GPT-5.3 Codex vs Claude 4.5 Opus

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.

1. Maximum capability with more practical pricing

• Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
• It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

• Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
• It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

• Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
• This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

• Added enhanced computer use with a zoom action for inspecting detailed screen regions.
• Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.