Best Value LLM for Reasoning in 2026 — Cost-Adjusted Rankings

Reasoning models tend to be the most expensive tier — they use chain-of-thought, produce more output tokens, and are priced accordingly. This ranking divides each model's weighted reasoning score by output token price, revealing which models deliver the best abstract reasoning, long-context comprehension, and multi-step logic per dollar. For applications that need strong reasoning without frontier-model budgets, the value leaders here are worth serious consideration.

Unless noted otherwise, ranking surfaces on this page use BenchLM’s provisional leaderboard lane rather than the stricter sourced-only verified leaderboard.

GPT-6 Luna leads this ranking with a score of 111.8, followed by MiniMax M3 (66.17) and GPT-5.6 Luna (45.42). There is a significant gap between the leading models and the rest of the field.

The best open-weight option is MiniMax M3 (ranked #2 with a score of 66.17). 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 reasoning. For detailed model profiles, click any model name above. To compare two specific models head-to-head, use the "vs #" links.