模型对比
26B MOE 和 Step 3.7 Flash 都是 AI 领域的模型。以下是基于 traeai 收录的真实报道数据的全面对比。
模型
也叫：26B Mixture of Experts
Gemma 系列中的一种混合专家模型，作为性能基准对比对象。
2 篇相关报道
模型
也叫：step3.7flash
阶跃星辰发布的高效推理模型。
7 篇相关报道
2
26B MOE 相关
0
共同提及
7
Step 3.7 Flash 相关
Gemma-4 12B is an encoder-free, unified multimodal model that runs directly on laptops with 16GB VRAM. It matches the performance of the 26B MOE with less than half the memory footprint, ships with Hermes and agent tools, macOS Edge Gallery, and RTLM, and is released under Apache 2.0.
入选理由：Gemma-4 12B 无需分别的视觉/音频编码器，图像与音频直接映射到 LLM，减少延迟与内存开销。
Gemma 4 12B is a unified, encoder-free multimodal model bringing high-performance multimodal intelligence to your laptop. It matches the performance of our 26B MoE at less than half the memory footprint, supports native audio inputs, and runs locally on 16GB VRAM hardware with low-latency multi-step reasoning.
入选理由：Gemma 4 12B 性能接近 26B MoE，内存仅其一半，适合在 16GB VRAM 现代本机运行。
StepFun released Step 3.7 Flash — a high-efficiency agentic coding model supporting multimodal understanding, tool use, and long-running workflows; its standout feature is full free access in Hermes Agent, removing typical API/credit barriers for real-world testing.
入选理由：Step 3.7 Flash 是 StepFun 新一代 agentic coding 模型，含196B总参数 + 1.8B 视觉模块 + ~11B 激活参数，支持256K上下文窗口。
Step 3.7 Flash by Yujue Star is a new-generation Flash model for production-grade AI Agents, featuring native multimodal understanding, high throughput with low latency, and enhanced web search. It achieves 97% of Claude Opus 4.6's coding performance at only 1/9 the cost per task, ideal for high-frequency, complex real-world workflows.
入选理由：Step 3.7 Flash 采用稀疏 MoE 架构，激活参数仅 11B，最高生成速度达 400 Tokens/s，支持 40 个 Agent 并行运行。
Step 3.7 Flash is a 196B MoE model designed from the ground up for inference efficiency, using MFA and AFD techniques to reduce KV-cache usage to ~22% of DeepSeek, supporting agent, coding, and multimodal workflows, open-sourced under Apache 2.0 and available on Fireworks.
入选理由：Step 3.7 Flash 是 196B MoE 模型，从设计之初就聚焦推理效率，而非事后优化。
NVIDIA introduces Step 3.7 Flash for large-scale MoE models.
入选理由：Step 3.7 Flash 支持 198B 参数 MoE 模型，其中 11B 参数为活跃状态。
文章仅提供了一个链接，介绍如何在 NVIDIA GPU 上运行 Step 3.7 Flash，缺乏深度和技术细节。
入选理由：文章链接指向 NVIDIA 开发者博客，介绍 Step 3.7 Flash 的运行方法。
Step 3.7 Flash significantly reduces KV-cache cost via MFA + AFD technology, enabling efficient inference with one-click deployment.
入选理由：Step 3.7 Flash采用MFA + AFD技术，将KV-cache成本降至原模型的分数。
Step 3.7 Flash model achieves 400 tokens/sec processing speed, reducing Agent task cost to a fraction of Claude's.
入选理由：处理速度达400 tokens/秒
