Gemini 3.1 Pro API in 2026: Pricing, Real-World Performance & Getting Started
Gemini 3.1 Pro launched quietly but has quickly become one of the most competitive frontier models for developers who need serious context length. The headline spec is a genuine 1-million-token context window — and independent benchmarks are showing reliable recall up to roughly 900,000 tokens, which means the long-context capability is a real production feature, not just a number on a spec sheet. For teams working with large codebases, long legal documents, or extended conversation histories, that's a meaningful shift from what was available six months ago.
The pricing math is also compelling. At roughly $1.25 per million input tokens, Gemini 3.1 Pro runs at approximately half the cost of GPT-5.4 for comparable workloads. Add native multimodal support — images, video frames, audio, and code can all go into a single API call — and the model starts to look like a strong default for long-context development work. Google has also made the onboarding path straightforward through AI Studio, with free-tier access and a getting-started codelab available for developers evaluating the model.
If you haven't run a serious benchmark of Gemini 3.1 Pro against your current stack yet, now is a good time. The combination of context depth and price efficiency makes it worth testing against whatever you're using today for heavy-lifting inference tasks.