Best LLM for Coding
Ranked on SWE-Bench, HumanEval, and dollars-per-1M output tokens. Balanced for autonomous and assistive coding workflows.
Updated April 2026. Top 3 this month: GPT-5, Codestral, DeepSeek V3.2.
How we rank
Choosing an LLM for coding comes down to three things: how well it turns specifications into working code, how well it reasons about large repositories, and how much it will cost once you wire it into CI or an agent loop. We weight SWE-Bench heaviest because it best predicts real-world coding-agent success, followed by HumanEval for short-form correctness, and a price pillar so the recommendation survives contact with a finance review.
Pillars and weights: SWE-Bench (50%) · HumanEval (30%) · price (20%). Our full methodology is published on the methodology page.
Top ranked models
| Rank | Model | Provider | Input $/1M | Output $/1M | Context |
|---|---|---|---|---|---|
| 1 | GPT-5 | OpenAI | $1.25 | $10.00 | 200,000 |
| 2 | Codestral | Mistral | $0.20 | $0.60 | 32,000 |
| 3 | DeepSeek V3.2 | DeepSeek | $0.27 | $1.10 | 128,000 |
| 4 | DeepSeek V3 | DeepSeek | $0.27 | $1.10 | 128,000 |
| 5 | Granite 20B | IBM | $0.80 | $0.80 | 8,192 |
| 6 | Qwen 2.5 | Alibaba (Qwen) | $0.50 | $1.50 | 131,072 |
| 7 | GPT-4.1 | OpenAI | $2.00 | $8.00 | 1,000,000 |
| 8 | GPT-5.1 | OpenAI | $1.25 | $10.00 | 200,000 |
| 9 | GPT-4o | OpenAI | $2.50 | $10.00 | 128,000 |
| 10 | Claude Sonnet 4.6 | Anthropic | $3.00 | $15.00 | 200,000 |
Tips for coding
- Prefer a model with a large context window if your repo is bigger than ~200 files.
- Use batch pricing for CI / nightly refactor jobs; interactive IDE work stays on the standard price.
- Check function-calling reliability before committing to an agentic flow.
Frequently asked questions
Which LLM is best for coding?
As of April 2026, our weighted top 3 are GPT-5, Codestral, DeepSeek V3.2.
Is Claude better than GPT for coding?
Claude wins long-horizon refactoring; GPT wins short-burst correctness. The right answer depends on your workload mix — see the scoring pillars below.
Do I need a fine-tuned model?
Rarely. Fine-tuning on proprietary code still helps, but for 90% of shops a strong frontier model with RAG over the repo gets you most of the way.
What about open-weight options?
DeepSeek and Meta Llama variants are competitive on price. We list their hosted pricing here; self-host economics live in our Shadow AI audit tool.
Related tasks
Want to model your own workload? Use the volume and switch-cost calculators on the main tool page. Sign in with Google to unlock compare-my-prompt with real tokenizer counts.
Data refreshed daily via our snapshot cron. See our public JSON API for programmatic access.