Solutions · Coding
Best LLMs for coding
Repo-scale edits, diffs, and green tests on the first try.
Coding routes optimize for SWE-Bench Verified pass rate, tool-use reliability inside editor loops, and long output windows for full-file rewrites. Pass auto:coding as the model to always land on the strongest coder available under your latency budget.
| # | Model | Score | SWE-Bench Verified | Why |
|---|---|---|---|---|
| 1 | claude-sonnet-4.5 | 96 | 77.2% | State-of-the-art on SWE-Bench Verified; best diff fidelity. |
| 2 | gpt-5-codex | 94 | 74.9% | Long output window, strongest at multi-file refactors. |
| 3 | claude-opus-4.5 | 93 | 74.5% | Highest planning quality; use for architectural changes. |
| 4 | gpt-5.5 | 91 | 72.0% | Generalist coder with excellent JSON + tool loops. |
| 5 | deepseek-coder-v3 | 89 | 68.4% | Open weights, unbeatable $/task for editor autocomplete. |
| 6 | qwen3-coder-480b | 88 | 67.1% | Open MoE, strong on Python + TypeScript refactors. |
| 7 | grok-code-fast-1 | 84 | 61.3% | Sub-100ms TTFT; ideal for inline suggestions. |
| 8 | codestral-2508 | 82 | 58.7% | Fill-in-the-middle specialist for IDE plugins. |
| 9 | devstral-medium | 80 | 55.9% | Agentic SWE loops; strong on shell + git tools. |
| 10 | qwen2.5-coder-32b | 78 | 52.4% | Cheapest capable coder for CI-time review bots. |
Why these models
- Ranked on SWE-Bench Verified, LiveCodeBench, and Aider polyglot edit success.
- Filtered to models with reliable diff output and 100K+ output windows.
- Balanced against $/task at typical 8K in / 4K out coding turns.
Drop-in example
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.llmcloud.ai/v1",
apiKey: process.env.LLMCLOUD_KEY,
});
const res = await client.chat.completions.create({
model: "auto:coding",
messages: [
{ role: "system", content: "Return a unified diff only." },
{ role: "user", content: "Add retry-with-backoff to fetchUser()." },
],
});