Can Gemini 3.5 Flash handle million-token contexts?

Yes. Gemini 3.5 Flash supports a 1 million token context window, making it suitable for repository-scale reasoning, long PDFs, and multi-document workflows.

How does Gemini 3.5 Flash compare to Gemini 3.1 Pro for coding?

Google reports that Gemini 3.5 Flash outperforms Gemini 3.1 Pro on agentic and coding benchmarks including Terminal-Bench 2.1 and MCP Atlas.

Does the Gemini 3.5 Flash API support multimodal inputs?

Yes. Gemini 3.5 Flash accepts text, images, audio, video, and PDF inputs through the Gemini API.

What tools and integrations are available in the Gemini 3.5 Flash API?

The model supports function calling, code execution, structured outputs, Google Search grounding, Maps grounding, file search, and URL context support.

Is Gemini 3.5 Flash suitable for AI agents and autonomous workflows?

Yes. Google specifically optimized Gemini 3.5 Flash for long-horizon agentic execution, tool orchestration, and persistent AI assistant workflows.

What are the current limitations of Gemini 3.5 Flash?

Gemini 3.5 Flash currently does not support native image generation, audio generation, or Live API conversational streaming.

When should developers choose Gemini 3.5 Flash instead of Claude Sonnet 4?

Gemini 3.5 Flash is a strong choice when low-latency multimodal reasoning, large context handling, and Google ecosystem integration are more important than premium long-form writing quality.

What benchmark scores has Gemini 3.5 Flash achieved?

Google reports benchmark results including 76.2% on Terminal-Bench 2.1, 83.6% on MCP Atlas, and 84.2% on CharXiv Reasoning.

Overkommelig Gemini 3.5 Flash API | text-to-text

Technical Specifications of Gemini 3.5 Flash

Item	Gemini 3.5 Flash
Provider	Google
Model family	Gemini 3.5
Official model ID	gemini-3.5-flash
Input types	Text, image, video, audio, PDF
Output types	Text
Context window	1 million tokens
Max output tokens	~65K output tokens
Primary strengths	Agentic workflows, coding, multimodal reasoning
Tool support	Function calling, code execution, search grounding, structured outputs, URL context, file search
Thinking support	Adjustable thinking/reasoning levels
Safety framework	Google Frontier Safety Framework

What is Gemini 3.5 Flash?

Google Gemini 3.5 Flash is Google's flagship high-speed multimodal reasoning model optimized for agentic execution, coding, and long-horizon workflows. It extends the Gemini Flash series with substantially stronger reasoning and software engineering capabilities while maintaining low-latency inference characteristics.

Unlike earlier Flash models primarily focused on lightweight inference, Gemini 3.5 Flash is designed for persistent AI agents, multi-step coding systems, and enterprise automation pipelines. Google positions it as its strongest agentic Flash-tier model to date.

Main Features of Gemini 3.5 Flash

1M token long-context support: Handles extremely large repositories, lengthy documentation, PDFs, transcripts, and multi-session workflows in a single prompt context.
Strong agentic execution: Optimized for multi-step autonomous workflows, tool orchestration, terminal tasks, and long-running AI agents.
Advanced coding performance: Outperforms Gemini 3.1 Pro on several coding and agentic benchmarks including Terminal-Bench and MCP Atlas.
Native multimodal reasoning: Accepts text, images, audio, video, and PDFs for unified reasoning tasks.
Production-grade tooling: Supports structured outputs, function calling, code execution, grounding with Google Search and Maps, and file search.
Configurable reasoning/thinking modes: Developers can tune latency versus reasoning depth using thinking-level controls.

Benchmark Performance of Gemini 3.5 Flash

Google-reported benchmark results position Gemini 3.5 Flash among the strongest agentic Flash-tier models currently available:

Benchmark	Gemini 3.5 Flash
Terminal-Bench 2.1	76.2%
GDPval-AA	1656 Elo
MCP Atlas	83.6%
CharXiv Reasoning	84.2%

These scores indicate major gains in autonomous execution, multimodal reasoning, and software engineering reliability compared with earlier Gemini Flash variants.

Gemini 3.5 Flash vs Other Models

Capability	Gemini 3.5 Flash	Gemini 3.1 Pro	Claude Sonnet 4
Context window	1M tokens	Large-context	Large-context
Agentic workflows	Excellent	Strong	Strong
Coding performance	Very strong	Strong	Excellent
Inference speed	Optimized Flash latency	Slower	Moderate
Multimodal inputs	Native multimodal	Native multimodal	Vision + text
Tool ecosystem	Extensive Google tooling	Extensive	Strong API tooling

Key Differences

vs Gemini 3.1 Pro: Gemini 3.5 Flash delivers better coding and autonomous task execution while maintaining significantly faster inference.
vs Claude Sonnet 4: Claude often remains stronger in nuanced long-form reasoning and writing quality, while Gemini 3.5 Flash emphasizes speed, agent execution, and Google ecosystem integration.
vs GPT-series reasoning models: Gemini 3.5 Flash is particularly competitive in multimodal agent workflows and large-context orchestration, especially for enterprise automation use cases.

Known Limitations of Gemini 3.5 Flash

Does not currently support native image or audio generation outputs.
Live conversational APIs are not supported on this model tier.
Community benchmarks show mixed performance across certain specialized evaluation tasks, especially vision-heavy niche workflows

How to Access Gemini 3.5 Flash API

Step 1: Get API Access

Log in to cometAPI. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.

cometapi-key

Step 2: Send Requests to Gemini 3.5 Flash API

Select the “` gemini-3.5-flash” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. base url is Gemini Generating Content

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Process Responses

The API returns structured candidate responses including generated text, citations, safety metadata, and optional tool outputs.

Priser for Gemini 3.5 Flash

Udforsk konkurrencedygtige priser for Gemini 3.5 Flash, designet til at passe til forskellige budgetter og brugsbehov. Vores fleksible planer sikrer, at du kun betaler for det, du bruger, hvilket gør det nemt at skalere, efterhånden som dine krav vokser. Opdag hvordan Gemini 3.5 Flash kan forbedre dine projekter, mens omkostningerne holdes håndterbare.

Comet-pris (USD / M Tokens)	Officiel Pris (USD / M Tokens)	Rabat
Indtast:$1.2/M Output:$7.2/M	Indtast:$1.5/M Output:$9/M	-20%

Eksempelkode og API til Gemini 3.5 Flash

Få adgang til omfattende eksempelkode og API-ressourcer for Gemini 3.5 Flash for at strømline din integrationsproces. Vores detaljerede dokumentation giver trin-for-trin vejledning, der hjælper dig med at udnytte det fulde potentiale af Gemini 3.5 Flash i dine projekter.

Python
JavaScript
Curl

from google import genai
from google.genai import types
import os

# Get your CometAPI key from https://www.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com"

client = genai.Client(
    http_options={"api_version": "v1beta", "base_url": BASE_URL},
    api_key=COMETAPI_KEY,
)

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Explain how parallel agentic execution works in three sentences.",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="minimal"),
    ),
)

print(response.text)

Python Code Example

from google import genai
from google.genai import types
import os

# Get your CometAPI key from https://www.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com"

client = genai.Client(
    http_options={"api_version": "v1beta", "base_url": BASE_URL},
    api_key=COMETAPI_KEY,
)

response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Explain how parallel agentic execution works in three sentences.",
    config=types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_level="minimal"),
    ),
)

print(response.text)

JavaScript Code Example

// Get your CometAPI key from https://www.cometapi.com/console/token, and paste it here
const api_key = process.env.COMETAPI_KEY;
const base_url = "https://api.cometapi.com/v1beta";
const model = "gemini-3.5-flash";
const operator = "generateContent";

async function main() {
  const response = await fetch(`${base_url}/models/${model}:${operator}`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: api_key,
    },
    body: JSON.stringify({
      contents: [
        {
          parts: [
            {
              text: "Explain how parallel agentic execution works in three sentences.",
            },
          ],
        },
      ],
      generationConfig: {
        thinkingConfig: {
          thinkingLevel: "MINIMAL",
        },
      },
    }),
  });

  const data = await response.json();
  console.log(data.candidates[0].content.parts[0].text);
}

await main();

Curl Code Example

#!/bin/bash

curl "https://api.cometapi.com/v1beta/models/gemini-3.5-flash:generateContent" \
  -H "Authorization: $COMETAPI_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      {
        "parts": [
          {
            "text": "Explain how parallel agentic execution works in three sentences."
          }
        ]
      }
    ],
    "generationConfig": {
      "thinkingConfig": {
        "thinkingLevel": "MINIMAL"
      }
    }
  }'

Versioner af Gemini 3.5 Flash

Årsagen til, at Gemini 3.5 Flash har flere øjebliksbilleder kan omfatte potentielle faktorer såsom variationer i output efter opdateringer, der kræver ældre øjebliksbilleder for konsistens, at give udviklere en overgangsperiode til tilpasning og migration, og at forskellige øjebliksbilleder svarer til globale eller regionale slutpunkter for at optimere brugeroplevelsen. For detaljerede forskelle mellem versioner, henvises der til den officielle dokumentation.

version
gemini-3.5-flash