LLM ComparisonGPT-5.3 CodexClaude 4.6 Opus

GPT-5.3 Codex vs Claude 4.6 Opus

Compare GPT-5.3 Codex and Claude 4.6 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.3 CodexClaude 4.6 Opus
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens1,000,000 tokens
Input Cost
$1.75/ 1M tokens
$5.00/ 1M tokens
Output Cost
$14.00/ 1M tokens
$25.00/ 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 4.6 Opus

Anthropic

1. Anthropic's top model for coding and agents

  • Anthropic positions Opus 4.6 as its most intelligent model for building agents and coding.
  • It builds on Opus 4.5 with higher reliability and precision for professional software engineering, complex agentic workflows, and high-stakes enterprise tasks.

2. Strong frontier performance on real agent benchmarks

  • Anthropic reports state-of-the-art results across coding and agentic evaluations.
  • Public benchmark highlights include 65.4% on Terminal-Bench 2.0, 72.7% on OSWorld, and 90.2% on BigLaw Bench.

3. Best fit for long-horizon, high-context work

  • Supports up to a 1M token context window in beta and up to 128K output tokens.
  • Designed for long-running tasks that need sustained planning, careful debugging, code review, and strong context retention.

4. Advanced reasoning controls and workflow support

  • Supports adaptive thinking and the effort parameter, including the new max effort level.
  • Anthropic also introduced fast mode, compaction, and dynamic filtering with web search and web fetch for Opus 4.6-era agent workflows.

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