OpenAI vs Gemini APIs: Token Price and Task Capability

For production workloads, choose between the OpenAI API and Gemini API by the cost of an accepted result, not the lowest token rate. GPT-6 Luna and Gemini 3.5 Flash-Lite are candidates for focused, high-volume work; coding, long-document analysis, and research may justify a costlier tier when it reduces failed attempts or review work.

Hold the task and acceptance standard constant, then count input, output, retries, cache use, and tool charges. The examples below apply published Standard paid rates to hypothetical workloads. They illustrate billing differences, not measured success rates for any model.

Start with comparable paid rates

The OpenAI Standard rate card prices GPT-6 Luna at $0.10 input and $0.50 output, GPT-6.1 Sol at $2 and $10, and GPT-6 Astra at $10 and $50 per million tokens; it also lists separate long-context, cached-input, and cache-write rates, plus $10 per 1,000 web-search calls with retrieved content billed at model rates.

The Gemini paid-tier table prices Gemini 3.5 Flash-Lite at $0.30 input and $2.50 output per million tokens, while Gemini 3.1 Pro Preview costs $2 and $12 for prompts up to 200,000 tokens and $4 and $18 above that threshold. It lists cache charges and a shared allowance of 5,000 Search-grounding requests per month for Gemini 3 and newer models, followed by $14 per 1,000 requests; a model request can trigger multiple billable search queries.

Luna has the lowest text-token rate among these examples, but a rate card cannot tell a team how often a model will produce a usable answer. Gemini’s listed Flash-Lite input price covers text, image, video, and audio tokens; modality support and the quality required of the output still matter when selecting a tier.

Extraction and coding change the unit of cost

Consider a hypothetical extraction job using 10,000 input tokens and 500 output tokens per attempt. At the listed rates, 1,000 first-pass attempts cost about $1.25 on Luna or $4.25 on Flash-Lite, before caching or tools. If both produce equally usable records, Luna wins on price; otherwise, divide the full bill, including retries, by the number of accepted records.

A hypothetical coding request with 30,000 input and 3,000 output tokens costs about $0.09 for one Sol attempt, $0.096 for Gemini 3.1 Pro Preview, or $0.45 for Astra. The Sol–Pro difference is small enough that an extra repair attempt can outweigh it. Astra’s higher per-attempt cost would need to be earned back through more accepted patches or less engineering review; these prices alone do not show that it will.

Output length matters as much as the number of calls. A model that produces more billable reasoning or repeatedly rewrites a patch can cost more even at a favorable input rate. Passing the same code tests is a better comparison for coding, while field-level correctness on the same records is a better one for extraction.

Long prompts and caches change the crossover

Gemini 3.1 Pro Preview’s input rate rises after a prompt exceeds 200,000 tokens.

Large prompts can put the whole request in a higher price tier. The listed long-context rates are $4 input and $15 output per million tokens for Sol; Gemini 3.1 Pro Preview charges $4 and $18 when its prompt exceeds 200,000 tokens. A hypothetical request with 300,000 input tokens and 2,000 output tokens therefore costs about $1.23 on Sol and $1.236 on Pro Preview before tools or caching. That near tie describes billing for this prompt, not equal document-analysis quality.

Cache economics depend on how often a prefix is reused. At the listed short-context rates, reading a hypothetical 100,000-token cached Sol prefix costs about $0.01, while writing it costs about $0.25. Reading the same-sized Gemini Pro Preview cache costs about $0.02 below its prompt threshold, and storing it for an hour adds about $0.45. Fresh input and output remain billable, so a changing document set cannot deliver the savings of a stable, frequently reused prefix.

Near Gemini Pro Preview’s threshold, prompt design can change the rate applied to a request. Supplying every source document may be appropriate when evidence is scattered across them; retrieving only relevant passages reduces input size when that narrower context preserves the facts needed for the answer.

Grounded research has a tool bill

OpenAI web search and Gemini Search grounding add different usage charges to a research request.

For a hypothetical grounded question with 4,000 prompt tokens, 1,000 output tokens, and one search call or query after Google’s shared free allowance is exhausted, the listed rates produce a base estimate of about $0.028 on Sol with OpenAI web search and $0.034 on Gemini 3.1 Pro Preview with Search grounding. Retrieved search-content tokens add to the OpenAI estimate. Additional Google Search queries add to the Gemini estimate, even when they serve one model request.

Several searches, substantial retrieved text, or a retry can therefore change the result more than the initial model-token price suggests. For research, count the cost of an answer whose claims are supported well enough for the product, including the search activity required to produce it.

Use benchmark position to select candidates

The Artificial Analysis Intelligence Index v4.2 update places GPT-6 Astra second behind Claude Fable 5.1 and identifies Gemini 3.5 Flash-Lite at the low-cost end of its output-token frontier; 40% of the index weighting uses private, held-out test sets. Its evaluations include agentic knowledge work and long-document reasoning. Those results signal different capability positions, but they do not provide acceptance rates for a particular extraction pipeline or codebase.

An aggregate rank can narrow the shortlist. The decisive comparison still uses the product’s own acceptance rule, because a benchmark rank spread can conceal differences between tasks. For a production decision, pair quality outcomes with the tokens and tool calls consumed to reach them.

Data terms can rule out a price tier

OpenAI’s API data controls state that API data is not used to train or improve its models unless the customer opts in, while default abuse-monitoring logs may contain prompts and responses for up to 30 days, subject to stated exceptions. Retention of application state and eligibility for Zero Data Retention depend on the endpoint and feature. A low token price does not resolve a workload’s retention requirements.

Under Google’s Gemini API terms, content sent through unpaid services may be used to improve products and reviewed by people; Gemini API access through a Cloud project with active billing is treated as paid, with prompts and responses excluded from product improvement but logged for a limited period for safety enforcement. A free-quota prototype and a billed deployment can therefore have different data-handling terms.

The economical production choice is the least expensive eligible tier that meets the workload’s acceptance standard. Short extraction may favor a low-cost model; coding or long-document work may justify a stronger one if it saves enough retries or review time. Search usage, cache reuse, and the applicable data terms belong in that calculation.