智商指数
88.4 #27
AAQI 88.0
输出速度
118 t/s
ITL 8.5 ms/tok
混合价格 (3:1)
$0.18 /1M
输入 $0.1/M · 输出 $0.3/M
上下文
1M
≈ 2,500 页 A4 · 最大输出 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
MiMo v2.6 Flash
Claude Mythos Preview (#1)
Average (68)
缺少直接基准测量时,雷达图各轴由类别得分近似得出 — 详见 评分方法。
优势与取舍
优势所在
- Fastest open-weights flagship — 118 tok/s
- SWE-bench-tier coding at flash pricing ($0.10/$0.30)
- 1M context self-hostable for regulated stacks
- Strong thinking/non-thinking dual-mode scores
需要注意的权衡
- Nuance and long-tail reasoning below frontier closed models
- 310B weights still need serious VRAM even at 15B active
- Smaller ecosystem than DeepSeek/Qwen
杀手级用例
High-throughput coding agents
Flash-speed agent loops at open-model prices.
On-prem copilots
Self-hosted coding assistants for enterprises.
Batch document processing
1M-context extraction at $0.10/M input.
开发者快速上手与 API
请先设置
MIMO_API_KEY — 获取 API 密钥curl https://api.mimo.mi.com/v1/chat/completions \
-H "Authorization: Bearer $MIMO_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "mimo-v2.6-flash",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.mimo.mi.com/v1",
api_key=os.environ["MIMO_API_KEY"],
)
resp = client.chat.completions.create(
model="mimo-v2.6-flash",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://api.mimo.mi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.MIMO_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "mimo-v2.6-flash",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);官方资源与链接
API 部署
对比托管商与延迟
Groq、Cerebras、Together、DeepInfra、Fireworks、Bedrock 与 Azure 的吞吐量、TTFT 和缓存定价。
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
各榜单得分
分类能力得分
基准测试分项
实时规格与架构
底层解析
310B hybrid MoE, ~15B active per token
- Hybrid active-parameter MoE — 310B total, ~15B live per token
- 1M context at 100+ tok/s sustained
- Open weights under a permissive license
- Pretraining-to-posttraining pipeline detailed in the MiMo paper
发布日期
2026年10月2日
架构
310B hybrid MoE, ~15B active per token
参数量
310B/15B MoE
开源协议
Open Weights
上下文窗口
1,000,000 tokens
最大输出
32,768 tokens
模态
text + image
缓存价格
读 $0.02 · 写 $0.1 /1M
推理
—
输入价格
$0.1/M
输出价格
$0.3/M
输出速度
118 t/s
首Token延迟
—
数据更新
2026年10月4日
每月账单预估
每月预估支出
$0.00
正在计算相对 GPT-4o 的节省额…
引用与嵌入
GitHub Markdown 徽章
Loading badge markdown...BibTeX
@misc{llmpodium2026mimov26flash,
title = {Benchmark Analysis and Intelligence Evaluation of MiMo v2.6 Flash},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/mimo-v2-6-flash}
}嵌入评分徽章
将实时 Podium 评分徽章添加到 GitHub 或 Hugging Face README —— 链接回实时排名。
预览
Markdown
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替代模型推荐
基于能力、定价和基准测试得分与 MiMo v2.6 Flash 最接近的替代模型。