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Neither pricing model is always cheaper. Token billing usually wins for short, tightly controlled prompts and responses, especially when prompt caching is effective. A fixed per-request fee can cost less when every call carries a long prompt, a large response, or substantial reasoning and the request price is below that call’s token-equivalent cost. The reliable answer comes from measuring your own production workload, then comparing cost per successful task rather than headline rates.
What token-based and request-based pricing actually charge
Token-based pricing meters the work inside each call
A token is a unit of text processed by a model. Token-based plans multiply each billable category by its published rate. The categories can include ordinary input tokens, cached input tokens, output tokens, and—depending on the provider and model—reasoning or intermediate tokens.
OpenAI’s documented calculation is:
Token charge = (input tokens / 1,000,000 × input rate) + (cached input tokens / 1,000,000 × cached-input rate) + (output tokens / 1,000,000 × output rate)
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Request-based pricing charges for the invocation
Request pricing applies a fixed amount to each API call, tool invocation, or operation. The charge is normally the same whether a bounded request contains a short prompt or a longer one, provided it remains within the product’s limits. That makes budgeting simple when payloads are tightly standardized. It can also overcharge tiny calls and undercharge unusually large calls because the fee does not track the amount of model computation as closely as token billing.
Hybrid plans combine both meters
Many real services are hybrids. A tool or operation may have a per-call fee while the underlying model still bills input, output, and reasoning tokens. Storage, retrieval, image, audio, or other feature fees may be separate as well. Treat a hybrid as its own pricing model; never compare only the request component with a token-only alternative.
The cost equations you need
Token-priced workload
For each request, use:
(input_tokens × input_rate + cached_input_tokens × cached_rate + output_tokens × output_rate + reasoning_or_intermediate_tokens × applicable_rate) / 1,000,000 + tool_fees + fixed_fees
Use the rate’s own unit and currency. If a provider quotes different prices for regions, batch processing, cache writes, or cache hits, select the rate that matches the actual request. Do not fold cached tokens into ordinary input tokens: a cache hit can be materially cheaper, while a cache write may have its own charge.
Request-priced workload
For a fixed-fee service, calculate:
number_of_requests × price_per_request + token_fees + tool_fees + storage_fees + overage_fees
The additional terms matter because a product advertised as “per request” may still meter model inference, premium tools, storage, or usage above a quota. Confirm whether retries, failed calls, and asynchronous jobs count as requests before forecasting spend.
Compare successful work, not just submitted calls
A request that times out, produces an unusable answer, or must be retried still consumes resources. Divide total spend by completed, accepted tasks:
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cost per successful task = total charges / successful tasks
Track latency and throughput alongside cost. A nominally cheaper model can be more expensive operationally if its slower responses trigger timeouts, duplicate calls, or manual review.
How to find the break-even point
Set up the two totals
Let N be the number of requests in a billing period, R the fixed price per request, and T the average fully loaded token-and-feature cost per request. The request model costs N × R; the token model costs N × T. The simple break-even condition is:
R = T
If the request model also has token or tool charges, use its complete per-request total rather than the advertised invocation fee. For a workload with several request types, calculate the weighted total:
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total token cost = Σ(request type count × fully loaded token cost for that type)
total request cost = Σ(request type count × fully loaded request cost for that type)
Worked token-cost example using a published rate
OpenAI’s 2026 published GPT-6 Luna example lists $0.10 per 1,000,000 input tokens, $0.01 per 1,000,000 cached input tokens, and $0.50 per 1,000,000 output tokens. Suppose one illustrative call contains 20,000 ordinary input tokens and 2,000 output tokens, with no cached portion or additional feature fee:
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- Input:
20,000 / 1,000,000 × $0.10 = $0.002 - Output:
2,000 / 1,000,000 × $0.50 = $0.001 - Total:
$0.003per call before any other charges
At that measured payload, a request-priced alternative charging more than three-tenths of a cent per equivalent call would be more expensive on the model-only portion; a lower request fee would be cheaper. This is an illustration of the method, not a universal request price or a promise that every Luna call has those token counts.
Use distributions instead of one average
Averages hide the calls that dominate a bill. Export at least these percentiles and rates from production:
- p50 and p95 input-token counts
- p50 and p95 output-token counts
- Reasoning or intermediate-token exposure, when reported
- Cache-hit percentage and cache-write frequency
- Tool calls per task, including retrieval and function invocations
- Retry, timeout, and failed-task rates
- Tasks completed successfully and the latency distribution
Run the break-even calculation separately for typical (p50), heavy (p95), and burst periods. A fixed fee may look attractive at p95 while wasting money on the large volume of short p50 calls, or the reverse.
Include operational constraints
Apply batch discounts, regional or data-residency premiums, rate limits, and minimum commitments exactly as they occur in your deployment. If a cheaper batch rate is unavailable for interactive traffic, it cannot be used in the interactive comparison. If a request limit forces you to split one task into several calls, count every resulting invocation and token stream.
Published price examples and what they imply
The following figures were published or accessed in 2026. They are examples for the stated model, region, and mode—not timeless rankings.
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| OpenAI GPT-6 Luna | $0.10 | $0.01 | $0.50 | OpenAI published figures, 2026; other feature charges may apply. |
| OpenAI GPT-6 Astra | $10.00 | $1.00 | $50.00 | OpenAI published figures, 2026; use the model’s own tokenizer and settings. |
| Claude Sonnet 4.6 on Google Vertex AI, global standard | $3.00 | Listed separately by Anthropic | $15.00 | Anthropic’s 2026 Vertex AI listing; cache-write and cache-hit rates are separate. |
| Claude Sonnet 4.6 on Google Vertex AI, batch row | Half the displayed global-standard input rate | Not stated for this comparison | Half the displayed global-standard output rate | Anthropic’s cited PDF shows batch processing at half the global-standard rates for that row. |
These numbers demonstrate why a single “price per million tokens” headline is insufficient. GPT-6 Astra’s output rate is 100 times GPT-6 Luna’s output rate in the examples, so a workload that generates long answers can change model economics dramatically. The Sonnet 4.6 batch row may be attractive for eligible offline jobs, but its discount does not automatically apply to interactive requests. Always verify the current regional, cache, batch, and model-specific schedule before committing spend.
Why your bill can jump when request volume does not
Prompts became longer
Conversation history, retrieved documents, system instructions, and tool results are all input. A stable count of API calls can therefore hide a rapidly growing input-token total. Logging the token count at request time exposes this change.
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Responses or reasoning became longer
Longer output directly increases token charges. A model or setting that performs more hidden reasoning can also increase intermediate-token usage where that category is billed. OpenAI warns that a lower price per million tokens does not necessarily produce a lower total cost because models can tokenize the same text differently and generate different amounts of output or reasoning. Test representative tasks rather than comparing only visible response length.
Cache performance deteriorated
When repeated prefixes stop matching, cached input becomes ordinary input or incurs a cache-write charge. Changes to system prompts, message ordering, dynamic timestamps, or context truncation can reduce hit rates without changing request count.
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A single user action may trigger retrieval, function calls, a model follow-up, and a final response. Timeouts and automatic retries add more invocations. In managed-agent systems, intermediate reasoning tokens and tool fees can be charged even when the final answer is short.
Deployment settings changed
Switching models, regions, tokenizer versions, context windows, reasoning settings, batch eligibility, or data-residency options can change rates and token counts. Record these settings with each cost sample so a billing change is explainable.
When token pricing is usually the better fit
- Short, constrained interactions: A classifier, extractor, router, or structured response with strict output limits uses few billable tokens.
- High cache reuse: Stable instructions or reference material can move a large fraction of input into a lower cached-input rate.
- Wide request-size variation: Token billing follows actual work, so tiny calls do not subsidize very large ones or vice versa.
- Enforceable budgets: Maximum input length, output-token limits, truncation rules, and per-user quotas make spend predictable enough for your risk tolerance.
Token pricing is not automatically economical for long prompts. Measure output and reasoning as carefully as input, and include every tool charge.
When request pricing is usually the better fit
- Predictable payloads: Every invocation carries nearly the same bounded prompt and response.
- Simple unit economics: Product teams need to quote a fixed cost per operation and the provider’s fee is below the measured token-equivalent cost.
- Large or variable responses: A fixed fee can cap exposure when occasional calls are much larger than normal, provided the service does not impose hidden overages.
- Administrative simplicity: Accounting values a stable per-operation charge more than fine-grained optimization.
Request pricing can become wasteful when many calls are trivial or when a workflow fans out into multiple hidden operations. Confirm what the provider considers one request.
How to measure your workload before choosing
- Define a successful task. Specify the output that a user or downstream system accepts, including quality checks and any human-review threshold.
- Instrument every model and tool event. Record request ID, model, region, batch mode, input tokens, cached input tokens, output tokens, reasoning or intermediate tokens when available, tool fees, latency, status, and retry count.
- Classify workload shapes. Separate chat, extraction, summarization, agentic workflows, batch jobs, and background tasks. Their token and tool distributions are rarely alike.
- Measure cache behavior. Log cache writes and hits, the reusable prefix, and the percentage of input receiving each rate.
- Calculate p50 and p95 costs. Use the equations above for each workload class, then aggregate by monthly volume rather than averaging all tasks together.
- Model failure and retry paths. Add the cost of timeouts, validation failures, duplicate submissions, and human-review escalations.
- Compare alternatives on identical tasks. Keep the prompt, acceptance test, latency target, and region constant. A model that returns shorter text but fails quality checks is not cheaper per successful task.
- Stress-test growth. Recalculate at current volume, expected growth, a cache-hit decline, and a p95-heavy month. This reveals whether the decision is robust or depends on ideal conditions.
Practical controls that keep either model economical
Control token volume
- Set explicit maximum output tokens and stop conditions.
- Trim redundant conversation history and retrieved passages.
- Use structured output schemas to prevent verbose explanations where they are unnecessary.
- Route simple tasks to a lower-cost model and reserve expensive reasoning for cases that need it.
Protect cache efficiency
- Keep reusable instructions in a stable prefix.
- Move volatile data, timestamps, and user-specific text after the reusable content when the provider’s cache rules permit.
- Monitor cache-hit and cache-write rates after every prompt-template change.
Control request multiplication
- Set bounded tool-call loops and maximum retries.
- Use idempotency keys so a network retry does not create duplicate work.
- Cache deterministic retrieval results and avoid repeating the same tool call inside one task.
- Alert on sudden increases in calls per successful task, not only on total request count.
Control budget risk
- Apply per-user, per-tenant, and per-workflow quotas.
- Alert on p95 token growth, cache-hit deterioration, and tool-call spikes.
- Review model, region, tokenizer, context-window, and reasoning-setting changes as cost-impacting deployments.
Common comparison mistakes
Comparing list prices from different units
One provider may quote input and output separately, another may include intermediate tokens, and a third may advertise a request fee before tool or storage charges. Normalize every figure to the same successful task and billing period.
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Using average tokens alone
Averages conceal long-context outliers and retry storms. Include p95 values and the distribution of task types.
Assuming visible output equals total generation
Reasoning or intermediate tokens may be billed even when they are not displayed. Ask what the service counts and include that category in the equation.
Assuming cache savings are permanent
Cache rates depend on eligibility, prefix stability, and retention rules. A template change can move cached input back to the standard rate.
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Ignoring regional and batch qualifications
A regional premium or batch discount applies only under its stated conditions. Do not use an offline discount to forecast interactive traffic.
Choosing the cheapest nominal model
OpenAI’s guidance is to test representative tasks, because tokenization and generated reasoning differ between models. Evaluate quality, latency, retries, and successful-task rate together with price.
A decision framework for your workload
| Workload characteristic | Usually favors | What to verify |
|---|---|---|
| Short prompts, strict output limits | Token-based | Minimum fees, output caps, and tool charges. |
| Long prompts with frequent reusable context | Token-based if cache hits remain high | Cache-hit eligibility, write costs, and prefix stability. |
| Very predictable payload and fixed operation value | Request-based | Whether retries, overages, and hidden subcalls are billed. |
| Highly variable prompt or response size | Depends on measured p50/p95 | Outlier cost, quality failures, and budget controls. |
| Agentic workflow with many tools | Whichever has the lower fully loaded successful-task cost | Intermediate tokens, tool fees, calls per task, and retries. |
| Offline, high-volume processing | Token-based batch rates may win | Batch eligibility, latency tolerance, and regional terms. |
Use this table as a screening tool, not a substitute for telemetry. Recalculate after any model, tokenizer, context-window, cache, region, batch, or reasoning-setting change.
Bottom-line test
Choose token pricing when measured calls are small or cache-efficient and you can enforce output and context budgets. Choose request pricing when payloads are tightly bounded and the fixed fee is below the fully loaded token cost, including reasoning, tools, retries, and overages. If the service mixes both, compare the complete hybrid bill. The decisive metric is not requests per month or a provider’s lowest headline rate; it is the cost of producing one accepted result under your real p50 and p95 workload conditions.
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