LLM ComparisonGemini 3.1 ProGemini 1.5 Flash

Gemini 3.1 Pro vs Gemini 1.5 Flash

Compare Gemini 3.1 Pro and Gemini 1.5 Flash. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGemini 3.1 ProGemini 1.5 Flash
ProviderGoogleGoogle
Model Typetexttext
Context Window1,048,576 tokens1,000,000 tokens
Input Cost
$4.00/ 1M tokens
$0.07/ 1M tokens
Output Cost
$18.00/ 1M tokens
$0.30/ 1M tokens

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Strengths & Best Use Cases

Gemini 3.1 Pro

Google

1. Google's most advanced reasoning Gemini model

  • Designed to solve complex problems across multimodal inputs, including text, audio, images, video, PDFs, and full code repositories.
  • Google highlights improved software engineering behavior, better agentic performance, and stronger usability in domains like finance and spreadsheets.

2. Large multimodal context with substantial output room

  • Supports a 1,048,576 token input context window for large repositories, long documents, and multi-source workflows.
  • Allows up to 65,536 output tokens for longer answers, plans, and code generations.

3. More efficient thinking with expanded controls

  • Improves token efficiency and reasoning performance across use cases.
  • Adds the MEDIUM thinking_level option to better balance cost, speed, and quality.

4. Strong support for production agents

  • Supports grounding with Google Search, code execution, function calling, structured outputs, context caching, RAG, and chat completions.
  • Also offers a custom-tools endpoint tuned for agentic workflows that mix bash-like tools with custom code tools.

Gemini 1.5 Flash

Google

1. Extremely fast and cost-efficient

  • Designed for ultra-low latency inference.
  • Handles high-throughput real-time applications and large-scale pipelines.

2. Strong multimodal capabilities

  • Accepts text, images, audio, video, and PDFs.
  • Efficient cross-modal understanding suitable for classification, extraction, and captioning.

3. Excellent for long-context tasks

  • Supports up to 1M tokens, enabling analysis of long documents, transcripts, and entire codebases.
  • Performs well on long-context translation and summarization.

4. Optimized for production workloads

  • Low operational cost and fast inference make it ideal for enterprise automation.
  • Great for chatbots, customer support systems, and background agent tasks.

5. High throughput with scalable rate limits

  • Flash variants support extremely high RPM for high-traffic environments.

6. Reliable performance on everyday tasks

  • Good at chat, rewriting, transcription, extraction, and structured reasoning.
  • More efficient than Pro for tasks that don't require deep reasoning.

7. Ideal for multimodal high-volume apps

  • Strong performance on captioning, OCR-style extraction, audio transcription, and video understanding.

8. Designed for developer workflows

  • Supports function calling, structured output, and integration with the Gemini API and Vertex AI.

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