Qwen2.5-72B Inference Efficiency vs. State-of-the-Art Models on MATH-PT

Abstract

Abstract: This report synthesises findings from 3 peer-reviewed papers addressing the following research question: How does the inference efficiency (e.g., tokens per second) of Qwen2.5-72B compare to other state-of-the-art models (e.g., Mistral-7B, Llama3-8B) when processing MATH-PT problems. We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts. The core lies in lightning attention and its efficient scaling. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference efficiency (e.g., tokens per second) of Qwen2.5-72B compare to other state-of-the-art models (e.g., Mistral-7B, Llama3-8B) when processing MATH-PT problems?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance