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google/gemini-2.5-pro

Gemini 2.5 Pro is Google’s flagship multimodal reasoning model, optimized for long-context understanding and complex vision-assisted tasks.

Google Vision Up to 1M Tokens
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Free Trial Credit On first TopUp of minimum $5
$1.00

api_example.sh

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "google/gemini-2.5-pro",
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "What is in this image? Describe the main elements."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
          }
        }
      ]
    }
  ],
  "max_tokens": 8192,
  "temperature": 0.2,
  "stream": true,
  "top_p": 1
}'

Technical Specifications

Model Architecture & Performance

Variant Gemini 2.5 Pro
Context Length Up to 1M Tokens
Architecture Sparse Mixture-of-Experts (MoE) Transformer with native multimodal support
Developers Google DeepMind

Pricing

Pay-per-use, no commitments

Input Tokens $1.25/1M Tokens
Output Tokens $10.00/1M Tokens
Cached Input Tokens $0.31/1M Tokens

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 0.2 Controls randomness. Higher values can increase creativity.
max_tokens number 8192 Maximum number of tokens to generate in the response.
top_p number 1 Nucleus sampling: considers tokens with top_p probability mass.
reasoning_effort select medium Adjusts the depth of reasoning and problem-solving effort (quality vs latency).

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
Sparse MoE architecture for strong performance and efficiency
Large context window (up to 1M input tokens)
Supports function calling and structured outputs
May be slower / more expensive than Flash variants

Use cases

Recommended applications for this model

Long-document and codebase analysis with vision context
High-quality visual question answering
Agentic document workflows with tool usage and structured outputs
Structured extraction from PDFs, screenshots, and mixed media

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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