GPT-5.2 Codex vs Qwen3-Max

1. Optimized for Long-Horizon Coding Tasks

• OpenAI describes GPT-5.2 Codex as a highly intelligent coding model built for long-horizon, agentic coding work.
• Well suited to planning, refactoring, debugging, and multi-step implementation flows inside real codebases.

2. Adjustable Reasoning for Coding Work

• Supports configurable reasoning effort from low to xhigh depending on speed and quality needs.
• Accepts both text and image inputs while producing text output.

3. Large Context + Long Output

• 400 k token context window supports broad repository understanding and larger working sets.
• Allows up to 128 k output tokens for longer patches, code generation, and technical explanations.

4. Up-to-Date Model Snapshot

• Knowledge cut-off of Aug 31 2025 keeps it current with newer tools and frameworks.
• Supports streaming, function calling, and structured outputs for tool-driven coding workflows.

1. Best performance in Qwen3 series

• Handles complex multi-step reasoning.
• Excellent for agent programming and tool calling.

2. Massive context window

• 262K tokens enable long multi-document tasks.
• Useful for RAG pipelines, analysis, and long-form workflows.

3. Tiered pricing support

• More cost-efficient for small requests.
• Supports context caching for repeated inputs.

4. Strong general-purpose intelligence

• High accuracy in coding, reasoning, and structured tasks.
• Reliable for enterprise automation.