LLM ComparisonGPT-5.2 CodexGemini 1.5 Flash

GPT-5.2 Codex vs Gemini 1.5 Flash

Compare GPT-5.2 Codex and Gemini 1.5 Flash. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.2 CodexGemini 1.5 Flash
ProviderOpenAIGoogle
Model Typetexttext
Context Window400,000 tokens1,000,000 tokens
Input Cost
$1.75/ 1M tokens
$0.07/ 1M tokens
Output Cost
$14.00/ 1M tokens
$0.30/ 1M tokens

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Strengths & Best Use Cases

GPT-5.2 Codex

OpenAI

1. Optimized for Long-Horizon Coding Tasks

  • OpenAI describes GPT-5.2 Codex as a highly intelligent coding model built for long-horizon, agentic coding work.
  • Well suited to planning, refactoring, debugging, and multi-step implementation flows inside real codebases.

2. Adjustable Reasoning for Coding Work

  • Supports configurable reasoning effort from low to xhigh depending on speed and quality needs.
  • Accepts both text and image inputs while producing text output.

3. Large Context + Long Output

  • 400 k token context window supports broad repository understanding and larger working sets.
  • Allows up to 128 k output tokens for longer patches, code generation, and technical explanations.

4. Up-to-Date Model Snapshot

  • Knowledge cut-off of Aug 31 2025 keeps it current with newer tools and frameworks.
  • Supports streaming, function calling, and structured outputs for tool-driven coding workflows.

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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