LLM ComparisonGPT-5.3 CodexClaude 3.5 Sonnet

GPT-5.3 Codex vs Claude 3.5 Sonnet

Compare GPT-5.3 Codex and Claude 3.5 Sonnet. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.3 CodexClaude 3.5 Sonnet
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens200,000 tokens
Input Cost
$1.75/ 1M tokens
$3.00/ 1M tokens
Output Cost
$14.00/ 1M tokens
$15.00/ 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.

Claude 3.5 Sonnet

Anthropic

1. Intelligence & Reasoning

  • Outperforms previous Claude models and competitor LLMs across major benchmarks.
  • Excels in graduate-level reasoning (GPQA), knowledge tasks (MMLU), and coding (HumanEval).
  • Handles nuance, humor, and complex instructions with human-like clarity.

2. Speed & Efficiency

  • Runs 2x faster than Claude 3 Opus, making it ideal for real-time and high-volume workflows.
  • Cost-effective pricing: $3/M input tokens and $15/M output tokens.
  • Supports a 200K token context window, enabling rich, long-form reasoning.

3. Coding Capabilities

  • Solves significantly more coding and bug-fix tasks (64% vs Opus's 38% in internal evaluations).
  • Can autonomously write, edit, and execute code when tool use is enabled.
  • Strong at translating and modernizing legacy codebases.

4. Vision Strength

  • Best vision model in the Claude family, surpassing Opus on vision benchmarks.
  • Excellent at interpreting charts, graphs, and imperfect images.
  • Reliable text extraction from low-quality visuals for retail, logistics, finance, etc.

5. Agentic Workflows

  • Highly capable for multi-step task orchestration.
  • Performs well as the engine for agents requiring reasoning, planning, and tool-calling abilities.

6. Content Quality

  • Produces natural, relatable writing with improved tone, style, and context awareness.
  • Strong at long-form content creation and editing.

7. Safety & Reliability

  • Rated ASL-2, meeting Anthropic's safety standards.
  • Undergoes extensive red-teaming and external evaluation (UK AISI & US AISI).
  • Not trained on user data without explicit permission.

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