BenchLM recommendation
Best Value LLM Overall in 2026 — Cost-Adjusted Rankings
As of July 23, 2026, the top model in best value llm overall on the BenchLM leaderboard is Ministral 3 3B (Reasoning) with a score of 395.2.
Last verified: July 23, 2026
This ranking answers the simplest question: which model gives you the most benchmark performance per dollar? It divides each model's overall weighted score (across all 8 categories) by its output token price. The leaders here are generalist value picks — strong across coding, reasoning, agentic, knowledge, and more, at prices that won't break your API budget. Start here if you need a single cost-effective model for mixed workloads.
Unless noted otherwise, ranking surfaces on this page use BenchLM's provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Ministral 3 3B (Reasoning) leads this ranking with a score of 395.2, followed by Ministral 3 8B (Reasoning) (269.2) and Ministral 3 14B (Reasoning) (246.7). There is a significant gap between the leading models and the rest of the field.
The best open-weight option is Ministral 3 3B (Reasoning) (ranked #1 with a score of 395.2). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking is based on provisional overall weighted scores across BenchLM.ai's scoring formula 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.
How to choose
Full Rankings (107 models)
Key Takeaways
The best value model is Ministral 3 3B (Reasoning) by Mistral with a provisional Score/$ ratio of 395.2 (score: 39.5, output: $0.1/1M tokens).
The best open-weight model is Ministral 3 3B (Reasoning) at position #1.
107 models are included in this ranking.
Score in Context
What these scores mean
Value scores divide the weighted overall score by output token price (per 1M tokens). Higher means more capability per dollar. Models with no listed price are excluded.
Known limitations
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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