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DeepSeek-R1 vs Qwen3.8-27B

Comparing DeepSeek-R1 and Qwen3.8-27B across benchmarks, pricing, and capabilities.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-R1 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 DeepSeek-R1

  • you want predictable pricing at $0.55/M input and $2.19/M output

Choose Qwen3.8-27B

  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Benchmark wins
Input price
$0.55 / M
— / M
Output price
$2.19 / M
— / M
Context window
131,072
Released
Jan 2025
Aug 2026
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1 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

Model Size

Parameter count comparison

643.2B diff

DeepSeek-R1 has 643.2B more parameters than Qwen3.8-27B, making it 2315.3% larger.

DeepSeek
DeepSeek-R1
671.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-27B
27.8Bparameters
671.0B
DeepSeek-R1
27.8B
Qwen3.8-27B

Context Window

Maximum input and output token capacity

Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3.8-27B
Input- tokens
Output- tokens
Mon Aug 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3.8-27B supports multimodal inputs, whereas DeepSeek-R1 does not.

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

DeepSeek-R1

Text
Images
Audio
Video

Qwen3.8-27B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1 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-R1

MIT

Open weights

Qwen3.8-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-R1 was released on 2025-01-20, while Qwen3.8-27B was released on 2026-08-14.

Qwen3.8-27B is 19 months newer than DeepSeek-R1.

DeepSeek-R1

Jan 20, 2025

1.6 years ago

Qwen3.8-27B

Aug 14, 2026

1 weeks ago

1.6yr 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-R1 and Qwen3.8-27B side-by-side, then vote on the output you prefer.

DeepSeek-R1
✓ Preferred
Qwen3.8-27B
Open in Playground

FAQ

Common questions about DeepSeek-R1 vs Qwen3.8-27B.

Which is better, DeepSeek-R1 or Qwen3.8-27B?

DeepSeek-R1 (DeepSeek) 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 DeepSeek-R1 compare to Qwen3.8-27B in benchmarks?

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-R1 and Qwen3.8-27B?

DeepSeek-R1 supports 131K 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-R1 and Qwen3.8-27B?

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

Who makes DeepSeek-R1 and Qwen3.8-27B?

DeepSeek-R1 is developed by DeepSeek and Qwen3.8-27B is developed by Alibaba Cloud / Qwen Team.