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There is no defensible universal “cheapest” provider. Gemini, OpenAI and Anthropic publish model-specific rates, but your bill also depends on input/output mix, context tier, caching, batch eligibility, tools, geography, endpoint and the date the rate applies. Compare one measured workload—not three headline numbers—and keep price separate from model quality.
The examples below use provider rate cards reviewed October 13, 2026, plus Anthropic’s list effective May 27, 2026. Prices are USD per million tokens unless explicitly noted. They are rate-card examples, not capability-equivalent recommendations.
What the three rate cards actually measure
Each provider charges metered usage, but the tables do not describe the same product or identical model capability. A model name next to a price is therefore only a starting point. Record the exact model, context tier, processing mode, region or endpoint, cache state, tools and effective date before comparing totals.
Input and output are separate meters
Input tokens are the prompt, system instructions, retrieved documents and conversation history sent to the model. Output tokens are the generated response. Output is often priced several times higher than input, so two applications with the same total token count can have very different bills. Always show the two quantities separately.
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Google’s Gemini pricing table, OpenAI’s API pricing documentation and Anthropic’s Claude Model Pricing document all list model-specific input and output prices. None of those pages establishes that the example models deliver equivalent quality on your task.
Context and service mode can change the unit price
A short prompt and a very large prompt may fall into different pricing tiers. OpenAI separates short- and long-context rates. Google lists Standard, Batch and Flex schedules. Anthropic lists Standard and Batch entries. A comparison that copies only the first row of each table can be wrong for a long-context or asynchronous workload.
Representative prices, with their qualifications
The following rows are concrete entries from the cited rate cards. They are useful for demonstrating the comparison method, not for declaring a winner.
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|---|---|---|---|---|
| Gemini 3.8 Flash | Standard | $0.75 | $3.75 | Applies through December 31, 2026; Google lists $1.50 input and $7.50 output starting January 1, 2027. |
| Gemini 3.8 Flash | Batch or Flex | $0.375 through December 31, 2026 | $1.875 through December 31, 2026 | Google lists Batch and Flex at half the Standard rates for this period; verify the current table before scheduling work. |
| Claude Sonnet 4.6 | Standard Global | $3 | $15 | Anthropic list prices are effective May 27, 2026 (2026-05-27). |
| Claude Sonnet 4.6 | Batch Global | $1.50 | $7.50 | Anthropic’s same dated list; asynchronous eligibility and geographic scope still matter. |
| gpt-6.1-sol | Standard, short context | $1 | $5 | OpenAI also lists long-context rates and other processing modes; this row is not a price for every request. |
All figures in the table are USD per one million tokens. The Gemini 3.8 Flash change is scheduled, not a current 2026 charge. The Anthropic date applies to the cited document, while OpenAI’s page was reviewed October 13, 2026 and can change independently. Link each production estimate to the live Google, OpenAI and Anthropic pages.
A worked workload comparison
Use one scenario to make the arithmetic visible. Suppose a service consumes 1,000,000 input tokens and generates 200,000 output tokens during a billing period. The calculation is:
cost = (input tokens ÷ 1,000,000 × input rate) + (output tokens ÷ 1,000,000 × output rate)
| Rate-card example | Input calculation | Output calculation | Token subtotal |
|---|---|---|---|
| Gemini 3.8 Flash Standard through Dec. 31, 2026 | 1 × $0.75 = $0.75 | 0.2 × $3.75 = $0.75 | $1.50 |
| Gemini 3.8 Flash Standard from Jan. 1, 2027 | 1 × $1.50 = $1.50 | 0.2 × $7.50 = $1.50 | $3.00 |
| Claude Sonnet 4.6 Standard Global | 1 × $3 = $3.00 | 0.2 × $15 = $3.00 | $6.00 |
| Claude Sonnet 4.6 Batch Global | 1 × $1.50 = $1.50 | 0.2 × $7.50 = $1.50 | $3.00 |
| gpt-6.1-sol Standard short context | 1 × $1 = $1.00 | 0.2 × $5 = $1.00 | $2.00 |
This arithmetic excludes cache charges, tools, endpoint uplifts, taxes and any minimums. It also assumes every request fits the stated context tier and that the entire workload qualifies for the stated mode. It does not test quality, latency or reliability, so the subtotals cannot answer which model is best for a particular application.
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Change the token mix before drawing a conclusion
If the same application generates 1,000,000 output tokens but only 200,000 input tokens, the output-heavy model has a much larger bill. Conversely, a retrieval system that resends a very large document on every request may be dominated by input charges. Export actual usage by request type—chat, extraction, summarization, coding or batch evaluation—instead of treating the example mix as typical.
How to compare context tiers correctly
- Measure the full serialized request. Include system messages, tool definitions, retrieved passages, conversation history and image or other modality tokens where the provider bills them.
- Find the model’s threshold. OpenAI publishes distinct short- and long-context rows. Google and Anthropic also organize pricing by model and mode, so do not assume another provider’s threshold or terminology transfers.
- Assign each request to its actual tier. A monthly average can hide a high-cost tail of requests that cross the long-context boundary.
- Recalculate after prompt changes. Adding a policy block or retrieval chunk can move every request into a different tier even when the model name is unchanged.
Where a rate card does not state a comparable threshold or number in the information used here, mark it “not stated” and verify the live provider documentation rather than filling the cell with an estimate.
Caching: compare every cache event, not just “discounted input”
Caching can reduce repeated-context cost, but providers bill different events. Your worksheet should have separate columns for cache creation or writes, cached-input or cache-hit reads, refreshes, storage duration and ordinary uncached input.
Anthropic’s published cache fields
For Claude Sonnet 4.6 Standard Global, Anthropic lists five-minute cache writes at $3.75 per million tokens, one-hour cache writes at $6, and cache hits and refreshes at $0.30 per million tokens. Those figures are attached to the May 27, 2026 list and should not be generalized to other Claude models, regions or modes.
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OpenAI and Google cache schedules
OpenAI exposes cached-input pricing alongside ordinary input and output and may show different values by model and context tier. The gpt-6.1-sol short-context row lists $0.05 cached input, $1.25 cache writes, $1 input and $5 output per million tokens. The long-context and processing-mode rows are separate.
Google’s pricing page also distinguishes cached usage and related cache charges by model. Read the cache-write, cache-hit and storage rules together; a low hit rate can make a write-heavy design cost more than an uncached prompt. Do not apply Anthropic’s cache numbers to Gemini or OpenAI.
Cache decision checklist
- What exact prefix is stable enough to cache?
- How many requests reuse it before expiration?
- Is the write charged once per lifetime, per time window or on refresh?
- Does the provider charge storage while the cache exists?
- What happens when a request exceeds the cacheable context or changes the prefix?
Batch, Flex and other processing modes
Asynchronous work can qualify for lower rates, but only when your product can tolerate delayed results and the provider’s eligibility rules are met.
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Google Batch and Flex
For Gemini 3.8 Flash, Google lists Batch and Flex input and output at half the Standard rates through December 31, 2026. That makes the worked 1,000,000-input/200,000-output workload $0.75 in Batch or Flex under the cited rates, rather than $1.50 in Standard. The scheduled January 1, 2027 Standard increase means you must also check whether Batch and Flex change at that date.
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Anthropic lists Claude Sonnet 4.6 Batch Global at $1.50 input and $7.50 output per million tokens, half its listed Standard Global rates. Confirm that your traffic is actually submitted through the Batch interface and that its completion-time and geographic requirements fit your service.
OpenAI processing modes
OpenAI’s pricing page separates processing modes and context tiers. The $1/$5 gpt-6.1-sol short-context row is therefore not a substitute for checking the row that matches your selected mode. Record the mode in deployment configuration and in cost reports so a later migration does not silently change the unit price.
Tools can create a second bill
Token arithmetic is incomplete when the model invokes a paid tool.
Google Search grounding
Google lists 5,000 free Search grounding requests per month shared across Gemini 3 and newer models, then $14 per 1,000 requests. A submitted request may generate one or more individual search queries, and those individual queries are the billable units. Count search queries, not merely API calls, and separate grounding charges from model tokens.
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Google lists Maps grounding separately from Search grounding. If an application uses both, create distinct counters and rates in the estimate; do not assume the Search allowance or price covers Maps.
Tool-call effects at the other providers
OpenAI and Anthropic can also expose tool-enabled workflows with their own usage or third-party service implications. The cited rate cards do not establish one universal tool fee, so identify every enabled tool, its billable unit and whether the tool’s response tokens are included in model input.
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Region, endpoint and deployment scope
Geography can change both eligibility and price. Anthropic’s document distinguishes geographic scopes such as Global entries. 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 surcharge to the eligible endpoint’s rates, not automatically to every OpenAI request.
For each provider, capture:
- Serving region and data-residency requirement.
- Endpoint type, including any regional or compliance endpoint.
- Model release eligibility for that endpoint.
- Whether the published row is global, regional, standard or otherwise scoped.
- Any surcharge, minimum or separate tool rate.
Content-use terms belong in the comparison
Price is not the only deployment cost. Google’s pricing page distinguishes free and paid tiers on whether submitted content is used to improve products. If data handling is a requirement, record the applicable tier and terms beside the dollar estimate. A lower token rate that violates your data policy is not an acceptable optimization.
A repeatable comparison procedure
- Freeze the date. Write “reviewed October 13, 2026” for the Google and OpenAI pages and “effective May 27, 2026” for the cited Anthropic list. Add a recheck date because live pages change.
- Define the workload. List request count, input tokens, output tokens, context-size distribution, retry rate and tool calls by request type.
- Select candidate rows. Record exact model, Standard/Batch/Flex mode, short/long context and geographic scope.
- Calculate uncached tokens. Multiply input and output quantities by their separate per-million rates.
- Add cache events. Price writes, hits, refreshes and storage using the provider’s own fields.
- Add tools and endpoint adjustments. Count billable Search or Maps queries and apply a documented regional or FedRAMP uplift where eligible.
- Run sensitivity cases. Recalculate with a higher output ratio, more long-context requests, fewer cache hits and the next announced rate change.
- Validate quality separately. Use your own task benchmark for accuracy, latency and failure handling. Do not infer capability equivalence from a price table.
A worksheet that prevents common mistakes
| Column | What to enter | Why it matters |
|---|---|---|
| Date and source URL | Effective date or page-review date, plus the exact provider link | Rates and promotions expire or change. |
| Model and version | Exact API model identifier | Model-family names can hide different prices. |
| Context tier | Short, long or provider-specific threshold | Long prompts may use another row. |
| Input/output tokens | Separate measured totals by workload type | Different mixes produce different bills. |
| Cache events | Writes, hits, refreshes and storage duration | “Cached” is not one universal discount. |
| Processing mode | Standard, Batch, Flex or other documented mode | Asynchronous eligibility can change rates. |
| Tools | Search, Maps or other billable calls and units | Tool charges can exceed token charges. |
| Region and endpoint | Global, regional, compliance endpoint and uplift | Geography can change availability and price. |
| Quality result | Your benchmark score and latency | Separates economic value from rate-card arithmetic. |
Pitfalls and how to correct them
Choosing the lowest input rate
Failure: selecting the smallest input number while ignoring output-heavy responses. Correction: calculate both meters using observed token ratios.
Mixing dates
Failure: comparing a future Gemini rate with a current OpenAI rate or an undated Anthropic row. Correction: put the effective date beside every figure and run a same-date scenario.
Calling models equivalent
Failure: treating Gemini 3.8 Flash, gpt-6.1-sol and Claude Sonnet 4.6 as interchangeable because all have input and output prices. Correction: benchmark the task and present price as one axis.
Ignoring cache creation
Failure: applying the cache-hit rate to the first request. Correction: count writes and their lifetime, then estimate the hit rate.
Forgetting asynchronous constraints
Failure: using Batch or Flex rates for interactive traffic. Correction: verify latency, cancellation, ordering and retry behavior before assigning the discounted row.
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Counting API calls instead of billable tool units
Failure: assuming one grounded request equals one Search query. Correction: count Google’s individual queries and track Maps separately.
Overlooking endpoint uplift
Failure: budgeting a standard OpenAI row for regional-processing or FedRAMP traffic. Correction: check eligibility and add the documented 10% uplift where it applies.
How to make a provider decision
After calculating comparable scenarios, rank candidates in separate columns:
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- Cost: expected monthly total under your measured token mix, cache rate, tools and endpoint.
- Operational fit: latency, batch tolerance, regional availability and failure recovery.
- Quality: results from the same prompt set and acceptance criteria.
- Commercial stability: effective dates, announced changes and the effort required to monitor them.
- Data policy: content-use terms and required deployment tier.
Use a weighted decision only after these values are measured. If quality differs materially, a higher token price may produce a lower cost per accepted answer because fewer retries or human corrections are needed; that conclusion requires your own benchmark, not a provider rate card.
Frequently Asked Questions
Which provider is cheapest for every developer?
None can be named universally from these rate cards. The answer changes with model, input/output ratio, context tier, cache behavior, processing mode, tools, endpoint and date.
Should I use the announced Gemini 3.8 Flash 2027 price in a 2026 budget?
Use it for a forward-looking sensitivity case, not as the current 2026 charge. Google lists the higher Standard rates beginning January 1, 2027, so schedule a price review before that date.
Does a cache-hit price include the cost of creating the cache?
Not automatically. Treat cache writes or creation, cache hits, refreshes and storage as separate line items because each provider publishes its own schedule.
Can I compare Batch prices with interactive API prices?
Only if the workload can tolerate asynchronous processing and meets the provider’s Batch eligibility rules. Otherwise, compare the Standard row used by the live application.
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