Raw benchmark scores only tell half the story. This ranking divides each model's weighted coding score by its output token price, surfacing models that deliver the most coding capability per dollar spent. A model scoring 70 at $1/1M tokens outranks one scoring 80 at $15/1M tokens here — because for the same budget you get far more coding work done. Use this alongside the standard coding leaderboard to find the sweet spot between performance and cost for your coding workflows.
According to BenchLM.ai, Gemini 3.1 Flash-Lite leads this ranking with a score of 76.62, followed by DeepSeek Coder 2.0 (47.76) and Gemini 2.5 Flash (39.01). There is a significant gap between the leading models and the rest of the field.
The best open-weight option is DeepSeek Coder 2.0 (ranked #2 with a score of 47.76). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking is based on weighted averages across the scoring benchmarks in coding tracked by BenchLM.ai. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
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