Best for coding 0 ranked by coding index

The best AI model for coding depends on your use case. For agentic coding tasks (editing files, running commands, fixing repos end-to-end), models with high Terminal-Bench scores perform best inside tools like Claude Code, Cursor, Codex, and Aider. For pure code generation from specifications, LiveCodeBench scores predict quality. For research and scientific computing, SciCode results matter most.

In production, model choice interacts with cost and latency. A slightly lower-ranked model at 10x cheaper per token may be the better default for high-volume autocomplete, while the top-ranked model is reserved for complex multi-file refactors. AI gateways like Requesty let you route between models dynamically: use the best model for hard tasks and a fast, cheap model for simple completions, all through one API.

Access all top coding models through a single OpenAI-compatible API at requesty.ai. Automatic prompt caching saves 40-60% on token costs, and failover ensures your coding agent never stops due to a single provider outage.

Scores for Coding Index come from Artificial Analysis, an independent benchmarking service. When a model is served by several providers (Anthropic direct, AWS Bedrock, Google Vertex), one canonical entry represents the model family so the ranking is not padded with duplicates. Benchmarks measure specific skills: validate on your own workload before committing.

Built from the same catalog the router reads at request time, rebuilt daily.