There is no universal cheapest API. Gemini, OpenAI, and Anthropic publish different model rates, context tiers, caching rules, batch discounts, tool charges, endpoint surcharges, and effective dates. A useful comparison prices the same token mix and service conditions for a real workload, then checks whether the models are suitable for the task. The figures below are dated rate-card examples, not a quality ranking.
For a reproducible estimate, record the model, input tokens, output tokens, context tier, cache behavior, processing mode, geography or endpoint, billable tools, and price date. Keep input and output calculations separate; a model with cheaper input can still cost more when it generates long responses.
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What a defensible price comparison includes
API billing is metered usage, not a single subscription price. The headline number people quote is usually one input or output rate for one model and one service tier. That number is not enough to predict a production bill.
Price is model-specific, not provider-wide
Each provider publishes rates by model. The model names in the examples below are not established as capability equivalents, so their prices cannot prove that one provider delivers better value or the same quality. If you run a quality benchmark, report it separately from the arithmetic.
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Use the provider tables as the source of truth: Google Gemini Developer API pricing, OpenAI API pricing, and Anthropic’s Claude Model Pricing document.
Every figure needs its scope and date
The examples in this article are USD per one million tokens unless noted otherwise. They are rate-card entries reviewed on October 13, 2026, except where the provider’s own effective date is specified. Prices can change, and a future announced change is not today’s price.
Published rate examples at a glance
This table deliberately keeps unlike rows separate. It is a starting point for a calculator, not a claim that the models perform the same work.
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| Provider and model | Mode or context | Input | Output | Cache details | Effective-date or scope note |
|---|---|---|---|---|---|
| Google Gemini 3.8 Flash | Standard | $0.75 through Dec. 31, 2026; $1.50 from Jan. 1, 2027 | $3.75 through Dec. 31, 2026; $7.50 from Jan. 1, 2027 | Google lists caching schedules; the cited figures do not specify a universal cache rate. | Google pricing page reviewed Oct. 13, 2026 |
| Google Gemini 3.8 Flash | Batch or Flex, through Dec. 31, 2026 | $0.375 (half of Standard) | $1.875 (half of Standard) | Check the model’s cache fields on the live table. | Half-Standard rates are stated for this period; confirm eligibility and timing. |
| Anthropic Claude Sonnet 4.6 | Standard Global | $3 | $15 | 5-minute cache write $3.75; one-hour cache write $6; cache hit or refresh $0.30 | Anthropic list prices effective May 27, 2026 |
| Anthropic Claude Sonnet 4.6 | Batch Global | $1.50 | $7.50 | Use the applicable cache entries in the document. | Anthropic list prices effective May 27, 2026 |
| OpenAI gpt-6.1-sol | Standard, short context | $1 | $5 | Cached input $0.05; cache write $1.25 | OpenAI page reviewed Oct. 13, 2026; not the long-context rate |
OpenAI also publishes different long-context prices and separate processing modes; the reviewed short-context row does not establish those values. Do not copy the short-context price into a long-context forecast. Google separates Standard, Batch, and Flex schedules, while Anthropic lists Standard and Batch entries. The relevant tables are the Google, OpenAI, and Anthropic documents.
Calculate a workload instead of comparing headline rates
The basic formula
For a request without caching, calculate the two token streams independently:
cost = (input_tokens / 1,000,000 × input_rate) + (output_tokens / 1,000,000 × output_rate)
Then add any cache-write or cache-storage charge, cached-input charge, batch or service-mode adjustment, tool usage, and endpoint surcharge. If request sizes vary, model several representative buckets rather than multiplying one unusually small request by a monthly total.
Illustrative 10-million-input, 2-million-output workload
The following arithmetic uses the published examples above. It assumes all tokens are billed at the listed row, no cache, no tools, and no endpoint uplift. It demonstrates billing mechanics; it does not test quality or latency.
| Rate-card row | Input calculation | Output calculation | Illustrative total |
|---|---|---|---|
| Gemini 3.8 Flash Standard through Dec. 31, 2026 | 10 × $0.75 = $7.50 | 2 × $3.75 = $7.50 | $15.00 |
| Gemini 3.8 Flash Batch or Flex through Dec. 31, 2026 | 10 × $0.375 = $3.75 | 2 × $1.875 = $3.75 | $7.50 |
| Gemini 3.8 Flash Standard from Jan. 1, 2027 | 10 × $1.50 = $15.00 | 2 × $7.50 = $15.00 | $30.00 |
| Claude Sonnet 4.6 Standard Global | 10 × $3 = $30.00 | 2 × $15 = $30.00 | $60.00 |
| Claude Sonnet 4.6 Batch Global | 10 × $1.50 = $15.00 | 2 × $7.50 = $15.00 | $30.00 |
| gpt-6.1-sol Standard short context | 10 × $1 = $10.00 | 2 × $5 = $10.00 | $20.00 |
These totals are not an apples-to-apples model comparison. They use different products and do not establish equal capability, context limits, latency, or reliability. They simply show why both token directions and service mode belong in the estimate.
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Include traffic shape and retries
A monthly forecast should multiply the cost of one request class by its expected count, then add other classes such as short chats, long-document analysis, tool calls, and retries. Include rejected or repeated requests when your client may resend after a timeout. Keep a high-percentile request estimate for budgeting so a few very large contexts do not disappear inside an average.
Context length can move a request into another price tier
Context is the total material presented to the model, not just the user’s latest message. It can include system instructions, conversation history, retrieved documents, tool results, and the requested output, depending on the provider’s counting rules.
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OpenAI short versus long context
The gpt-6.1-sol example above is the Standard short-context row: $1 input, $0.05 cached input, $1.25 cache writes, and $5 output per million tokens. The same pricing page shows different long-context rates, so a request that crosses the model’s threshold must be priced from the long-context row. The cited OpenAI pricing materials do not establish the long-context dollar amounts; obtain them from the current OpenAI pricing table before budgeting.
Google Standard, Batch, and Flex
Google presents separate schedules for Gemini models. A workload that can wait may qualify for Batch or Flex, while an interactive request generally needs the Standard path. For Gemini 3.8 Flash, Google lists Batch and Flex input and output rates at half the Standard rates through December 31, 2026. Confirm that your endpoint, queueing expectations, and request type meet the current eligibility rules.
Anthropic context and geography rows
Anthropic’s document separates Standard and Batch entries and identifies Global rows. Select the row matching the API route and processing requirement; do not assume a Global price applies to every regional or hosted-platform configuration.
Caching is a separate cost model
“Cached” does not mean one universal discount. A comparison needs four entries: the price to create or write the cache, the price for a cache hit or refresh, any storage duration charge, and the ordinary uncached input rate. Also state how often the prefix is reused and whether cache writes happen once or repeatedly.
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For gpt-6.1-sol Standard short context, the reviewed row lists $0.05 per million cached input tokens and $1.25 per million tokens for cache writes, alongside $1 ordinary input and $5 output. If 8 million of a request’s 10 million input tokens are cache hits and 2 million are ordinary input, the input portion is (8 × $0.05) + (2 × $1) = $2.40. A first-time 10-million-token cache write would add 10 × $1.25 = $12.50 under that row. The write is not a recurring hit price; model the lifecycle explicitly.
Anthropic cache durations
Claude Sonnet 4.6 Standard Global lists a 5-minute cache write at $3.75 per million, a one-hour cache write at $6, and cache hits and refreshes at $0.30. Choose the duration that matches reuse. A one-hour write may cost more upfront but avoid repeated writes when the same context is reused; that is a workload calculation, not a blanket recommendation.
Google cache fields
Google’s live pricing page exposes cache-related schedules, but the named Gemini 3.8 Flash figures above do not include a single cache price that can safely be applied to every model or mode. Read the cache input, creation, and storage entries for the exact model and endpoint when you build the spreadsheet.
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Batch and asynchronous processing
Batch pricing matters only when the workload can tolerate asynchronous completion and the provider accepts the request in that mode.
- Gemini: Gemini 3.8 Flash Batch and Flex rates are listed at half the Standard input and output rates through December 31, 2026. Treat the January 1, 2027 Standard change separately; do not assume the same discount or schedule continues without checking the live page.
- Anthropic: Claude Sonnet 4.6 Batch Global is $1.50 input and $7.50 output per million, compared with $3 and $15 for Standard Global in the May 27, 2026 list.
- OpenAI: The pricing page identifies separate processing modes and context tiers. Use the mode-specific row for your request rather than inferring a discount from another provider’s Batch rate.
When comparing modes, include the operational value of waiting: queue time, deadline risk, concurrency limits, cancellation behavior, and the engineering work needed to reconcile asynchronous results. A lower token rate is not useful if the application requires an immediate response.
Tools, grounding, regions, and endpoint uplifts
Google Search grounding
For Gemini 3 models, Google provides 5,000 free Search grounding requests per month shared across Gemini 3 and newer models. After that allowance, the charge is $14 per 1,000 requests. Google says a submitted request may generate one or more individual search queries, and those individual queries are the billable units. Therefore, count queries—not just top-level API calls—in your forecast. The allowance and rate are documented on the Google pricing page.
Google also lists Maps grounding separately. If your application uses it, add the Maps line item rather than treating grounding as included model output.
OpenAI regional processing and FedRAMP endpoints
OpenAI notes a 10% endpoint uplift for eligible models released on or after March 5, 2026 when using regional processing or FedRAMP requests. Apply that uplift to the eligible endpoint’s applicable charges; do not add it to every request by default. Verify model eligibility and endpoint selection on the OpenAI pricing page.
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Anthropic’s list contains rows with different geographic scopes, including the Claude Sonnet 4.6 Standard Global and Batch Global examples. Geography is part of the price key. A regional requirement can therefore change the row you need even when the model name is unchanged.
Price dates and announced changes must stay attached to the number
- Anthropic: The list-price PDF states, “Prices Effective: May 27, 2026 (2026-05-27).” Treat the Claude figures as effective from that date and recheck for later revisions.
- Google: The Gemini 3.8 Flash Standard rates change on January 1, 2027, from $0.75/$3.75 to $1.50/$7.50 per million input/output tokens. The cited Batch and Flex half-Standard figures are stated through December 31, 2026.
- OpenAI: The pricing page flags a promotional price period for GPT-5.6 Sol. A promotion is not a permanent list price; record its end date and eligibility if it affects your forecast.
Store the retrieval date, effective date, URL, model version, and a copy of the assumptions with every estimate. This prevents a later rate change from being mistaken for a billing error.
A practical comparison worksheet
- Describe the workload. State the task, expected requests per day or month, typical and high-percentile input tokens, expected output tokens, retries, and tool calls.
- Choose candidate rows. Record the exact model name, Standard or Batch/Flex mode, context tier, and geographic or endpoint scope for each provider.
- Separate token streams. Multiply input and output tokens by their own per-million rates. Never substitute output volume for input volume or vice versa.
- Model cache lifecycle. Enter ordinary input, cache writes or creation, cache-hit or refresh tokens, storage duration, and the percentage of requests that reuse the prefix.
- Add tools. Count billable Search or Maps queries and any other metered tool event. For Google Search grounding, convert API requests into individual queries where one request can fan out.
- Apply endpoint rules. Add the OpenAI 10% regional-processing or FedRAMP uplift only when the selected eligible endpoint requires it. Use the geographic row required by Anthropic or another provider.
- Apply dates. Put the effective date beside every rate. Create separate columns for today’s rate and a known future rate, such as Google’s January 1, 2027 Gemini 3.8 Flash change.
- Run sensitivity cases. Recalculate with short and long contexts, no cache and warm cache, interactive and batch traffic, normal and high output lengths, and tool-heavy requests.
- Validate billing telemetry. Compare token counts and tool events in provider usage reports with your forecast. Investigate a mismatch by checking context tier, retries, cache misses, and endpoint—not just the headline model price.
Worked cache and tool scenarios
Warm-cache OpenAI request
Suppose a short-context gpt-6.1-sol workload has 10 million input tokens, of which 8 million are cache hits, plus 2 million uncached input tokens and 2 million output tokens. Using the listed Standard short-context rates, input costs $2.40 and output costs $10, for $12.40 after the cache is warm. If the 10-million-token prefix must first be written, add the $12.50 cache-write charge for that initial event, producing $24.90 for that write-and-response example. Subsequent requests depend on the cache-hit and refresh behavior rather than repeating the write assumption.
Search-grounded Gemini traffic
Assume a service submits 6,000 billable individual Search queries in one month across Gemini 3 models. The first 5,000 are within Google’s shared monthly free allowance; the remaining 1,000 are billed at $14. This tool charge is separate from Gemini input and output tokens. If each top-level request produces two individual queries, 3,000 top-level requests would consume the 6,000-query allowance and paid units.
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Batch eligibility changes the answer
For the 10-million-input, 2-million-output example, Gemini 3.8 Flash Batch or Flex through December 31, 2026 produces a $7.50 token total under the stated half-Standard rates, while the same volume in Standard is $15. The lower total is meaningful only if asynchronous processing is acceptable and the request is eligible for that mode.
Common comparison mistakes and fixes
Declaring a winner from one input price
Failure: comparing $0.75 input for Gemini with $1 input for gpt-6.1-sol and calling the former universally cheaper.
Fix: include output, context tier, mode, cache, tools, endpoint, and dates. The illustrative workload above shows how output volume can materially change totals.
Treating model names as capability tiers
Failure: presenting the rows as equivalent intelligence or accuracy.
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Applying a short-context price to long prompts
Failure: multiplying all OpenAI traffic by the gpt-6.1-sol short-context row.
Fix: classify requests by the provider’s context threshold and use the long-context row when applicable.
Calling every repeated prefix “free caching”
Failure: ignoring cache-write, refresh, storage-duration, or cache-miss charges.
Fix: model the first write, hit ratio, refresh interval, and ordinary input separately. Anthropic’s five-minute and one-hour write prices illustrate why duration belongs in the calculation.
Ignoring query fan-out
Failure: counting one Google Search-grounded API request as one billable search when it creates multiple individual queries.
Fix: instrument the individual query count and compare it with the shared 5,000-query allowance.
Mixing current and future prices
Failure: using Google’s January 1, 2027 Gemini rate as if it applied before that date, or treating a promotion as permanent.
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Fix: keep effective dates in the same cell as the price and forecast each billing period under the rate that applies then.
Omitting regional requirements
Failure: estimating a global route while procurement requires regional processing or FedRAMP.
Fix: select the actual endpoint first, then apply the geographic row or uplift. For eligible OpenAI models and requests released on or after March 5, 2026, the documented regional-processing and FedRAMP uplift is 10%.
How to make a provider decision without overclaiming
Use a two-stage decision rather than a single “cheapest” label:
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- Filter for fit. Remove rows that fail the required context length, latency, geography, endpoint certification, tool availability, or batch deadline.
- Measure the workload. Run the same representative prompts and documents, record quality and operational metrics, and retain the exact token mix.
- Price the measured mix. Apply input, output, cache, mode, tool, endpoint, and date rules to each candidate.
- Stress the assumptions. Test longer conversations, cache misses, retries, output spikes, and future rate changes.
- Choose with a stated objective. A team optimizing interactive latency may reject a cheaper Batch row; a nightly document pipeline may prefer it. State that trade-off instead of naming a universal winner.
Content-use terms are another comparison axis
Cost is not the only contractual consideration. Google’s pricing documentation distinguishes free and paid tiers on whether submitted content is used to improve products. If data handling affects your deployment, read the terms for the exact tier and account type and record that decision alongside the rate calculation. Do not infer data-use terms from the token price.
Sources and maintenance
Recheck the live provider pages before launch or renewal because model names, prices, promotions, allowances, and effective dates can change:
Quick Recap
- Google AI for Developers: Gemini Developer API pricing
- OpenAI API pricing
- Anthropic List Prices — 2026-05-27
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
