Model Comparison

DeepSeek-V3 vs Qwen2.5-Coder 7B Instruct

DeepSeek-V3 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V3 outperforms in 4 benchmarks (LiveCodeBench, MMLU, MMLU-Pro, MMLU-Redux), while Qwen2.5-Coder 7B Instruct is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Tue May 05 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

664.0B diff

DeepSeek-V3 has 664.0B more parameters than Qwen2.5-Coder 7B Instruct, making it 9485.7% larger.

DeepSeek-V3

671.0Bparameters

Qwen2.5-Coder 7B Instruct

7.0Bparameters

671.0B

DeepSeek-V3

7.0B

Qwen2.5-Coder 7B Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek-V3

Input131,072 tokens

Output131,072 tokens

Qwen2.5-Coder 7B Instruct

Input- tokens

Output- tokens

Tue May 05 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Qwen2.5-Coder 7B Instruct uses Apache 2.0.

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

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Qwen2.5-Coder 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Qwen2.5-Coder 7B Instruct was released on 2024-09-19.

DeepSeek-V3 is 3 months newer than Qwen2.5-Coder 7B Instruct.

DeepSeek-V3

Dec 25, 2024

1.4 years ago

3mo newer

Qwen2.5-Coder 7B Instruct

Sep 19, 2024

1.6 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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek-V3

View details

DeepSeek

Larger context window (131,072 tokens)

Higher LiveCodeBench score (37.6% vs 18.2%)

Higher MMLU score (88.5% vs 67.6%)

Higher MMLU-Pro score (75.9% vs 40.1%)

Higher MMLU-Redux score (89.1% vs 66.6%)

Qwen2.5-Coder 7B Instruct

View details

Alibaba Cloud / Qwen Team

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

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek-V3	Qwen2.5-Coder 7B Instruct

FAQ

Common questions about DeepSeek-V3 vs Qwen2.5-Coder 7B Instruct.

Which is better, DeepSeek-V3 or Qwen2.5-Coder 7B Instruct?

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek and Qwen2.5-Coder 7B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3 compare to Qwen2.5-Coder 7B Instruct in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Qwen2.5-Coder 7B Instruct scores HumanEval: 88.4%, GSM8k: 83.9%, MBPP: 83.5%, HellaSwag: 76.8%, Winogrande: 72.9%.

What are the context window sizes for DeepSeek-V3 and Qwen2.5-Coder 7B Instruct?

DeepSeek-V3 supports 131K tokens and Qwen2.5-Coder 7B Instruct 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 and Qwen2.5-Coder 7B Instruct?

Key differences include licensing (MIT + Model License (Commercial use allowed) vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Qwen2.5-Coder 7B Instruct?

DeepSeek-V3 is developed by DeepSeek and Qwen2.5-Coder 7B Instruct is developed by Alibaba Cloud / Qwen Team.