GPT-5.5
OpenAI · 1M context · $5/$30 per 1M
Complex reasoning · Coding · Professional workflows
9.8Coding
9.7Tools
9.8Reasoning
Coding and agent model guide
Pick models for coding agents, not leaderboard screenshots.
Use this page to decide which model should plan, edit, review, and handle bulk work in an AI coding-agent stack. It combines coding quality, tool use, context length, and API cost into practical routing guidance.
Shortlist
Best Models for Agentic Coding
Use frontier models for planning and risky edits. Use cheaper or local models for repetitive, low-risk work.
GPT-5.4
OpenAI · 1M context · $2.5/$15 per 1M
Coding · Agents · Tool integration
9.8Coding
9.7Tools
9.5Reasoning
Claude Opus 4.7
Anthropic · 1M context · $5/$25 per 1M
Complex reasoning · Agentic coding · Critical decisions
9.7Coding
9.5Tools
9.8Reasoning
GPT-5.2-Codex
OpenAI · 400K context · $1.75/$14 per 1M
Coding-focused tasks · Type inference · Agentic coding
9.7Coding
9.4Tools
9.3Reasoning
Claude Sonnet 4.6
Anthropic · 1M context · $3/$15 per 1M
Balanced performance · Production workloads · Cost-efficient
9.4Coding
9.1Tools
9.3Reasoning
Gemini 3.1 Pro Preview
Google · 1M context · $2/$12 per 1M
Multimodal tasks · Long context · Search integration
9.5Coding
9.3Tools
9.5Reasoning
DeepSeek V3
DeepSeek · 128K context · $0.27/$1.1 per 1M
Budget coding · High-volume · Cost-sensitive
8.8Coding
8.5Tools
8.9Reasoning
Llama 4 (405B)
Meta · 128K context · $2/$8 per 1M
Self-hosted · Open source · Customizable
9Coding
8.7Tools
9.1Reasoning
Routing pattern
A Practical Coding-Agent Stack
The best setup is usually a router, not one model doing every step. Split planning, implementation, review, and bulk work.
Planner
GPT-5.5 or Claude Opus 4.7
Use the strongest reasoning model to decompose tasks, choose files, and decide when to stop.
Coder
GPT-5.4 or GPT-5.2-Codex
Use a coding-optimized model for implementation loops, test fixes, and repository edits.
Reviewer
Claude Opus 4.7
Use a second frontier model for architecture review, hidden assumptions, and regression risk.
Bulk Work
Claude Sonnet 4.6, DeepSeek V3, or local models
Route repetitive linting, extraction, and low-risk edits to lower-cost models.
Workflow map
What to Use for Each Coding Workflow
Choose based on the job: long context, autonomy, privacy, and retry cost matter as much as raw coding score.
| Workflow | Primary model | Fallback | Why |
|---|---|---|---|
| Large refactor | Claude Opus 4.7 | GPT-5.4 | Prioritize context, careful planning, and review quality. |
| Autonomous feature build | GPT-5.5 | GPT-5.4 | Keep planning and execution separate for cleaner diffs. |
| Bug fix from failing tests | GPT-5.2-Codex | GPT-5.4 | Give the model test output, touched files, and reproduction steps. |
| Repo Q&A / codebase search | Gemini 3.1 Pro Preview | Claude Sonnet 4.6 | Long context helps, but still pair it with retrieval. |
| High-volume code review | Claude Sonnet 4.6 | DeepSeek V3 | Use cheaper models for first-pass comments, then escalate risky files. |
| Private codebase | Llama 4 (405B) | GPT-OSS-120B | Prefer open or local deployment when source cannot leave your environment. |
Need a recommendation for your exact workflow?
Use the model picker for budget, context, tool-use, and deployment constraints.