Gemini 3.1 Pro Preview
gemini-3.1-pro-previewGemini 3.1 Pro Preview is Google's frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Built on the multimodal foundation of the Gemini 3 series, it combines high-precision reasoning across text, image, video, audio, and code with a 1M-token context window for large-scale tasks. The 3.1 update introduces measurable gains on SWE benchmarks and real-world coding environments, along with stronger autonomous execution in structured domains such as finance and spreadsheet-based workflows. Designed for advanced development and agentic systems, it improves long-horizon stability and tool orchestration while adding a new medium thinking level to better balance cost, speed, and performance. Gemini 3.1 Pro Preview is well suited for agentic coding, structured planning, multimodal analysis, financial modeling, spreadsheet automation, and high-context enterprise applications.
- Context
- 1.0M tokens
- Endpoint
Service Status
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Pricing
Quick Start
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="gemini-3.1-pro-preview", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="gemini-3.1-pro-preview",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )Supported Parameters
API docsmodelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Compare with Other Models
See how this model compares to others from the same provider.
Gemini 4 Argon
Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.
- Context
- 1M
- Input
- $2.00/M
- Output
- $10.00/M
Gemini 3.1 Flash Lite Image (Google Vertex AI)
Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.
- Context
- N/A
- Input
- — (Not priced per input token)
- Output
- — (Not priced per output token)
Gemini 3.1 Flash Lite Image (Google AI Studio)
Gemini 3.1 Flash Lite Image served from Google AI Studio. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.
- Context
- N/A
- Input
- — (Not priced per input token)
- Output
- — (Not priced per output token)
Gemini 3.1 Flash Image (Google Vertex AI)
Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048.
- Context
- N/A
- Input
- — (Not priced per input token)
- Output
- — (Not priced per output token)