Knowledge benchmarks test factual accuracy, expert-level science, and professional comprehension. This ranking divides each model's weighted knowledge score (GPQA, SuperGPQA, MMLU-Pro, HLE, FrontierScience, SimpleQA) by output token price. For RAG pipelines, research assistants, and Q&A systems where accurate knowledge retrieval matters but API costs add up, the value leaders here offer the best accuracy per dollar.
According to BenchLM.ai, Gemini 3.1 Flash-Lite leads this ranking with a score of 116.08, followed by Gemini 2.5 Flash (80.55) and DeepSeek Coder 2.0 (55.55). 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 #3 with a score of 55.55). 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 knowledge 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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