Xiaomi
MiMo v2.6 FlashOpen
Xiaomi open-weights hybrid active parameter MoE running at 100+ tok/s with 1M context and frontier terminal agent benchmarks.
Intelligence
88.4 #27
AAQI 88.0
Output Speed
118 t/s
ITL 8.5 ms/tok
Blended price (3:1)
$0.18 /1M
In $0.1/M · Out $0.3/M
Context
1M
≈ 2,500 A4 pages · max out 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
MiMo v2.6 Flash
Claude Mythos Preview (#1)
Average (68)
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- 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
Trade-offs to watch
- Nuance and long-tail reasoning below frontier closed models
- 310B weights still need serious VRAM even at 15B active
- Smaller ecosystem than DeepSeek/Qwen
Killer Use Cases
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.
Developer Quickstart & API
Set
MIMO_API_KEY first — get an API keycurl 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);Official Resources & Links
API deployment
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Scores by Source
Category Scores
Benchmark Results
Head-to-head Live Matchups
Live Specs & Architecture
Under the hood
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
Release date
Oct 2, 2026
Architecture
310B hybrid MoE, ~15B active per token
Parameters
310B/15B MoE
License
Open Weights
Context Window
1,000,000 tokens
Max Output
32,768 tokens
Modalities
text + image
Cache Pricing
read $0.02 · write $0.1 /1M
Reasoning
—
Input Price
$0.1/M
Output Price
$0.3/M
Output Speed
118 t/s
Time to First Token
—
Data Updated
Oct 4, 2026
Estimated monthly bill
Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...
Cite & embed
GitHub Markdown badge
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}
}Embed Score Badge
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