Gemini 3.5 Flash API Руководство по модели

Краткий вывод

• Official capabilities include a 1M context window, up to 64K output, plus function calling, structured output, search tools, and code execution.
• Compared with classic lightweight Flash tiers, it is better suited to complex Q&A, coding, long-document handling, and agent workflows.
• The recommended integration path remains OpenAI-compatible chat so existing SDKs, SSE streaming, and retry middleware can be reused.

Ключевые возможности

• High intelligence with Flash latency：It is not just a low-cost fallback tier; it aims to handle deeper understanding, planning, and generation while staying responsive.
• 1M long context：Well suited to long-document summarization, codebase analysis, knowledge synthesis, large prompts, and extended conversations.
• Structured and tool-based output：Function calling, structured JSON, search tools, and code execution make it a stronger fit for agentic workflows.
• Coding and technical generation：Useful for code explanation, unit test generation, refactor proposals, SDK wrappers, and technical drafts.
• OpenAI-compatible migration path：Chat Completions-style payloads reduce switching friction from GPT- or Claude-oriented stacks.
• Streaming interaction：SSE streaming supports chat UIs, terminal assistants, and IDE-style progressive rendering.

Когда использовать

• When you need more reasoning and coding strength than a classic lightweight model without giving up too much responsiveness.
• When handling long-context tasks such as document Q&A, codebase analysis, long-session memory, or retrieval-grounded answers.
• When you need structured JSON, function calling, or search/tool integration for agents.

Когда не использовать

• For ultra-simple, ultra-cheap bulk templating tasks where higher intelligence is unnecessary.
• For pure image or video generation workloads; use dedicated media models instead.

Особенности работы

• The recommended path is POST /v1/chat/completions using an OpenAI-compatible request shape.
• stream=true returns SSE chunks, while stream=false returns a standard full completion object.
• If your workflow depends on structured output or tools, validate schemas, tool choice, and fallback behavior on a small slice first.

Минимальный запрос

{
  "model": "gemini-3.5-flash",
  "messages": [
    {
      "role": "system",
      "content": "You are a senior full-stack engineer. Summarize the approach first, then provide the smallest runnable code."
    },
    {
      "role": "user",
      "content": "Write a Node.js function that reads a CSV, removes duplicate emails, and returns summary stats."
    }
  ],
  "temperature": 0.2,
  "max_tokens": 500,
  "stream": false
}

Минимальный ответ

{
  "id": "chatcmpl_xxxxxxxx",
  "object": "chat.completion",
  "created": 1747699200,
  "model": "gemini-3.5-flash",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 120,
    "completion_tokens": 260,
    "total_tokens": 380
  }
}

Ключевые параметры

Частые ошибки

FAQ

1. What is Gemini 3.5 Flash best for?
   It is best for text and coding assistant tasks that need strong intelligence without giving up throughput and response speed.
2. How is it different from a classic Flash model?
   The main difference is a higher intelligence ceiling, making it more suitable for complex Q&A, long context, and tool-enabled workflows.
3. What is the fastest way to integrate it?
   Use the OpenAI-compatible Chat Completions path first so you can reuse existing messages, streaming, and retry middleware.
4. When should max_tokens be constrained carefully?
   Constrain max_tokens carefully for long-context, coding, and structured-output tasks to avoid bloated responses, cost drift, or timeouts.

Связанные API

• Chat Completions
• Models
• Responses API