# MiniMax-M2.7模型在六大推理服务商上线，速度与价格差异显著

- 来源：Artificial Analysis (@ArtificialAnlys)
- 发布时间：2026-05-06 02:46
- AIHOT 分数：58
- AIHOT 链接：https://aihot.virxact.com/items/cmoszqxp3015bslv7okhhqzt4
- 原文链接：https://x.com/ArtificialAnlys/status/2051735255044997215

## AI 摘要

MiniMax-M2.7模型已在六家推理服务商上线，各提供商在速度和价格上差异明显。SambaNovaAI以每秒435个输出令牌的速度领先，比其他提供商快3倍以上，但其价格也高出约2倍。FireworksAI、Novita Labs等四家则与MiniMax官方API定价持平。分析指出，Fireworks和SambaNova在速度与价格的权衡中处于帕累托前沿：前者性价比高，后者则以高价换取极致速度。此外，各家的高速缓存折扣政策不同，这对缓存密集型工作负载的成本影响显著。因此，最优选择高度依赖于具体工作负载对延迟和成本的敏感度。

## 正文

MiniMax-M2.7 is now available across six inference providers on Artificial Analysis， with significant differentiation in speed and price

@SambaNovaAI leads on speed at 435 output tokens/s， >3x faster than any other provider. @FireworksAI_HQ， @novita_labs， @togethercompute， and @GMI_cloud have all matched @MiniMax_AI's first-party API pricing， while SambaNova is 2x higher.

Key takeaways：

➤ Fireworks and SambaNova are on the Pareto frontier for Speed vs. Price. At 127 output tokens/s and ~$0.22 per 1M tokens blended， Fireworks is ~2.2x faster than MiniMax's first-party API at the same blended price， whereas SambaNova delivers 435 output tokens/s but at ~2-3.5x the blended price of the other providers （depending on cache usage）

➤ SambaNova is the fastest provider at 435 output tokens/s， ~3.4x the next fastest provider （Fireworks at 127 output tokens/s）. The remaining providers run substantially slower： MiniMax's first-party API at 57 output tokens/s， Novita at 54， GMI at 41， and Together AI at 29

➤ Cache discounts vary across providers. Fireworks， MiniMax， Novita， and Together AI offer 80% cache hit discounts， while GMI and SambaNova do not offer a discount. For cache-heavy workloads， this can materially increase the relative pricing for GMI and SambaNova

➤ Optimal provider choice depends on workload. SambaNova may be more suited to latency-sensitive deployments， albeit at a higher cost， while Fireworks may be more suitable for high-volume workloads that are not as latency-sensitive
