Best Models for Coding (2026)
The AI coding landscape moves fast. This page tracks which models perform best for different coding tasks, based on public benchmarks, community reports, and our own testing.
Last updated: February 7, 2026
Quick Recommendation
Section titled “Quick Recommendation”| Use Case | Best Model | Runner-Up |
|---|---|---|
| Complex multi-file refactors | Claude Opus 4.6 | GPT-5.2-Codex |
| Quick edits & code review | Claude Sonnet 4.5 | GPT-5.2 |
| Large codebase understanding | Gemini 3 Pro | Claude Opus 4.6 |
| Agentic workflows (tool use) | Claude Opus 4.6 | GPT-5.2-Codex |
| Speed-optimized coding | Gemini 3 Flash | Claude Sonnet 4.5 |
| Budget-friendly coding | DeepSeek V3 | Qwen 2.5 Coder 32B |
| Local / privacy-first | Qwen 2.5 Coder 32B | DeepSeek Coder V2 |
Detailed Model Rankings
Section titled “Detailed Model Rankings”Tier 1: Frontier Models
Section titled “Tier 1: Frontier Models”Claude Opus 4.6 (Anthropic)
Section titled “Claude Opus 4.6 (Anthropic)”- SWE-bench Verified: 80.8% · source
- Context window: 200K tokens
- Strengths: Near-identical to Opus 4.5 on SWE-bench while improving on reasoning and instruction following. Top-tier for agentic coding — multi-file refactors, debugging complex race conditions, and working with CLAUDE.md / AGENTS.md configurations. Strong extended thinking capabilities for hard architectural problems.
- Weaknesses: Slightly lower SWE-bench than Opus 4.5 (80.8% vs 80.9%). Expensive. Can be overkill for simple tasks.
- Best agents: Claude Code, AdaL CLI, Amp, Cline
- Pricing: $15 / $75 per 1M tokens (input/output)
GPT-5.2-Codex (OpenAI)
Section titled “GPT-5.2-Codex (OpenAI)”- SWE-bench Verified: 80.0% · source
- SWE-bench Pro: State-of-the-art · Terminal-Bench 2.0: State-of-the-art
- Context window: 128K tokens (with context compaction)
- Strengths: Optimized specifically for agentic coding workflows. Leads on SWE-Bench Pro and Terminal-Bench 2.0 — benchmarks designed for real-world agentic performance. Strong long-horizon task completion with context compaction for extended sessions.
- Weaknesses: Smaller base context than Claude/Gemini. Codex-specific model requires separate API access.
- Best agents: OpenAI Codex, GitHub Copilot
- Pricing: $2 / $8 per 1M tokens (input/output) · source
Claude Opus 4.5 (Anthropic)
Section titled “Claude Opus 4.5 (Anthropic)”- SWE-bench Verified: 80.9%
- Context window: 200K tokens
- Strengths: Highest raw SWE-bench score of any model. Deep reasoning, excellent at complex architectural decisions and legacy code understanding.
- Weaknesses: Being superseded by 4.6 in practice. Very expensive.
- Best agents: Claude Code, AdaL CLI
- Pricing: $15 / $75 per 1M tokens (input/output)
Gemini 3 Flash (Google)
Section titled “Gemini 3 Flash (Google)”- SWE-bench Verified: 78.0% · source
- Context window: 1M tokens
- Strengths: Remarkably strong for a “Flash” model — actually outperforms Gemini 3 Pro on coding benchmarks. Massive context window ideal for large monorepos. Excellent speed-to-quality ratio. Best value frontier model for coding.
- Weaknesses: Less refined tool-use than Claude. Some reports of inconsistency on very long sessions.
- Best agents: Gemini CLI, Cursor (as alternative model)
- Pricing: ~$0.15 / $0.60 per 1M tokens (input/output)
Gemini 3 Pro (Google)
Section titled “Gemini 3 Pro (Google)”- SWE-bench Verified: ~75% · source
- Context window: 1M tokens
- Strengths: Strong multimodal capabilities. 1M context for massive codebases. Good at reasoning through complex problems.
- Weaknesses: Surprisingly outperformed by Flash on coding. Reports of memory issues and code deletion in long sessions. source
- Best agents: Gemini CLI
- Pricing: $1.25 / $10 per 1M tokens (input/output)
Claude Sonnet 4.5 (Anthropic)
Section titled “Claude Sonnet 4.5 (Anthropic)”- SWE-bench Verified: 77.2%
- Context window: 200K tokens
- Strengths: Best balance of quality and cost in the Claude family. Strong at agentic coding without the Opus price tag. Excellent for day-to-day development workflows.
- Weaknesses: Gap to Opus on the hardest problems.
- Best agents: Claude Code, AdaL CLI, Amp, Cline
- Pricing: $3 / $15 per 1M tokens (input/output)
Tier 2: Previous Generation (Still Strong)
Section titled “Tier 2: Previous Generation (Still Strong)”GPT-5.2 (OpenAI)
Section titled “GPT-5.2 (OpenAI)”- SWE-bench Verified: 80.0% · source
- Context window: 128K tokens
- Strengths: Significant improvements in general intelligence, long-context understanding, and agentic tool-calling over GPT-5. Strong vision capabilities.
- Weaknesses: Codex variant is better for pure coding tasks.
- Best agents: GitHub Copilot, Cursor
- Pricing: $2 / $8 per 1M tokens (input/output)
Claude Sonnet 4 (Anthropic)
Section titled “Claude Sonnet 4 (Anthropic)”- SWE-bench Verified: 72.7%
- Context window: 200K tokens
- Strengths: Still very capable. Well-tested across many agent frameworks. Good instruction following.
- Weaknesses: Superseded by Sonnet 4.5 on all benchmarks.
- Best agents: Claude Code, AdaL CLI, Cline
- Pricing: $3 / $15 per 1M tokens (input/output)
GPT-4.1 (OpenAI)
Section titled “GPT-4.1 (OpenAI)”- SWE-bench Verified: 54.6%
- Context window: 128K tokens
- Strengths: Fast, clean code generation. Strong instruction following and coding style adherence.
- Weaknesses: Significantly behind current frontier on agentic tasks.
- Best agents: GitHub Copilot, Cursor
- Pricing: $2 / $8 per 1M tokens (input/output)
Tier 3: Open-Source & Local
Section titled “Tier 3: Open-Source & Local”DeepSeek V3 (DeepSeek)
Section titled “DeepSeek V3 (DeepSeek)”- SWE-bench Verified: 42.0%
- Context window: 128K tokens
- Strengths: Exceptional value. Open-weight with strong Python and web framework support.
- Weaknesses: Weaker on less common languages. Agentic tool-use less reliable.
- Pricing: $0.27 / $1.10 per 1M tokens (input/output)
Qwen 2.5 Coder 32B (Alibaba)
Section titled “Qwen 2.5 Coder 32B (Alibaba)”- HumanEval: 65.9%
- Context window: 128K tokens
- Strengths: Best open-source coding model. Runs on consumer hardware (~20GB VRAM).
- Weaknesses: Weaker at multi-step reasoning.
- Best for: Local development, privacy-sensitive environments
Benchmark Explainer
Section titled “Benchmark Explainer”| Benchmark | What It Measures | Why It Matters |
|---|---|---|
| SWE-bench Verified | Fix real GitHub issues end-to-end | Most realistic agentic coding measure |
| SWE-bench Pro | Harder subset of real-world issues | Tests frontier agent capability |
| Terminal-Bench 2.0 | Agentic terminal-based coding tasks | Tests real development workflows |
| Aider Polyglot | Multi-language code editing accuracy | Tests edit-apply workflows |
| HumanEval | Function-level code generation | Classic but limited |
| LiveCodeBench Pro | Competitive programming | Tests algorithmic reasoning |
Our recommendation: Focus on SWE-bench Verified for agentic use cases and Terminal-Bench 2.0 for real-world development workflows.
Choosing the Right Model
Section titled “Choosing the Right Model”Need the absolute best for hard problems? → Claude Opus 4.6 (80.8% SWE-bench)
Need strong agentic coding on a budget? → Gemini 3 Flash (78% SWE-bench, cheapest frontier)
Need OpenAI ecosystem? → GPT-5.2-Codex (SOTA on Terminal-Bench)
Working with a massive codebase? → Gemini 3 Flash/Pro (1M context)
Good balance of quality and cost? → Claude Sonnet 4.5 (77.2% SWE-bench)
On a tight budget? → DeepSeek V3 (API) or Qwen 2.5 Coder (local)How We Update This Page
Section titled “How We Update This Page”- Weekly benchmark scan: We check SWE-bench, Aider, Terminal-Bench, and LiveCodeBench leaderboards every Saturday
- Community reports: We aggregate feedback from r/ClaudeAI, r/ChatGPTCoding, r/GoogleGeminiAI, and developer forums
- Own testing: We use these models daily through AdaL CLI and share real observations
- New releases: When a major model drops, we test and add it within 48 hours
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