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.
Unless noted otherwise, ranking surfaces on this page use BenchLM's provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Bottom line: Knowledge tasks don't require expensive reasoning models. Gemini 3.1 Flash-Lite leads value, and DeepSeek Coder 2.0 offers strong raw knowledge at low cost.
According to BenchLM.ai, DeepSeek V4 Flash (Max) leads this ranking with a score of 240.61, followed by DeepSeek V4 Flash (High) (223.12) and DeepSeek V4 Flash (158.78). There is a significant gap between the leading models and the rest of the field.
The best open-weight option is DeepSeek V4 Flash (Max) (ranked #1 with a score of 240.61). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking is based on provisional 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.
DeepSeek V4 Flash (Max)
DeepSeek · 1M
Score: 67.4 · $0.28/1M
DeepSeek V4 Flash (High)
DeepSeek · 1M
Score: 62.5 · $0.28/1M
DeepSeek V4 Flash
DeepSeek · 1M
Score: 44.5 · $0.28/1M
Gemini 3.1 Flash-Lite leads knowledge value — most knowledge capability per dollar.
DeepSeek Coder 2.0 strong raw knowledge scores at very low cost.
Gemini 2.5 Flash good knowledge value with broader model capabilities.
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The best value model is DeepSeek V4 Flash (Max) by DeepSeek with a provisional Score/$ ratio of 240.61 (score: 67.4, output: $0.28/1M tokens).
The best open-weight model is DeepSeek V4 Flash (Max) at position #1.
53 models are included in this ranking.
Value scores divide the weighted knowledge score by output token price (per 1M tokens). Higher means more capability per dollar. Models with no listed price are excluded.
Value rankings favor cheap models even if absolute performance is modest. A model scoring half as well at one-tenth the price wins on value — but may not meet your quality bar. Always check raw scores alongside value rankings.
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