Best Value LLM for Math in 2026 — Cost-Adjusted Rankings
As of October 8, 2026, the top model in best value llm for math on the BenchLM leaderboard is Kimi K2.5 with a score of 20.7.
Bottom line: value here is math score per output dollar, so check each row's raw math score before you shortlist it.
This ranking moves when models ship. Get the releases, price changes and retirements that affect your shortlist. Follow model changes
Full Rankings (6 ranked)
Key Takeaways
The best value model is Kimi K2.5 by Moonshot AI with a provisional Score/$ ratio of 20.7 (score: 62.1, output: $3/1M tokens).
The best open-weight model is Kimi K2.5 at position #1.
6 models are included in this ranking.
Score in Context
What these scores mean
Value scores divide the weighted math 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 8, 2026
This ranking divides each model's weighted math score (AIME, BRUMO, MATH-500) by its output token price, so it shows which models give the most math capability per dollar. A cheap model with a low math score can rank high here, so read the score column next to the value column before you shortlist one.
Unless noted otherwise, ranking surfaces on this page use BenchLM’s provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.
Kimi K2.5 leads this ranking with a score of 20.7, followed by Kimi K2.6 (17.75) and GLM-5 (17.66). There is meaningful separation between the top models, suggesting genuine performance differences.
The best open-weight option is Kimi K2.5 (ranked #1 with a score of 20.7). Open-weight models are highly competitive in this category — self-hosting is a viable alternative to proprietary APIs.
This ranking uses provisional weighted averages across the scoring benchmarks in math. 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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