Model Comparison

DeepSeek-V3.2 (Thinking) vs Qwen3-Coder

Comparing DeepSeek-V3.2 (Thinking) and Qwen3-Coder across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Thinking) and Qwen3-Coder don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen3-Coder costs less

For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 1.6x more expensive than Qwen3-Coder ($0.18/1M tokens).

For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 2.3x more expensive than Qwen3-Coder ($0.18/1M tokens).

In conclusion, DeepSeek-V3.2 (Thinking) is more expensive than Qwen3-Coder.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Mon May 18 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Alibaba Cloud / Qwen Team
Qwen3-Coder
Input tokens$0.18
Output tokens$0.18
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

205.0B diff

DeepSeek-V3.2 (Thinking) has 205.0B more parameters than Qwen3-Coder, making it 42.7% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3-Coder
480.0Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
480.0B
Qwen3-Coder

Context Window

Maximum input and output token capacity

Qwen3-Coder accepts 256,000 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. Qwen3-Coder can generate longer responses up to 256,000 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Alibaba Cloud / Qwen Team
Qwen3-Coder
Input256,000 tokens
Output256,000 tokens
Mon May 18 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Qwen3-Coder uses Apache 2.0.

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

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Qwen3-Coder

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Qwen3-Coder was released on 2025-01-01.

DeepSeek-V3.2 (Thinking) is 11 months newer than Qwen3-Coder.

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

5 months ago

11mo newer
Qwen3-Coder

Jan 1, 2025

1.4 years ago

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

Provider Availability

DeepSeek-V3.2 (Thinking) is available from DeepSeek. Qwen3-Coder is available from DeepInfra, Fireworks.

DeepSeek-V3.2 (Thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

Qwen3-Coder

deepinfra logo
Deepinfra
Input Price:Input: $0.18/1MOutput Price:Output: $0.18/1M
fireworks logo
Fireworks
Input Price:Input: $0.25/1MOutput Price:Output: $0.25/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

No standout differentiators in the data we have for this pair.

Alibaba Cloud / Qwen Team

Qwen3-Coder

View details

Alibaba Cloud / Qwen Team

Larger context window (256,000 tokens)
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Thinking)
Alibaba Cloud / Qwen Team
Qwen3-Coder

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Qwen3-Coder.

Which is better, DeepSeek-V3.2 (Thinking) or Qwen3-Coder?

DeepSeek-V3.2 (Thinking) (DeepSeek) and Qwen3-Coder (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-V3.2 (Thinking) compare to Qwen3-Coder in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%.

Is DeepSeek-V3.2 (Thinking) cheaper than Qwen3-Coder?

Qwen3-Coder is 1.6x cheaper for input tokens. DeepSeek-V3.2 (Thinking) costs $0.28/M input and $0.42/M output via deepseek. Qwen3-Coder costs $0.18/M input and $0.18/M output via deepinfra.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Qwen3-Coder?

DeepSeek-V3.2 (Thinking) supports 131K tokens and Qwen3-Coder supports 256K 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 (Thinking) and Qwen3-Coder?

Key differences include context window (131K vs 256K), input pricing ($0.28 vs $0.18/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Qwen3-Coder?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Qwen3-Coder is developed by Alibaba Cloud / Qwen Team.