Last updated: 2026. Author: HolySheep AI Engineering Team. Benchmark data captured on H100 80GB SXM5 with vLLM 0.6.3 and SGLang 0.3.2 serving backends; full methodology in the load-ramp script below.
I spent the last two weeks running head-to-head throughput and cost benchmarks against MiniMax M2.7 and DeepSeek V4 through the HolySheep AI gateway. The TL;DR is that M2.7 still wins on raw tokens-per-dollar for chat workloads, while V4 leads on long-context reasoning and code-heavy tasks. The rest of this post shows you exactly how I measured it, what the numbers look like under realistic concurrency, and how to tune either model for your production traffic.
Architecture at a Glance
Both endpoints are Mixture-of-Experts transformers, but the design points diverge sharply:
- MiniMax M2.7 — 16B active parameters out of 64B total, 8 experts per layer with top-2 routing, 32k native context extended to 128k via YaRN. Designed for low-lat