Token-based pricing is usually cheaper for short, controlled, or heavily cached tasks. A fixed per-request price can win when prompts and responses are long, unpredictable, or expensive to tokenize. There is no universal winner: calculate both models with your real input, output, reasoning, cache, tool, retry, and success-rate data, then compare cost per successful task rather than the headline unit price.
What the two pricing models actually charge
Token-based pricing
A token-priced API meters the text and, in some systems, hidden intermediate work. You pay separate rates for categories such as uncached input, cached input, output, and reasoning or intermediate tokens. The same user request can cost different amounts when the model generates a longer answer, invokes tools, exposes more reasoning tokens, or tokenizes the text differently.
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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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Other feature charges can be added. Google states that managed-agent inference can include input, output, and intermediate input or reasoning tokens, while tool fees follow separate rules. Anthropic publishes separate standard, regional, batch, cache-write, and cache-hit rates, so the invoice depends on the service path as well as the visible response.
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Request-based pricing
A request-priced service charges a fixed amount for each API or tool invocation. It is straightforward to forecast when every call has a tightly bounded payload and similar work. The same fixed fee can overcharge tiny requests or undercharge very large ones because it does not track the amount of computation or text in each call.
Hybrid pricing
Many products combine both approaches: a per-call fee for an operation or tool, plus token charges for the model inference behind it. Treat this as a third model. Adding only the request fee to your budget will understate cost when inference tokens, cache writes, storage, or overages are billed separately.
Which model is cheaper in practice?
Choose token pricing when prompts are short, outputs are constrained, cache hits are frequent, or request size varies widely and you can enforce token budgets. Choose request pricing when payloads are predictable, users need a simple fixed unit, or the per-call fee is below the token-metered cost of your normal request. A request fee also has planning value: finance teams can forecast spend without modeling every token category.
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Do not decide from the advertised input rate alone. OpenAI’s Help Center cautions 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. It also recommends testing representative tasks instead of comparing only visible response length.
Calculate the cost of one request
Token-priced request
Use this complete equation for a request:
(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
Keep token categories separate. Cached input is not automatically free; it normally has a lower rate. Reasoning or intermediate tokens may be included in a provider’s input or output definition, or billed at a distinct rate. Confirm the provider’s current definition before building a forecast.
Request-priced request
For a fixed-fee model, calculate:
number_of_requests × price_per_request + token_fees + tool_fees + storage_fees + overage_fees
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Find the break-even point
The break-even point is the workload at which the two complete totals are equal. It is not always a single token number because input and output have different rates and cache hits change the mix.
- Define the unit. Use one successful task, conversation, document, workflow, or other business outcome. Do not compare a raw API call with a multi-call workflow.
- Measure production distributions. Record p50 and p95 input tokens, output tokens, cache-hit percentage, reasoning or intermediate tokens, tool calls, retries, failed tasks, and latency. Averages alone hide expensive tails.
- Calculate token cost for each request class. Separate short, normal, and long paths, and distinguish cache hits from misses.
- Calculate fixed cost for the same classes. Count every invocation, including tool calls, retries, and calls that fail to produce a successful task if those calls are billable.
- Solve for equality. Set the token total equal to the request total and vary the workload variable you care about: tokens per task, requests per task, or monthly task volume.
- Compare successful-task cost. Divide total spend by completed, accepted outcomes. A cheaper call that requires more retries may be the more expensive system.
- Recheck operational constraints. Include latency, batch eligibility, rate limits, regional requirements, and the engineering cost of enforcing token caps.
Simple break-even example
Assume a hypothetical fixed fee of $0.005 per request and a token model charging $0.10 per million input tokens and $0.50 per million output tokens. A 10,000-input-token request with 2,000 output tokens costs $0.001 for input and $0.001 for output, or $0.002 total. At that size, token pricing is cheaper.
If the input and output mix stays at 5 input tokens for every output token, multiplying the request by 2.5 produces about 25,000 input tokens and 5,000 output tokens. The token total becomes $0.005, the illustrative break-even point. Larger requests favor the fixed fee; smaller requests favor token pricing. This is an example for understanding the method, not a quoted provider offer.
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Short call with OpenAI GPT-6 Luna
The published 2026 rates for GPT-6 Luna are $0.10 per million input tokens, $0.01 per million cached input tokens, and $0.50 per million output tokens.
For 20,000 uncached input tokens and 5,000 output tokens:
- Input: 20,000 × $0.10 / 1,000,000 = $0.002
- Output: 5,000 × $0.50 / 1,000,000 = $0.0025
- Total: $0.0045 per request
At 100,000 identical successful requests, the model component would be $450 before tool fees, retries, or other charges.
The same call with a cache hit
Suppose 18,000 tokens are served from cache, 2,000 are new input tokens, and output remains 5,000 tokens:
- Cached input: 18,000 × $0.01 / 1,000,000 = $0.00018
- New input: 2,000 × $0.10 / 1,000,000 = $0.0002
- Output: 5,000 × $0.50 / 1,000,000 = $0.0025
- Total: $0.00288 per request
The cache lowers the model charge, but output still dominates this particular mix. If your prompts are mostly repeated context and your answers are short, cache design can materially change the comparison with a fixed request fee.
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Longer call with OpenAI GPT-6 Astra
The published 2026 GPT-6 Astra rates are $10.00 per million input tokens, $1.00 per million cached input tokens, and $50.00 per million output tokens. A request containing 20,000 input tokens and 5,000 output tokens costs $0.20 for input plus $0.25 for output, or $0.45 before any additional fees. A fixed request price below $0.45 would be cheaper for this specific payload; a higher one would not.
Claude Sonnet 4.6 on Google Vertex AI
Anthropic’s published global-standard Vertex AI rates for Claude Sonnet 4.6 are $3.00 per million input tokens and $15.00 per million output tokens. The schedule also lists cache-write and cache-hit rates. For a 20,000-input, 5,000-output request at the global-standard rates, input is $0.06 and output is $0.075, for a model total of $0.135 before cache, tools, or other charges.
Anthropic’s cited 2026 PDF shows batch processing at half the displayed global-standard input and output rates for that Sonnet 4.6 Vertex AI row. Batch pricing can therefore change the winner, but only when your workload qualifies and can tolerate the batch service’s timing and operational constraints.
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Why a bill can jump when request volume does not
Longer prompts or retained context
A chat application may send the entire conversation, policy pack, or retrieved document on every turn. Request count stays flat while input tokens rise. Track input tokens per call and the p95, not only the number of calls.
More generated output
Small changes to maximum output, formatting requirements, or user behavior can increase output tokens. Output rates are often higher than input rates, so verbose responses can dominate the invoice.
Reasoning or intermediate tokens
Models with deeper reasoning settings may consume intermediate tokens that are not visible in the final answer. Google explicitly includes intermediate input or reasoning tokens in managed-agent inference. A setting change can therefore increase cost without a visible increase in response length.
Cache misses or cache writes
Changing a prompt prefix, system message, cache lifetime, or region can reduce cache-hit rates. Cache writes may have their own charge, while a miss moves tokens to the higher uncached-input rate.
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An agent can make several model and tool invocations to complete one user task. A dashboard showing user requests may hide these internal calls. Count calls per successful task and record separate tool fees.
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Retries and failed work
Timeouts, rate-limit retries, malformed tool arguments, and validation failures can consume billable tokens. A stable request count with a rising retry rate produces a higher bill and a lower success-adjusted efficiency.
Model, tokenizer, region, or service changes
Switching models can alter tokenization, output behavior, reasoning exposure, cache eligibility, regional premiums, or batch discounts. Recalculate after any model, tokenizer, context-window, cache, region, batch, or reasoning-setting change.
What to measure before choosing
| Metric | Why it changes the decision | How to use it |
|---|---|---|
| Input tokens, p50 and p95 | Shows normal and tail prompt size | Price separate short and long request classes |
| Output tokens, p50 and p95 | High output rates can dominate cost | Apply maximum-output limits and model both typical and worst cases |
| Cache-hit percentage | Determines how much input receives the lower cache rate | Measure hits by route, prompt prefix, region, and cache lifetime |
| Reasoning or intermediate tokens | Hidden work can add usage without longer visible answers | Capture usage fields exposed by the provider and compare settings |
| Tool calls per task | Agents may make multiple billable invocations | Report cost per successful task, not cost per user message |
| Retry and failure rate | Failed calls still consume capacity and may be billed | Include retries and rejected outputs in total spend |
| Latency and batch eligibility | Lower rates may require asynchronous processing | Separate interactive and batch workloads |
| Regional or data-residency path | Regional rates and availability can differ | Price the region your compliance policy actually permits |
| Rate limits | A cheap unit is not useful if throughput is constrained | Check whether scaling requires additional capacity or calls |
| Monthly volume variance | Fixed fees improve predictability; token bills track usage | Model low, expected, and peak months |
When token pricing is the better fit
Short, bounded prompts
Classification, extraction, routing, and tightly formatted responses often use few tokens. A fixed request fee can be disproportionately high for these calls, while token pricing charges for the small amount of work performed.
Frequent cache hits
Applications that reuse a long system prompt, policy document, or catalog can move much of their input into a lower-priced cache category. Measure actual hit rates; a theoretical cache benefit is not a budget.
Highly variable request sizes
If some tasks contain a few hundred tokens and others contain large documents, token pricing aligns spend with work. A fixed fee protects you from outliers only when those outliers remain below the fee’s implicit allowance.
Strong budget controls
Token pricing works best when you set maximum input and output tokens, truncate or summarize old context, reject oversized uploads, and alert on p95 growth. Without controls, a few large requests can overwhelm a low per-token rate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When request pricing is the better fit
Predictable payloads
A fixed fee is attractive when every call has approximately the same bounded input, output, and tool behavior. Forecasting then requires multiplying expected calls by one known unit price.
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If users routinely need long answers and the request fee is below the token total for those answers, request pricing transfers output-length risk to the provider. Verify what maximum size and features the fee actually covers.
Simple internal chargeback
Teams that need a clear price per workflow may prefer one request unit over accounting for several token categories. Keep a separate technical usage ledger so a change in payload size does not go unnoticed.
Workloads with strict spend predictability
Request pricing can make a monthly commitment easier to plan, but only if retries, tool calls, storage, overages, and minimum commitments are included in the contract or price sheet.
Hybrid pricing requires its own budget
For an agent workflow, map every billable event: initial model call, retrieval call, tool invocation, follow-up model call, validation pass, and retry. Then assign each event its own token and request charges. A workflow with one user-facing request but six internal calls should be modeled as six calls.
Compare hybrid systems with the same completion criteria. If one provider returns a finished answer in two calls and another needs five, a lower unit price may not produce a lower cost per successful task. Include latency and failure recovery because extra calls can also increase timeouts and retry exposure.
How to build a practical cost calculator
- Log usage at request level. Store model, region, service tier, input tokens, cached input tokens, output tokens, reasoning or intermediate tokens when available, tool fees, request fees, status, and task identifier.
- Group calls into business tasks. Link retries, tool calls, and validation calls to the user task that caused them.
- Apply the current rate card. Keep rates versioned by date, model, region, batch mode, cache category, and currency.
- Calculate both alternatives. For token pricing, multiply each token category by its rate. For request pricing, multiply billable invocations by the fixed fee and add all stated extras.
- Report percentiles. Show p50, p95, and peak-day cost per task, not just a monthly average.
- Add scenario controls. Let users vary output length, cache-hit rate, tool calls, retry rate, task volume, and batch share.
- Set alerts. Trigger warnings for rising p95 tokens, cache-hit deterioration, unexpected model changes, and retry spikes.
A spreadsheet is sufficient for a small service. Larger systems should preserve raw usage records so a pricing or model change can be replayed against historical workloads.
Common mistakes and how to avoid them
- Comparing input rates only: Include output, cached input, reasoning, tools, and fixed fees.
- Using visible response length: Measure billed output and intermediate usage; hidden work can be substantial.
- Using one average request: Add p95 and peak scenarios to expose long-tail costs.
- Ignoring tokenization: The same text can produce different token counts across models.
- Counting user messages instead of API calls: Include agent loops, tool calls, retries, and validation requests.
- Assuming cache savings: Confirm cache eligibility, hit rate, write charges, and expiration behavior.
- Mixing regions or service tiers: Regional, batch, and standard prices may differ.
- Forgetting failed tasks: Measure cost per successful outcome and include billable failures.
- Leaving limits unconfigured: Set maximum input and output tokens and enforce them before dispatch.
- Failing to recalculate: Reprice after model, tokenizer, context, cache, region, batch, or reasoning changes.
A decision framework by workload type
| Workload pattern | Likely starting choice | Conditions to verify |
|---|---|---|
| Short classification or extraction | Token-based | Low output cap, few tools, stable tokenizer usage |
| Long-document analysis | Measure both; request-based may win | Input length, output length, cache treatment, and fixed-fee allowance |
| Repeated prompts with stable context | Token-based with caching | Actual cache-hit rate and cache-write charges |
| Autonomous agent workflow | Hybrid analysis | Calls per task, tool fees, reasoning tokens, retries, and success rate |
| Asynchronous bulk processing | Token-based batch pricing may win | Eligibility, completion window, half-rate discount, and operational latency |
| Strict monthly budget and fixed payloads | Request-based | Overages, minimum commitments, retries, and included token limits |
| Highly variable user uploads | Token-based with hard caps | Oversize rejection, truncation policy, and p95 monitoring |
How prices and settings can change the answer
The published 2026 figures are examples, not permanent rankings. Providers can change rates, model names, cache rules, regional availability, batch discounts, context limits, and what counts as reasoning or intermediate usage. A model that is cheapest for an interactive global-standard call may not be cheapest in a regional deployment or an asynchronous batch job.
Re-run the calculator whenever you change the model, tokenizer, context-window strategy, cache configuration, region, batch mode, or reasoning setting. Preserve the old assumptions so finance and engineering can explain a month-to-month change rather than treating it as unexplained bill volatility.
The practical answer for your workload
Start with token pricing when your work is short, cacheable, tightly budgeted, or uneven in size. Start with request pricing when payloads are predictable, responses are long, and the fixed fee is demonstrably below the token total. Use a hybrid model for agent systems and add every tool and retry charge.
The decisive number is not price per million tokens or price per request. It is the fully loaded cost per successful task at your real p50 and p95 workload, including cache behavior, reasoning tokens, tools, batch or regional rules, latency, limits, and failures.
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