Best LLM for long-context refactors (2026)
For whole-repo analysis and large refactors, context window size is the constraint that matters most. Ranked by maximum context window, filtered to coding-capable models.
🏆 Top pick: Llama 4 Scout
Llama 4 Scout handles 10M of context — enough to load a small repo or a full framework's docs in one shot.
The ranked list
| # | Model | Context window | Max output | SWE-bench | Input price |
|---|---|---|---|---|---|
| 1 | Llama 4 Scout | 10M | 8K | 52% | $0.20 |
| 2 | Kimi K3 | 2M | 16K | 70% | $0.60 |
| 3 | Claude Sonnet 4.6 | 1M+ | 128K | 64% | $3 |
| 4 | Claude Opus 4.7 | 1M+ | 128K | 72% | $5 |
| 5 | DeepSeek V4 Flash | 1M+ | 384K | 48% | $0.14 |
| 6 | DeepSeek V4 Pro | 1M+ | 384K | 62% | $0.44 |
| 7 | Grok 4.3 | 1M+ | 128K | 52% | $1.25 |
| 8 | Grok 4.20 | 1M+ | 128K | 58% | $1.25 |
Why each made the list
1 Llama 4 Scout
On-prem 10M-token context analysis, doc/codebase RAG without external chunking
2 Kimi K3
Agentic coding at low cost, ultra-long context, China-region deployments
3 Claude Sonnet 4.6
Day-to-day coding, fast agentic loops, balanced cost/quality
4 Claude Opus 4.7
Complex refactors, agentic coding, hard debugging, deep reasoning
5 DeepSeek V4 Flash
Ultra-cheap high-quality coding, bulk classification, context-heavy tasks
6 DeepSeek V4 Pro
Complex reasoning, agentic coding, hard debugging with long context
7 Grok 4.3
Fast general-purpose coding with native web and X search agent capabilities
8 Grok 4.20
Deep reasoning, multi-step agentic coding, massive context tasks
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