billable, replace the example rates with the applicable ones. Do not use a blended monthly average to obscure those differences.
Why a monthly token total can mislead
Two applications can consume the same number of tokens and incur different bills. One may repeatedly send a reusable prefix that qualifies for cache reads; another may send mostly unique input. Long prompts can move requests to a different rate tier, and output tokens have their own rate. A useful forecast therefore models the distribution of prompt lengths and cache behavior, not only the monthly aggregate.
Build estimates from a range of request sizes: typical calls, unusually large retrieval or conversation contexts, and high-output cases. Track what is actually read from cache versus written, and how often the same prefix is reused. For Haiku, the 5-minute and 1-hour cache-write options make cache duration part of the cost calculation. The model comparison should reflect how your application uses caching, not just the cheapest listed cache-read number.
What are the context windows, output limits, and API options?
The maximum context window and maximum output describe different limits. Context is the amount of material the model can take into account in a request, while maximum output is the model’s documented ceiling for generated tokens. The larger context window listed for GPT-6 Luna does not mean it can generate more output than Haiku 5.5.
| Attribute | Claude Haiku 5.5 | GPT-6 Luna |
|---|---|---|
| Provider model ID | claude-haiku-5-5 |
gpt-6-luna |
| Context window | 1 million tokens | 1,050,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
| Input and output modalities documented | Text and image input; text output | Text and image input; the model page says audio and video are not supported |
| Documented model controls or details | Adaptive thinking; reliable knowledge cutoff listed as June 2026 | Reasoning-effort controls |
| Documented API access | Claude API; also listed through Amazon Bedrock, Google Cloud, and Microsoft Foundry | Responses API and Chat Completions, among other endpoints |
These capabilities are described in the providers’ respective Haiku 5.5 and GPT-6 Luna documentation. A maximum context or output figure is a model limit, not a promise that every request can use the full amount at a given price or that doing so is operationally appropriate.
The context figures are close in scale, but they are not interchangeable with the pricing thresholds. Haiku’s 1-million-token context capacity is not the same as its 100,000-token pricing trigger. Likewise, the 1,050,000-token GPT-6 Luna context window does not reveal the cutoff for its long-context pricing tier. Check both the model’s limits and the provider’s separate pricing definition before estimating the cost of a very large prompt.
API access can also affect implementation choices. Anthropic lists Haiku 5.5 through its Claude API and through Amazon Bedrock, Google Cloud, and Microsoft Foundry. OpenAI documents Responses API and Chat Completions, among other endpoints. The listed options are useful starting points for integration planning, but do not establish that every deployment path has identical account, region, or operational terms. Confirm the access path you intend to use with the relevant provider or cloud platform.
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For modalities, the documented pages describe text and image input for both models. Haiku 5.5 is documented with text output, while GPT-6 Luna’s model page says audio and video are not supported. If an application depends on those modalities, verify that these model pages meet the requirement before comparing token rates; a cheaper text call cannot replace a capability the workflow needs.
Which model makes more sense for a particular API use case?
The listed prices do not establish a universal winner. Both providers position these models for focused, high-volume tasks, but those are provider descriptions, not independent evidence that one is faster, more accurate, or cheaper for a particular application after all costs are included.
When most prompts are short
If your typical prompt stays within both providers’ standard short-prompt tiers, the listed input and output rates match. At that point, evaluate the API features your integration needs, expected cache reuse, output lengths, required modalities, and measured latency and task quality on your own representative workload.
For many short-prompt applications, the key cost variables may be the amount of output generated and the frequency of cache reuse rather than the small difference in context-window capacity. Model the number of calls as well as tokens per call, then run both candidates on the same examples. An application that emits long responses can spend more on output than one that uses the same input volume but returns concise results.
When prompts are long or cache-heavy
Calculate both models using their own tier definitions and cache rules. Haiku’s stated over-100,000-token prompt rule is explicit and applies to the request, including cached input. OpenAI lists distinct long-context rates, but do not map Anthropic’s threshold onto OpenAI’s tier. Include both cache reads and writes; comparing only cache-read rates can hide a meaningful cost difference.
Large contexts should be tested for operational need as well as price. A context window tells you the documented maximum capacity; it does not mean every request needs to include the entire conversation, document collection, or history. Sending only the material required for the current task can control cost and keep a request focused. Where repeated context is genuinely useful, measure the actual cache behavior and then model the appropriate cache-write and cache-read prices.
When your workload can run in the background
Both providers list a 50% batch discount on input and output. Batch can substantially change token cost when the task tolerates asynchronous processing, but it is not automatically suitable for user-facing requests that must return immediately. Check operational fit before treating the discount as savings your product can actually realize.
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Batch pricing is most relevant when the workflow can queue work and handle results later, rather than waiting for an immediate answer inside an interaction. Compare the discount with the product’s latency requirement and delivery process. The fact that a task is high-volume does not by itself make it batch-eligible in practical terms; the application still has to tolerate asynchronous completion.
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Anthropic positions Haiku 5.5 for high-volume, latency-sensitive classification, extraction, routing, and subagent tasks, and labels it the fastest in its comparison. OpenAI describes GPT-6 Luna as an efficient model for focused, high-volume tasks. Those claims are provider positioning; the official pages do not establish an independent head-to-head performance result. For a defensible choice, test both with the same representative prompts, quality rubric, concurrency, and latency conditions, and include the billable token pattern in the comparison.
For example, a classification evaluation should use the same sample and the same definition of a correct label for both candidates. An extraction test should score completeness and accuracy consistently; a routing test should use the same destinations and success criteria. Record response time and token counts as well as task outcomes. This approach makes a cost comparison meaningful without treating either provider’s marketing description as a neutral ranking.
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A small evaluation can prevent a headline-rate comparison from turning into an inaccurate deployment budget. Use a consistent sample and record both outcome quality and the inputs that drive cost.
- Prompt-size distribution: Track the share of requests that fall into each model’s documented pricing tier, including outliers and large retrieval or conversation histories.
- Cache behavior: Estimate how often the same input is reused, how much is read from cache, and how much cache writing occurs. Account for Haiku’s documented cache duration options.
- Output-length distribution: Measure typical and high-percentile generated token counts; the maximum output limit is not a suitable default output estimate.
- Task-specific quality: Define what counts as a correct classification, complete extraction, or useful routed result before testing. A lower token price is not savings if errors require expensive retries or human correction.
- Latency and concurrency: Measure under the response-time and traffic conditions your application actually needs. Provider positioning alone does not prove that one model will meet your service target.
- Modalities and integrations: Confirm that the documented text/image input and endpoint options fit the application. GPT-6 Luna’s model page does not list audio or video support; Haiku documentation lists text and image input to text output.
- Operational mode: Decide which jobs can be deferred to batch and which require synchronous responses. Price only the work that qualifies for the mode you can use.
- Price freshness: Recheck official pricing and model documentation before launch and when usage patterns or provider terms change.
When you build a side-by-side estimate, keep the assumptions visible: which model tier each request falls into, what share of input is uncached or cached, whether there are cache writes, how many output tokens are generated, and whether the request is processed in batch. Then run representative calls and compare those estimates with observed usage. A forecast that says only “millions of tokens per month” leaves out the details that distinguish the listed rates.
Are the provider performance descriptions a head-to-head result?
No. Anthropic’s language about Haiku 5.5 and OpenAI’s description of GPT-6 Luna are provider statements about intended positioning. They do not provide a neutral comparison of latency, quality, or cost on a shared workload, so the published prices and specifications are more directly comparable than broad performance claims.
For a fair evaluation, keep prompts, decoding settings where applicable, concurrency, and scoring criteria consistent. Track actual input, cached input, output, failures, retries, and response time. That gives you a workload-specific answer without implying that one model wins across tasks it may not have been tested on.
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Include retries and corrections in the evaluation because token price is only one part of the cost of completing useful work. If one system requires additional calls to reach an acceptable result, those calls change the actual input and output totals. Conversely, a model that produces a longer answer may consume more output tokens even if the first-call rate is the same. The useful comparison is the cost and quality of the completed task under your application’s conditions, not a provider’s general positioning or a theoretical maximum context size.
Frequently Asked Questions
Can I compare a provider’s price per million tokens with my app’s monthly API bill directly?
Not by itself. The monthly bill can include non-token charges or billable features, and the effective token cost depends on request tier, cache reads and writes, and processing mode; use the provider’s invoice and current pricing page to reconcile a forecast.
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Does a 1-million-token context window mean I should send the whole conversation every time?
No. Context capacity is an upper limit, not guidance to maximize each request. Sending unnecessary history can add cost and may make it harder to keep the model focused on the current task.
Do the listed model prices guarantee that the model is available in my cloud region?
No. A model page or API price list does not establish availability in every region or under every account configuration. Confirm access and regional terms with the provider or cloud platform you plan to use before building around a specific model ID.
Which model is cheaper for API use?
For standard short-prompt input and output, both list $0.10 and $0.50 per million tokens, respectively. Long-context rates differ, and each provider applies its own tier rules. Cache behavior, output volume, and batch eligibility also affect the total, so compare representative requests using current provider pricing.
How does Haiku 5.5’s 100,000-token threshold affect cached input?
Anthropic says a Haiku request whose prompt exceeds 100,000 tokens is charged at the higher rate for that request, including when some input is cache reads or cache writes. Requests are priced independently.
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Both providers list a 50% batch discount on input and output. Batch is asynchronous, so the discount is relevant only when the application can accommodate deferred processing.
Do both models support image input, and can GPT-6 Luna process audio or video?
The documented model pages list text and image input for both. GPT-6 Luna’s model page says audio and video are not supported; Haiku 5.5 is documented with text output.
Do the models have the same maximum output?
Yes. Both model pages document a maximum output of 128,000 tokens, although their context windows differ: 1 million tokens for Haiku 5.5 and 1,050,000 for GPT-6 Luna.
The Bottom Line
Claude Haiku 5.5 and GPT-6 Luna have matching listed short-context rates, but their long-context pricing rules, cache terms, and API options differ. Estimate costs using real prompt lengths, cache reads and writes, output volumes, and processing mode; then test quality and latency on the same workload. Neither provider’s positioning establishes a universal winner.
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