The AI arena is free today

Open Superagent

DeepSeek-V3.1 vs MiMo-V2.6-Flash

MiMo-V2.6-Flash leads the LLM Stats Score 45.7 to 22.1.

DeepSeek · Xiaomi · Updated for 2026

Which is better?

MiMo-V2.6-Flash leads the overall LLM Stats Score 45.7 to 22.1, ranking #29 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V3.1

  • you want predictable pricing at $0.25/M input and $0.95/M output

Choose MiMo-V2.6-Flash

  • overall performance matters — it scores 45.7 and ranks #29 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

DeepSeek-V3.1
DeepSeek
MiMo-V2.6-Flash
Xiaomi
Core performance indexes
LLM Stats Score
22.1
#182
45.7
#29
Reasoning
22.3
#173
43.2
#42
Coding
12.5
#147
36.6
#22
Agents
0.3
#175
33.3
#25
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.25 / M
— / M
Output price
$0.95 / M
— / M
Context window
163,840
—

Individual benchmarks

16 reported for DeepSeek-V3.1 · 16 for MiMo-V2.6-Flash

No common benchmarks found

DeepSeek-V3.1 and MiMo-V2.6-Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

362.0B diff

DeepSeek-V3.1 has 362.0B more parameters than MiMo-V2.6-Flash, making it 117.2% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Xiaomi
MiMo-V2.6-Flash
309.0Bparameters
671.0B
DeepSeek-V3.1
309.0B
MiMo-V2.6-Flash

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Xiaomi
MiMo-V2.6-Flash
Input- tokens
Output- tokens
Tue Sep 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

MiMo-V2.6-Flash supports multimodal inputs, whereas DeepSeek-V3.1 does not.

MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.1

Text
Images
Audio
Video

MiMo-V2.6-Flash

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-V3.1

MIT

Open weights

MiMo-V2.6-Flash

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while MiMo-V2.6-Flash was released on 2026-09-22.

MiMo-V2.6-Flash is 21 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.7 years ago

MiMo-V2.6-Flash

Sep 22, 2026

-1 days ago

1.7yr newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V3.1 and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.

DeepSeek-V3.1
✓ Preferred
MiMo-V2.6-Flash
Open in Playground

FAQ

Common questions about DeepSeek-V3.1 vs MiMo-V2.6-Flash.

Which is better, DeepSeek-V3.1 or MiMo-V2.6-Flash?

MiMo-V2.6-Flash leads the LLM Stats Score 45.7 to 22.1. DeepSeek-V3.1 is made by DeepSeek and MiMo-V2.6-Flash is made by Xiaomi. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3.1 compare to MiMo-V2.6-Flash in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. MiMo-V2.6-Flash scores CyberGym: 95.1%, Terminal-Bench 2.1: 87.6%, OSWorld-Verified: 80.8%, MiMo Cyber Bench: 77.2%, Toolathlon-Verified: 73.6%.

What are the context window sizes for DeepSeek-V3.1 and MiMo-V2.6-Flash?

DeepSeek-V3.1 supports 164K tokens and MiMo-V2.6-Flash supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.1 and MiMo-V2.6-Flash?

Key differences include LLM Stats Score (22.1 vs 45.7), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.1 and MiMo-V2.6-Flash?

DeepSeek-V3.1 is developed by DeepSeek and MiMo-V2.6-Flash is developed by Xiaomi.

Related comparisons