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DeepSeek-V3.2 vs Qwen3.8-27B

Qwen3.8-27B leads the LLM Stats Score 46.1 to 33.5.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3.8-27B leads the overall LLM Stats Score 46.1 to 33.5, ranking #24 overall.

The models split the 2 individual benchmarks reported for both models evenly.

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

Choose DeepSeek-V3.2

  • you want predictable pricing at $0.26/M input and $0.38/M output

Choose Qwen3.8-27B

  • overall performance matters — it scores 46.1 and ranks #24 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Core performance indexes
33.5
#86
46.1
#24
33.6
#83
45.6
#24
23.3
#67
34.5
#29
12.7
#90
32.9
#24
Cost, coverage & limits
Benchmark wins
1 of 2
1 of 2
Input price
$0.26 / M
— / M
Output price
$0.38 / M
— / M
Context window
163,840

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V3.2
Qwen3.8-27B
31.7#57
31.5#59
10.6#114
25.0#30
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

17 reported for DeepSeek-V3.2 · 26 for Qwen3.8-27B

2 shared

DeepSeek-V3.2 outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3.8-27B is better at 1 benchmark (GPQA).

Both models are evenly matched across the benchmarks.

Sun Aug 30 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

657.2B diff

DeepSeek-V3.2 has 657.2B more parameters than Qwen3.8-27B, making it 2365.7% larger.

DeepSeek
DeepSeek-V3.2
685.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-27B
27.8Bparameters
685.0B
DeepSeek-V3.2
27.8B
Qwen3.8-27B

Context Window

Maximum input and output token capacity

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

DeepSeek
DeepSeek-V3.2
Input163,840 tokens
Output163,840 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-27B
Input- tokens
Output- tokens
Sun Aug 30 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3.8-27B supports multimodal inputs, whereas DeepSeek-V3.2 does not.

Qwen3.8-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2

Text
Images
Audio
Video

Qwen3.8-27B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 is licensed under MIT, while Qwen3.8-27B uses Apache 2.0.

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

DeepSeek-V3.2

MIT

Open weights

Qwen3.8-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 was released on 2025-12-01, while Qwen3.8-27B was released on 2026-08-14.

Qwen3.8-27B is 9 months newer than DeepSeek-V3.2.

DeepSeek-V3.2

Dec 1, 2025

9 months ago

Qwen3.8-27B

Aug 14, 2026

2 weeks ago

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

DeepSeek-V3.2
✓ Preferred
Qwen3.8-27B
Open in Playground

FAQ

Common questions about DeepSeek-V3.2 vs Qwen3.8-27B.

Which is better, DeepSeek-V3.2 or Qwen3.8-27B?

Qwen3.8-27B leads the LLM Stats Score 46.1 to 33.5. DeepSeek-V3.2 is made by DeepSeek and Qwen3.8-27B is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3.2 compare to Qwen3.8-27B in benchmarks?

DeepSeek-V3.2 scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. 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 DeepSeek-V3.2 and Qwen3.8-27B?

DeepSeek-V3.2 supports 164K 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 DeepSeek-V3.2 and Qwen3.8-27B?

Key differences include LLM Stats Score (33.5 vs 46.1), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 and Qwen3.8-27B?

DeepSeek-V3.2 is developed by DeepSeek and Qwen3.8-27B is developed by Alibaba Cloud / Qwen Team.