LLM ComparisonGPT-5.3 CodexGemini 3 Pro

GPT-5.3 Codex vs Gemini 3 Pro

Compare GPT-5.3 Codex and Gemini 3 Pro. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.3 CodexGemini 3 Pro
ProviderOpenAIGoogle
Model Typetexttext
Context Window400,000 tokens1,000,000 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 Pro

Google

1. State-of-the-art reasoning

  • Top performance across academic reasoning, scientific knowledge, math, and complex problem-solving.
  • Excels at long-horizon, multi-step workflows and deep logical interpretation.

2. World-leading multimodal capabilities

  • Natively understands text, images, videos, audio, and code.
  • Ranked highest on benchmarks like MMMU-Pro, Video-MMMU, ScreenSpot-Pro.

3. Exceptional coding + agentic workflows

  • Strong in competitive coding and real-world agentic tasks (SWE-Bench Verified, Terminal-Bench, LiveCodeBench).
  • Improved tool calling, planning, and execution for autonomous or semi-autonomous agents.

4. Powerful for long-context tasks

  • Effective at 128K-1M context windows with high retrieval accuracy.
  • Ideal for document-heavy workflows, research, analysis, multi-file coding, and multi-document reasoning.

5. Strong information synthesis and interpretation

  • Outperforms peers in chart reasoning, OCR, structured extraction, and screen understanding.
  • Excellent at combining multimodal inputs into coherent, concise answers.

6. High reliability for enterprise tasks

  • Benchmarks show superior factuality, grounding, and parametric knowledge.
  • Strong multilingual accuracy and global commonsense performance.

7. Optimized for production agents

  • Designed for complex multi-step planning, simultaneous task execution, and improved consistency.
  • Works across coding, research, creative workflows, UI generation, and data-heavy applications.

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