LLM ComparisonGPT-5.3 CodexGemini 1.0 Pro

GPT-5.3 Codex vs Gemini 1.0 Pro

Compare GPT-5.3 Codex and Gemini 1.0 Pro. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.3 CodexGemini 1.0 Pro
ProviderOpenAIGoogle
Model Typetexttext
Context Window400,000 tokens128,000 tokens
Input Cost
$1.75/ 1M tokens
$0.50/ 1M tokens
Output Cost
$14.00/ 1M tokens
$1.50/ 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 1.0 Pro

Google

1. Strong all-purpose performance

  • Designed as Google's balanced middle-tier model.
  • Handles a wide range of tasks: reasoning, writing, coding, and problem-solving.

2. Natively multimodal understanding

  • Trained from the ground up on text, images, audio, and video.
  • More consistent multimodal reasoning than stitched-together architectures.

3. Great cost-to-capability ratio

  • Offers much of Gemini Ultra's reasoning quality at a fraction of the cost.
  • Strong default choice for large-scale production workloads.

4. Reliable reasoning and factual performance

  • Performs well on benchmarks like MMLU, MMMU, and code reasoning.
  • Handles long-form analysis, multi-step reasoning, and structured problem solving.

5. Advanced coding capabilities

  • Supports major languages such as Python, Java, C++, Go.
  • Generates, edits, debugs, and explains code with high accuracy.
  • Powers advanced coding systems like AlphaCode 2.

6. Efficient and scalable

  • Optimized for Google TPUs for lower latency and faster inference.
  • Suitable for batch workloads, agents, and complex multi-step pipelines.

7. Strong multimodal reasoning

  • Understands math, physics, and scientific diagrams.
  • Handles mixed data inputs (charts + text, screenshots + instructions, etc.).

8. Enterprise-ready reliability

  • Available through Google AI Studio and Vertex AI.
  • Benefits from enterprise-grade governance, safety, privacy, and compliance.

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