PROVIDER MODELS

Google Gemini models

Gemini 3.7 Flash is Google's production-ready Flash model for coding, AI agents, and multimodal reasoning, with a 1M-token context window.

Core specificationsSource data checked September 1, 2026
gemini-3.1-pro-preview1M / 64K · $2 / $12 below 200K input · Preview release
gemini-3.7-flash1M / 64K · $0.75 / $3.75 through Dec 31, 2026 · Stable release
gemini-3.6-flashSee current model page · See current pricing · Stable release

Model guides

Open current model pages and retained retirement notices

Google model

Gemini 3 Pro Preview

A historical Gemini 3 Pro preview route retained in Model Plaza; verify account availability because Google has ended the native preview.

1,048,576 context · Exact API ID gemini-3-pro-preview
Google model

Gemini 2.5 Pro

Google's higher-capability Gemini 2.5 model for complex reasoning, coding, and long multimodal inputs.

1,048,576 context · Exact API ID gemini-2.5-pro
Google model

Gemini 3.8 Flash

Google's newer Flash model focused on extended software-engineering tasks, autonomous agents and complex enterprise workflows, with multimodal input.

1,048,576 context · Exact API ID gemini-3.8-flash
Google model

Gemini 3.7 Flash

Google's Flash model for everyday coding, multimodal reasoning and tool-driven tasks, with configurable thinking depth.

1,048,576 context · Exact API ID gemini-3.7-flash
Google model

Gemini 3.6 Flash

Google's earlier Flash model for iterative coding, agent execution and spatial reasoning, with multimodal input and a focus on speed.

1,048,576 context · Exact API ID gemini-3.6-flash
Google model

Gemini 3 Flash Preview

A Gemini 3 Flash preview route for fast multimodal reasoning and agent experiments; preview behavior can change.

1,048,576 context · Exact API ID gemini-3-flash-preview
Google model

Gemini 2.5 Flash

Google's efficient Gemini 2.5 model for multimodal understanding, everyday reasoning, and high-volume applications.

1,048,576 context · Exact API ID gemini-2.5-flash
Google model

Gemini 2.0 Flash

A previous-generation Gemini Flash model retained in Model Plaza for existing integrations; Google lists the native model as shut down.

1,048,576 context · Exact API ID gemini-2.0-flash
02

Choosing and integrating

01

Choose the right model in the family

Start with the workload and service target, then pick the smallest model that passes a representative evaluation set.

  • Start with the stable Gemini 3.7 Flash release for multimodal coding and AI agent workflows.
  • Keep Gemini 3.6 Flash when its established behavior matters more than the newest features.
  • Use preview models only when they can be switched off quickly and a tested fallback is ready.
02

Inputs, outputs, context and tools

  • Gemini 3.7 Flash accepts text, image, video, audio and PDF input and returns text.
  • It publishes a 1,048,576-token input limit and 65,536-token output limit.
  • Supported features include function calling, structured output, code execution, search grounding, file search and URL context.
  • Thinking levels are low, medium and high; medium is the documented default.
03

Where this family fits

These are strong starting points—not universal recommendations. Test them with your own prompts, tools and acceptance criteria.

  • Understanding video, audio, and documents through one request format.
  • Coding agents that combine structured tools with large context windows.
  • Analysis backed by web search when current facts matter.
  • Large-scale extraction and classification with explicit schemas.
04

Reasoning, context and cost controls

  • Use context caching for repeated large prefixes and compare the cache economics with real traffic.
  • Batch processing can reduce vendor cost for asynchronous workloads, but route support must be confirmed.
  • Pin an exact stable ID for production instead of relying on a moving preview alias.
05

Send a minimal request

This example uses the first provider model ID in the table. If Model Plaza shows a different ID for your API key, copy the Model Plaza value exactly.

request.example
curl https://app.llmfly.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMFLY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-3.1-pro-preview",
    "messages": [{"role": "user", "content": "Explain this request in three bullets."}],
    "stream": false
  }'
06

Limits and compatibility notes

  • Gemini 3.7 Flash does not publish audio or image generation and does not support the Live API.
  • Computer use is preview functionality and needs separate risk controls.
  • Pricing and preview status are date-sensitive; recheck them before launch.
07

Production evaluation checklist

  • Run 20–50 representative tasks and define pass criteria before testing.
  • Measure time to first token, total latency, input and output tokens, tool retries, and cost per successful task.
  • Test the endpoint, streaming mode, tool schemas, and structured output used by the application.
  • Verify 400, 401, 404, 429, and temporary 5xx handling with bounded retries.
  • Keep the model ID in configuration and validate a fallback before moving production requests.
08

What to confirm in Model Plaza

Model Plaza shows which model IDs your API key can use, their billing multipliers, availability, supported endpoints, and compatibility notes. Confirm these details with a small request before deployment.

  • Gemini 3.7 Flash supports low, medium and high thinking levels.
  • Its published input modalities include text, image, video, file and audio.
  • Preview aliases can change; prefer a stable exact ID for production when one meets the task.

Frequently asked questions

Can I send these provider model IDs directly to LLMFly AI?

Not always. Use the model ID shown for your API key in Model Plaza.

Are these LLMFly AI prices?

No. They are provider reference prices. Check Model Plaza and the usage record for the amount charged by LLMFly AI.

Does the largest context window always produce a better answer?

No. Curated context usually improves relevance, latency, and cost. Treat the published window as a limit, not a target.

How should I choose a reasoning level?

Start with the documented default, then change it only when your evaluation shows a measurable quality or latency benefit.

What should a production fallback cover?

It should accept the same request format, tools, and safety checks, and your application should define when switching is allowed.

Confirm the model available to your API key

Open Model Plaza before adding a model ID to production configuration.

Open Model Plaza