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DeepSeek-V3.2-Exp vs MiMo-V2.6-Pro

MiMo-V2.6-Pro leads the LLM Stats Score 40.6 to 28.2.

DeepSeek · Xiaomi · Updated for 2026

Which is better?

MiMo-V2.6-Pro leads the overall LLM Stats Score 40.6 to 28.2, ranking #57 overall.

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

Choose DeepSeek-V3.2-Exp

  • you want predictable pricing at $0.27/M input and $0.41/M output

Choose MiMo-V2.6-Pro

  • overall performance matters — it scores 40.6 and ranks #57 on LLM Stats
  • your work emphasizes coding and agents — 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-V3.2-Exp
DeepSeek
MiMo-V2.6-Pro
Xiaomi
Core performance indexes
LLM Stats Score
28.2
#137
40.6
#57
Coding
17.5
#122
34.1
#35
Agents
5.9
#146
29.9
#38
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.27 / M
— / M
Output price
$0.41 / M
— / M
Context window
163,840
—

Individual benchmarks

14 reported for DeepSeek-V3.2-Exp · 3 for MiMo-V2.6-Pro

No common benchmarks found

DeepSeek-V3.2-Exp and MiMo-V2.6-Prodon'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

335.0B diff

MiMo-V2.6-Pro has 335.0B more parameters than DeepSeek-V3.2-Exp, making it 48.9% larger.

DeepSeek
DeepSeek-V3.2-Exp
685.0Bparameters
Xiaomi
MiMo-V2.6-Pro
1.0Tparameters
685.0B
DeepSeek-V3.2-Exp
1020.0B
MiMo-V2.6-Pro

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2-Exp specifies input context (163,840 tokens). Only DeepSeek-V3.2-Exp specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V3.2-Exp
Input163,840 tokens
Output65,536 tokens
Xiaomi
MiMo-V2.6-Pro
Input- tokens
Output- tokens
Mon Sep 21 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

MiMo-V2.6-Pro supports multimodal inputs, whereas DeepSeek-V3.2-Exp does not.

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

DeepSeek-V3.2-Exp

Text
Images
Audio
Video

MiMo-V2.6-Pro

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-V3.2-Exp

MIT

Open weights

MiMo-V2.6-Pro

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Exp was released on 2025-09-29, while MiMo-V2.6-Pro was released on 2026-09-22.

MiMo-V2.6-Pro is 12 months newer than DeepSeek-V3.2-Exp.

DeepSeek-V3.2-Exp

Sep 29, 2025

11 months ago

MiMo-V2.6-Pro

Sep 22, 2026

-1 days ago

11mo 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-V3.2-Exp and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.

DeepSeek-V3.2-Exp
✓ Preferred
MiMo-V2.6-Pro
Open in Playground

FAQ

Common questions about DeepSeek-V3.2-Exp vs MiMo-V2.6-Pro.

Which is better, DeepSeek-V3.2-Exp or MiMo-V2.6-Pro?

MiMo-V2.6-Pro leads the LLM Stats Score 40.6 to 28.2. DeepSeek-V3.2-Exp is made by DeepSeek and MiMo-V2.6-Pro 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-V3.2-Exp compare to MiMo-V2.6-Pro in benchmarks?

DeepSeek-V3.2-Exp scores SimpleQA: 97.1%, AIME 2025: 89.3%, MMLU-Pro: 85.0%, HMMT 2025: 83.6%, GPQA: 79.9%. MiMo-V2.6-Pro scores DeepSWE 1.1: 71.9%, MiMo Coding Bench: 63.2%, Program Bench: 26.5%.

What are the context window sizes for DeepSeek-V3.2-Exp and MiMo-V2.6-Pro?

DeepSeek-V3.2-Exp supports 164K tokens and MiMo-V2.6-Pro 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-V3.2-Exp and MiMo-V2.6-Pro?

Key differences include LLM Stats Score (28.2 vs 40.6), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2-Exp and MiMo-V2.6-Pro?

DeepSeek-V3.2-Exp is developed by DeepSeek and MiMo-V2.6-Pro is developed by Xiaomi.

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