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GPT-6 Astra API pricing and cost calculator

Astra's 100K-input, 5K-output example costs $1.25 uncached or $0.80 with half the input cached. Longer prompts need a separate estimate once input exceeds 272K.

GPT-6 Astra's standard base prices are $10 per million input tokens and $50 per million output tokens. Cache reads cost $1 per million, which can reduce the input cost of repeated context, but output remains a major part of the bill. These prices make the number of attempts and tool turns especially relevant. Use the calculator to estimate a request, then evaluate whether the completed result earns that spending on your own tasks.

By TechCompare · Updated

Input tokens
100,000
per request
Output tokens
5,000
per request
Volume
100 / monthly
Standard API

Calculator

Cost Comparison

Based on 100,000 input tokens (50% cached), 5,000 output tokens, and 100 requests.Prices are fetched live from OpenRouter and may include temporary promotional discounts not accounted for in our article and comparison figures.

Same 100,000 in / 5,000 out (50% cached) scenario, pre-loaded. Adjust tokens, caching, or swap models.
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How this is calculated

A 100,000-input, 5,000-output request costs $1 for uncached input plus $0.25 for output, totaling $1.25. Reading half the input from cache lowers the input estimate to $0.55 and the total to $0.80. At 100 monthly calls, the starting example is $80 before writes and tools. Prompts above 272,000 input tokens use $20 input, $2 cache reads, and $75 output per million for the full request. That makes a 300,000-input, 5,000-output call $6.375 without cache hits or $3.675 with half the input cached. Cache writes cost $12.50 per million at the base tier and $25 in the large-prompt tier. The embedded chart uses live OpenRouter prices and conditional pricing data rather than a fixed copy of OpenAI's standard table.

Verdict

Treat Astra as a model to evaluate on work where the added spending has a measurable payoff. Compare review time, failures, and total billed usage with a lower-cost route before making it the default for an application. Caching can make large repeated prefixes cheaper, but it doesn't discount generated output or remove the long-prompt boundary. Limit needless tool turns and context growth, then apply request volume to the cost of successful tasks.

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Related guides

Astra vs GPT-6.1 Sol
Compare base, cached, and long-prompt token costs.
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GPT-6.1 Sol pricing
Budget a lower-cost route for the same context capacity.
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GPT-6 Luna pricing
Estimate spending on frequent, focused requests.
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Official Astra pricing reference
OpenAI's model prices and long-prompt billing rules.
View guide ➜

Frequently asked questions

How much would 10,000 GPT-6 Astra calls cost?
For 100,000 input and 5,000 output tokens per call, the standard token estimate is $12,500 without cache hits or $8,000 with half the input cached. Add cache writes, tools, and extra attempts when budgeting an application. A session with several model calls can cost much more than one request, even if its visible final answer is short.
How do Astra's cache rates differ from its input rate?
At the base tier, uncached input is $10 per million tokens, cache reads are $1, and cache writes are $12.50. A reused prefix can therefore lower spending after the cache is created, while creating it costs more than plain input processing. Estimate writes and reads separately. The calculator uses a cache-read percentage and doesn't include the write charge.
What happens when Astra input exceeds 272,000 tokens?
The whole request uses double input and cache rates and 1.5 times the output rate. Input becomes $20 per million, cache reads $2, cache writes $25, and output $75. This is a pricing boundary below the model's full context capacity. Staying within the context window doesn't mean every prompt uses the base rates.
Can batch processing make Astra affordable for bulk work?
The provider Batch API halves applicable token rates, so the uncached 100K-input, 5K-output example becomes $0.625 before tools and writes. Whether that fits your budget depends on total volume and the quality benefit on your workload. Batch is useful for asynchronous processing, while applications that need an immediate interactive result need a different delivery choice.
Is Astra five times as expensive as GPT-6.1 Sol?
Its base input and output rates are five times Sol's, but cache reads are ten times higher. The ratio for a whole request therefore depends on how much input is cached and how much output is generated. Both advertise the same context capacity. Compare actual usage and accepted results rather than assuming the higher price brings a benefit on every task.

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