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MiMo-V2-Omni vs Qwen3.8-27B

Comparing MiMo-V2-Omni and Qwen3.8-27B across benchmarks, pricing, and capabilities.

Xiaomi · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

MiMo-V2-Omni and Qwen3.8-27B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose MiMo-V2-Omni

  • you want predictable pricing at $0.40/M input and $2.00/M output

Choose Qwen3.8-27B

  • you want the most recent training data — it shipped Aug 2026
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

MiMo-V2-Omni
Xiaomi
Qwen3.8-27B
Alibaba Cloud / Qwen Team
Benchmark wins
—
—
Input price
$0.40 / M
— / M
Output price
$2.00 / M
— / M
Context window
262,000
—
Released
Mar 2026
Aug 2026
License
Proprietary
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

MiMo-V2-Omni and Qwen3.8-27Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Context Window

Maximum input and output token capacity

Only MiMo-V2-Omni specifies input context (262,000 tokens). Only MiMo-V2-Omni specifies output context (16,384 tokens).

Xiaomi
MiMo-V2-Omni
Input262,000 tokens
Output16,384 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-27B
Input- tokens
Output- tokens
Tue Aug 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both MiMo-V2-Omni and Qwen3.8-27B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

MiMo-V2-Omni

Text
Images
Audio
Video

Qwen3.8-27B

Text
Images
Audio
Video

License

Usage and distribution terms

MiMo-V2-Omni is licensed under a proprietary license, while Qwen3.8-27B uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

MiMo-V2-Omni

Proprietary

Closed source

Qwen3.8-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

MiMo-V2-Omni was released on 2026-03-18, while Qwen3.8-27B was released on 2026-08-14.

Qwen3.8-27B is 5 months newer than MiMo-V2-Omni.

MiMo-V2-Omni

Mar 18, 2026

5 months ago

Qwen3.8-27B

Aug 14, 2026

1 weeks ago

4mo 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 MiMo-V2-Omni and Qwen3.8-27B side-by-side, then vote on the output you prefer.

MiMo-V2-Omni
✓ Preferred
Qwen3.8-27B
Open in Playground

FAQ

Common questions about MiMo-V2-Omni vs Qwen3.8-27B.

Which is better, MiMo-V2-Omni or Qwen3.8-27B?

MiMo-V2-Omni (Xiaomi) and Qwen3.8-27B (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does MiMo-V2-Omni compare to Qwen3.8-27B in benchmarks?

MiMo-V2-Omni scores PinchBench: 81.2%, SWE-Bench Verified: 74.8%, Claw-Eval: 54.8%, MM-BrowserComp: 52.0%, OmniGAIA: 49.8%. Qwen3.8-27B scores MathVision: 94.6%, OmniDocBench 1.5: 91.1%, LiveCodeBench v6: 90.3%, CharXiv-R: 90.2%, GPQA: 89.2%.

What are the context window sizes for MiMo-V2-Omni and Qwen3.8-27B?

MiMo-V2-Omni supports 262K tokens and Qwen3.8-27B 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 MiMo-V2-Omni and Qwen3.8-27B?

Key differences include licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes MiMo-V2-Omni and Qwen3.8-27B?

MiMo-V2-Omni is developed by Xiaomi and Qwen3.8-27B is developed by Alibaba Cloud / Qwen Team.

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