1. Purpose-Built for Agentic Coding

• Designed specifically for environments where the model acts as an autonomous or semi-autonomous coding agent.
• Optimized for multi-step reasoning in code tasks such as planning, refactoring, debugging, file generation, and tool coordination.

2. Enhanced Coding Intelligence

• Extends GPT-5.1's advanced reasoning capabilities to handle complex software architecture decisions.
• Better accuracy in code generation across languages (JavaScript, Python, TypeScript, Go, Rust, etc.).
• Produces cleaner, more idiomatic code aligned with modern frameworks and best practices.

3. Superior Tool Use & Code Navigation

• Excels at reading, understanding, and transforming multi-file codebases.
• Works well with Codex workflows that simulate real developer tooling.
• Strong at following function signatures, constraints, and code patterns within an existing project.

4. Long-Range Context Awareness

• 400,000-token context window enables the model to ingest large repositories or multiple files simultaneously.
• Supports deep analysis of project structures, dependencies, and cross-file logic.

5. Multi-Modal Development Capabilities

• Accepts text + image input and output - suitable for tasks like:
  • Reading UI mockups or screenshots to generate code
  • Understanding architectural diagrams
  • Reviewing images of whiteboard sessions

6. Agentic Workflow Optimization

• Built to manage longer chains of thought and execution typically required in:
  • Automated code repair
  • Project bootstrapping
  • Linting and migration tasks
  • Long-running coding agents using planning + execution loops

7. Continually Updated Model Snapshot

• Codex-specific version receives regular upgrades behind the scenes.
• Ensures the latest coding improvements without requiring developers to update model names.

8. Reliable Instruction Following

• Highly consistent in honoring explicit constraints:
  • Code styles
  • Folder structures
  • API contracts
  • Framework conventions

9. Broad API Support

• Works across Chat Completions, Responses API, Realtime, Assistants, and more.
• Ideal for apps that need live, reasoning-heavy coding agents or generative dev environments.

1. State-of-the-art software engineering

• A notable upgrade over Opus 4.6 on the hardest coding tasks, with users reporting they can hand off work that previously required close supervision.
• Early partners reported double-digit gains on real-world benchmarks - e.g., Cursor saw CursorBench jump from 58% to 70%, and Rakuten-SWE-Bench resolution tripled versus Opus 4.6.
• Handles complex, long-running tasks with rigor: plans carefully, catches its own logical faults, and verifies its outputs before reporting back.

2. Long-horizon agent reliability

• Full 1M token context window at standard pricing, with state-of-the-art long-context consistency.
• Far fewer tool errors, stronger recovery from tool failures, and better follow-through on multi-step workflows - designed for async work like CI/CD, automations, and managing multiple agents in parallel.
• Stronger file-system-based memory, retaining useful notes across long, multi-session runs.

3. Sharper instruction following and honesty

• Takes instructions literally and precisely - existing prompts may need re-tuning since earlier models were more lenient.
• More honest about its own limits: reports missing data instead of fabricating plausible-but-wrong answers, and resists dissonant-data traps that tripped up Opus 4.6.

4. Substantially improved vision and multimodal reasoning

• Accepts images up to 2,576 px on the long edge (~3.75 MP) - over 3x more than prior Claude models.
• Unlocks dense-screenshot computer use, complex diagram extraction, and pixel-perfect reference tasks.
• Stronger document reasoning for enterprise analysis (e.g., 21% fewer errors than Opus 4.6 on Databricks' OfficeQA Pro).

5. Top-tier professional knowledge work

• State-of-the-art on the Finance Agent evaluation and GDPval-AA, with tighter, more professional finance analyses, models, and presentations.
• Strong on legal work - e.g., 90.9% on BigLaw Bench at high effort, with better-calibrated reasoning on review tables and ambiguous edits.
• Noted by design-focused partners as the best model for building dashboards and data-rich interfaces.

6. Modern effort and budget controls

• Introduces a new xhigh effort level between high and max for finer control over reasoning vs. latency.
• Task budgets (public beta) let developers guide token spend across long runs.
• Recommended to start with high or xhigh effort for coding and agentic use cases.