Gemini 3.1 Pro

Google's current Pro flagship: the model to reach for when a job leans on deep reasoning or precise retrieval across very long documents — capable today, but with a successor weeks away.

Gemini 3.1 Pro, released in February 2026, is the higher-end tier of the Gemini line — the model Google positions for the hardest work: dense reasoning, careful analysis, and finding the right detail in very large documents. Where the newer Gemini 3.5 Flash is the fast everyday default, 3.1 Pro is the deliberate, heavier model you call in when accuracy on a difficult problem matters more than speed or cost.

Here's the honest, slightly awkward part: the recently-released 3.5 Flash now beats 3.1 Pro on coding and agentic benchmarks. What 3.1 Pro still wins is the genuinely hard reasoning (it leads on academic-reasoning tests like Humanity's Last Exam) and precise long-context retrieval — finding a specific fact buried in a hundred-thousand-tokenThe basic unit of text a model reads and writes. Tokens are roughly three-quarters of a word in English — so 100 tokens is about 75 words. Models don't see letters or words directly; they see tokens. Pricing is almost always quoted per million tokens, and context windows are measured in tokens rather than words. document, where the larger model's depth shows. So it remains the right tool for a specific, demanding slice of work.

The bigger context: Google announced Gemini 3.5 Pro at I/O 2026 and said it ships "next month" (June 2026). So 3.1 Pro is a model in transition — fully capable now, but about to be succeeded. If you're choosing a Pro-tier Gemini for the long term, it's worth watching for 3.5 Pro before committing (we're tracking it on the watchlist).

• Deep-reasoning tasks — dense legal, financial, or scientific analysis where correctness beats speed.
• Precise long-context retrieval — finding specific facts across very large documents or codebases.
• MultimodalA model that can handle more than one type of input or output — typically text plus images, sometimes plus audio or video. "GPT-4 Vision" and "Llama 3.2 11B Vision" are multimodal models that accept both text and images. A text-only model is called "unimodal" but nobody uses that term; text-only is the assumed default. reasoning over images, audio, and video where you want the higher Pro tier.
• Existing Gemini Pro workloads that need a stable model today while you evaluate the move to 3.5 Pro.

• Everyday or high-volume work — Gemini 3.5 Flash is faster, cheaper, and now better at coding/agentic tasks.
• New long-term Pro deployments — Gemini 3.5 Pro is imminent; consider waiting (see the watchlist).
• Self-hosting or data-in-house requirements — it's closed; use Gemma 4 31B.
• Cost-sensitive use — at $2/$12 it's the priciest Gemini tier short of the unreleased 3.5 Pro.

Proprietary, like all of Gemini — accessed through Google's API under Google Cloud's terms, with commercial rights to your outputs but none to the model. The diligence items are Google's Generative AI Prohibited Use Policy and the Gemini API / Vertex AI data terms (with the usual Google Cloud enterprise data-residency options). No weightsThe numerical values inside a trained model that encode everything it has learned. A model is, functionally, a giant list of weights — tens of billions of numbers for a mid-sized model, hundreds of billions for a frontier model. "Open-weight" means those numbers are published. "Downloading the weights" means getting the actual file you'd need to run the model yourself., no self-hosting; for that, Gemma is Google's open line.

Against Gemini 3.5 Flash, 3.1 Pro is slower and pricier and now trails on coding, but still leads on the hardest reasoning and long-context precision — a narrowing but real advantage until 3.5 Pro lands. Against the closed flagships from OpenAI and Anthropic, Gemini Pro's calling cards are long-context retrieval and native multimodality, especially video. Against open alternatives like Gemma 4 31B or the China-based open flagships, the trade is the usual closed-vs-open one: managed frontier capability and no self-hosting, versus weightsThe numerical values inside a trained model that encode everything it has learned. A model is, functionally, a giant list of weights — tens of billions of numbers for a mid-sized model, hundreds of billions for a frontier model. "Open-weight" means those numbers are published. "Downloading the weights" means getting the actual file you'd need to run the model yourself. you own and run yourself.