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Gemini 3.1 Pro

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gemini-3.1-pro

Gemini 3.1 Pro 是 Google 新一代旗舰多模态模型，具备超长上下文、顶级推理与代码能力。完全兼容 OpenAI 接口，只需把 model 改成 gemini-3.1-pro 即可在现有 SDK 中直接调用；默认低推理档（更快更省），传 reasoning_effort=high 切换高推理档（Preview 档计价）。

文本对话长上下文多模态OpenAI 兼容
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Try out Gemini 3.1 Pro right here (available after login)。

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About Gemini 3.1 Pro

Gemini 3.1 Pro is Google's next-generation flagship reasoning model, built for complex multi-step reasoning, long-context comprehension, and multimodal inputs such as text and images. On NezhaGate it is served through an OpenAI-compatible API: set the model to "gemini-3.1-pro" and point your base_url at NezhaGate to reuse the OpenAI SDK directly. It defaults to the fast low-effort tier; pass reasoning_effort="high" to switch to the deep-reasoning tier (billed at the Preview rate). Billing is pay-as-you-go, and failed requests are not charged.

Use cases

Long-document analysis

Summarize, extract from, and answer questions across contracts, reports, and papers, using the long context window to hold an entire document while keeping the reasoning coherent.

Complex reasoning & planning

Tackle multi-step reasoning, math and logic problems, task decomposition, and solution planning where a clear, structured chain of analysis matters.

Multimodal understanding

Combine text with image inputs for chart interpretation, screenshot Q&A, and visual description, all handled through a single chat interface.

Code generation & review

Generate, explain, and refactor code, leaning on the long context to read whole files or modules and assist with debugging and design review.

RAG knowledge Q&A

Act as the core model in retrieval-augmented generation, feeding retrieved passages alongside the user question to produce grounded, citable answers.

How to choose

Pick the flagship Gemini 3.1 Pro when you need the deepest reasoning, the longest context, and multimodal support; if low latency and cost-efficiency for everyday chat matter more, reach for Google's faster siblings Gemini 3 Flash, Gemini 3.5 Flash, or Gemini 2.5 Flash. To compare flagships across providers, switch to GPT-5.5 or Claude Sonnet 4.6 the same way: they all live behind the one OpenAI-compatible API, so only the model field changes.

FAQ

What is Gemini 3.1 Pro?
Gemini 3.1 Pro is Google's next-generation flagship reasoning model, aimed at complex reasoning, long-context, and text-plus-image multimodal tasks. On NezhaGate it is exposed through an OpenAI-compatible API under the model name "gemini-3.1-pro", with two effort tiers selected by the reasoning_effort parameter: low (default) and high.
How do I call Gemini 3.1 Pro with the OpenAI SDK?
Keep using the OpenAI SDK you already have: point the client's base_url at NezhaGate, supply your NezhaGate API key, and call the chat.completions endpoint with model set to "gemini-3.1-pro". No new SDK or request-shape changes are required. Add reasoning_effort="high" (or use the model name gemini-3.1-pro-high) when you need deeper reasoning.
What is the difference between the low and high tiers? Does the old gemini-3.1-pro-preview id still work?
Both tiers are the same model at different reasoning depths: low (the default) responds faster at a lower price, ideal for everyday chat and bulk tasks; high thinks deeper for hard reasoning and complex code, billed at the Preview rate. Switch by passing reasoning_effort="high". The legacy id gemini-3.1-pro-preview keeps working — it maps to the high tier at its existing price, so old integrations need no change.
What is Gemini 3.1 Pro best for?
It is best for tasks that demand deep reasoning, long-document handling, or text-and-image multimodal understanding, such as long-form analysis, complex problem solving, code review, and RAG knowledge Q&A. For latency-sensitive or low-cost high-volume chat, consider the faster Flash models instead.
Gemini 3.1 Pro vs Gemini 3 Flash: how do I choose?
Gemini 3.1 Pro is the flagship model with deeper reasoning and a longer context, ideal for demanding and multimodal work; Gemini 3 Flash is the fast, low-latency tier suited to everyday chat where speed and cost-efficiency come first. Both are served through the same OpenAI-compatible API, so switching is just a change to the model field.

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