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