Best Value LLM Overall in 2026 — Cost-Adjusted Rankings
As of October 1, 2026, the top model in best value llm overall on the BenchLM leaderboard is Qwen3.7 Flash with a score of 377.3.
This ranking moves when models ship. Get the releases, price changes and retirements that affect your shortlist. Follow model changes
Full Rankings (116 models)
Key Takeaways
The best value model is Qwen3.7 Flash by Alibaba with a provisional Score/$ ratio of 377.31 (score: 49.1, output: $0.13/1M tokens).
The best open-weight model is MiMo-V2.6-Flash at position #2.
116 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.
About this ranking
Ranking data as of October 1, 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. A very cheap model with a modest overall score can rank high here, so read the score column next to the value column before you shortlist one 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.
Qwen3.7 Flash leads this ranking with a score of 377.31, followed by MiMo-V2.6-Flash (237) and Mercury 2.5 (234.53). There is a significant gap between the leading models and the rest of the field.
The best open-weight option is MiMo-V2.6-Flash (ranked #2 with a score of 237). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking uses provisional overall weighted scores from the active scoring formula. For detailed model profiles, click any model name above. To compare two specific models head-to-head, use the "vs #" links.
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