GPT-5.2 Codex vs Qwen-Flash

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. Ultra-fast, ultra-cheap

• Designed for mass-scale workloads.
• Excellent for rewriting, extraction, classification.

2. Limited reasoning but great utility

• High throughput, low latency.

3. Optional thinking mode

• Adds chain-of-thought when needed.

4. Supports context cache & batch calls

• Very cost-effective system design.