G

Google Gemini 1.5 Flash

Google
Vision
1M
Context
$0.0750
Input /1M
$0.3000
Output /1M
23K
Max Output
Run API Call Open in Chat

Google Gemini 1.5 Flash

Model Overview

Property Value
Model ID google/gemini-flash-1.5
Full Name Google: Gemini 1.5 Flash
Provider Google
Release Date May 14, 2024
Model Family Gemini 1.5

Description

Gemini 1.5 Flash is a foundation model that performs well at a variety of multimodal tasks such as visual understanding, classification, summarization, and creating content from image, audio and video. The model excels at processing visual and text inputs including photographs, documents, infographics, and screenshots.

Flash is optimized for high-volume, high-frequency tasks where cost and latency are critical considerations. It achieves comparable quality to other Gemini Pro models at a significantly reduced cost on most common tasks.

Key Strengths

  • Visual understanding and classification
  • Document and infographic processing
  • Screenshot analysis
  • Content summarization
  • High-volume chat applications
  • On-demand content generation
  • Cost-effective inference at scale

Technical Specifications

Specification Value
Context Window 1,048,576 tokens
Context Length 1,000,000 tokens (1M)
Max Output Tokens 22,937 tokens
Input Modalities Text, Image
Output Modalities Text
Supports Reasoning No
Knowledge Cutoff April 2023

Pricing

LangMart Pricing (Per Million Tokens)

Type Price
Input (Text/Image) $0.075
Output $0.30

Cost Comparison

Model Input Cost Output Cost
Gemini 1.5 Flash $0.075/M $0.30/M
Gemini 1.5 Pro $3.50/M $10.50/M
Gemini 2.0 Flash $0.10/M $0.40/M
Gemini 2.5 Flash $0.30/M $2.50/M

Gemini 1.5 Flash offers significant cost savings compared to Pro models while maintaining quality for most common tasks.

Supported Parameters

Parameter Type Description
temperature float Controls randomness/creativity of responses (0.0-2.0)
top_p float Nucleus sampling parameter for diversity (0.0-1.0)
top_k integer Limits token selection to top K candidates
max_tokens integer Maximum number of tokens to generate
stop array Stop sequences to end generation 
{
  "temperature": 0.7,
  "top_p": 0.95,
  "top_k": 40,
  "max_tokens": 8192
}

Limitations

  • Does not support reasoning/thinking mode
  • Output limited to text (no image/audio generation)
  • Knowledge cutoff at April 2023
  • May not match Pro model quality for complex reasoning tasks
Model Context Use Case
google/gemini-flash-1.5-8b 1M tokens Smaller, faster variant
google/gemini-pro-1.5 2M tokens Higher quality, higher cost
google/gemini-2.0-flash-001 1M tokens Newer version with improvements
google/gemini-2.5-flash 1M tokens Latest Flash generation

Providers

Google AI Studio

Property Value
Adapter GoogleAIStudioGeminiAdapter
Base URL https://generativelanguage.googleapis.com/v1beta
Terms of Service https://cloud.google.com/terms/

LangMart routes requests to optimal providers based on prompt size and parameters, with fallback providers to maintain service availability.

Usage Examples

LangMart API (cURL)

curl https://api.langmart.ai/v1/chat/completions \
  -H "Authorization: Bearer $LANGMART_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-flash-1.5",
    "messages": [
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms."
      }
    ],
    "temperature": 0.7,
    "max_tokens": 1024
  }'

LangMart API (Python)

import requests

response = requests.post(
    "https://api.langmart.ai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {LANGMART_API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "model": "google/gemini-flash-1.5",
        "messages": [
            {"role": "user", "content": "Explain quantum computing in simple terms."}
        ],
        "temperature": 0.7,
        "max_tokens": 1024
    }
)

print(response.json())

OpenAI SDK Compatible (Python)

from openai import OpenAI

client = OpenAI(
    base_url="https://api.langmart.ai/v1",
    api_key="YOUR_LANGMART_API_KEY"
)

completion = client.chat.completions.create(
    model="google/gemini-flash-1.5",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    temperature=0.7,
    max_tokens=1024
)

print(completion.choices[0].message.content)

Multimodal Request (Image + Text)

import base64
import requests

# Load image and encode to base64
with open("image.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

response = requests.post(
    "https://api.langmart.ai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {LANGMART_API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "model": "google/gemini-flash-1.5",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "Describe what you see in this image."
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/png;base64,{image_data}"
                        }
                    }
                ]
            }
        ]
    }
)

print(response.json())

LangMart Gateway Usage

curl -X POST https://api.langmart.ai/v1/chat/completions \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-flash-1.5",
    "messages": [
      {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Usage Statistics

Based on recent LangMart activity data, Gemini 1.5 Flash shows high adoption:

Date Requests Prompt Tokens Completion Tokens
Sept 25, 2025 909,014 906.6B 145.6M
Sept 24, 2025 1.57M 1.09T 171.2M

This indicates the model is heavily used for high-volume applications.

Best Use Cases

  1. Chat Assistants - Fast responses for conversational AI
  2. Document Analysis - Processing PDFs, screenshots, and documents
  3. Content Generation - Quick content creation at scale
  4. Classification Tasks - Categorizing text and images
  5. Summarization - Condensing long documents
  6. High-Volume Applications - Cost-effective for large-scale deployments

Terms of Use

Usage of Gemini is subject to Google's Gemini Terms of Use.

References

Document generated: December 23, 2025 Data sources: LangMart, Google AI, Helicone, LLM Price Check