Gemini and OpenAI can both support a broad application, but endpoint choice matters as much as model choice. Compare the workflow you will actually deploy, particularly when it uses persistent interactions, media or asynchronous processing.

Side by side

This table compares each provider’s lowest documented baseline input-price model. Baseline prices use standard or short-context conditions and exclude separate free-tier and off-peak rows when paid standard rates exist. The full pricing reference preserves all documented conditions.

API model comparison
AttributeGemini 2.5 Flash Preview TTSgpt-5.6-luna
ProviderGoogleOpenAI
Official identifiergemini-2.5-flash-preview-ttsgpt-5.6-luna
Input USD / 1MFree$0.2
Output USD / 1MFree$1.2
Cached input USD / 1MNot documented$0.02
Context tokens8,1921,050,000
Maximum output tokens16,384128,000
Modalitiestexttext
OpenAI-compatible endpointNot documentedYes
SDK languagesPython, JavaScript, Go, JavaPython, JavaScript, Go, Java, C#
ReferenceGemini 2.5 Flash Preview TTSgpt-5.6-luna

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Read the source and pricing condition beside a value before comparing it. The table describes published records; it does not establish which model will perform best on your task.

Pricing differences

Gemini’s pricing separates free and paid conditions and distinguishes eligible cache and batch operations. Read the tier for the exact API path and model you intend to use. Official documentation.

OpenAI lists token, hosted-tool and media billing categories separately. Build a task-level budget that includes the selected operations. Official documentation.

For a long-document request, retain the assembled prompt length with the estimate. For a multimodal request, retain the actual input types and output properties. Treating every workflow as ordinary text can hide the charge that drives the decision.

Google pricing and OpenAI pricing explain the full charge categories. Use the cost calculator with the same observed workload for both.

Limits and free tiers

Gemini’s account limits depend on the project and its tier. Model and endpoint eligibility must be checked alongside an apparent free allowance. Official documentation.

Compare the two active accounts rather than infer identical limits from similarly named plans. A prototype with a small free allowance may still need a queue, a stop condition and a deliberate paid fallback. Review Gemini free access and OpenAI access evidence.

If funding and throughput are both sufficient, evaluate burst handling before launch. Keep the first capacity rejection visible so retries do not hide an unsuitable traffic pattern.

Models and modalities

Gemini’s catalog identifies its supported task surfaces and model inputs. Check the selected model instead of inferring capability from a family name. Official documentation.

Compare image understanding, generated media, embeddings and speech only within the relevant operation. The existence of several modalities in a provider catalog does not mean one model performs them all. Use the live model links to narrow the table to a valid pair.

Developer experience (SDKs, compatibility, docs)

Google documents Interactions as an application interface and also provides an OpenAI compatibility path. These are distinct contracts; compatibility does not make every native capability interchangeable. Official documentation.

Gemini’s compatibility guide describes the supported mapping for OpenAI clients. Verify optional fields and output handling when reusing an existing client. Official documentation.

Choose the endpoint deliberately before committing to a cache or asynchronous design. Retain native errors and completion states in the adapter. A minimal shared text call is a useful migration starting point, but it does not validate every feature in the larger application.

Which to pick for … (3 scenarios)

A multimodal prototype. Start with the model that supports the exact input and output path with the least additional work. Test representative files and incomplete responses, then compare the full operation cost.

An existing OpenAI client. Evaluate Gemini through the documented compatibility path on a saved payload. If the application needs native interaction features, budget the explicit migration rather than assuming a base-URL change completes it.

Offline document processing. Select a supported batch path and verify cache eligibility for that endpoint. Keep job identifiers and item failures so a deferred run can be reconciled with its actual output.

Choose with a saved evaluation set and an explicit operating budget. Google setup and reference and OpenAI setup and reference link the first-request steps.

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Frequently asked questions

Is Gemini’s compatibility path the same as its native API?
No. It is a documented mapping with its own supported surface. Check the features required by your application.
Does a free model entry cover every API operation?
No. Eligibility belongs to the documented model, tier and operation. Check paid-only features and the actual project.
Can I compare media using only text-token rates?
No. Include the relevant media units and generated output properties.
Should an existing client force the provider choice?
It is one integration cost. Compare that cost with the native features and measured task results you need.
What should I verify before moving an offline job?
Check endpoint and model eligibility, result collection, item errors, usage accounting and the allowed completion window.

Sources

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