LLM ComparisonGPT-5.3 CodexGemini 3.1 Pro

GPT-5.3 Codex vs Gemini 3.1 Pro

Compare GPT-5.3 Codex and Gemini 3.1 Pro. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.3 CodexGemini 3.1 Pro
ProviderOpenAIGoogle
Model Typetexttext
Context Window400,000 tokens1,048,576 tokens
Input Cost
$1.75/ 1M tokens
$4.00/ 1M tokens
Output Cost
$14.00/ 1M tokens
$18.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5.3 Codex

OpenAI

1. Strongest Codex Model for Agentic Engineering

  • OpenAI positions GPT-5.3 Codex as its most capable agentic coding model to date.
  • Built for long-horizon software engineering tasks that require planning, iteration, and reliable code transformation across files.

2. Configurable Reasoning + Multimodal Input

  • Supports configurable reasoning effort from low to xhigh so teams can trade off depth against latency.
  • Accepts both text and image inputs while producing text output.

3. Large Context for Real Codebases

  • 400 k token context window helps it work across larger repositories, implementation plans, and supporting documentation.
  • Allows up to 128 k output tokens for longer code generations, patches, and technical write-ups.

4. Current Knowledge for Modern Dev Workflows

  • Knowledge cut-off of Aug 31 2025 keeps it aligned with newer frameworks, libraries, and tooling.
  • Supports streaming, function calling, and structured outputs for agent-style coding workflows.

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.

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