Model Comparison

DeepSeek-V3 vs DeepSeek R1 Distill Qwen 1.5B

DeepSeek-V3 shows notably better performance in the majority of benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V3 outperforms in 3 benchmarks (GPQA, LiveCodeBench, MATH-500), while DeepSeek R1 Distill Qwen 1.5B is better at 1 benchmark (AIME 2024).

DeepSeek-V3 shows notably better performance in the majority of benchmarks.

Fri May 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

669.2B diff

DeepSeek-V3 has 669.2B more parameters than DeepSeek R1 Distill Qwen 1.5B, making it 37596.6% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
DeepSeek
DeepSeek R1 Distill Qwen 1.5B
1.8Bparameters
671.0B
DeepSeek-V3
1.8B
DeepSeek R1 Distill Qwen 1.5B

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
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
DeepSeek
DeepSeek R1 Distill Qwen 1.5B
Input- tokens
Output- tokens
Fri May 15 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while DeepSeek R1 Distill Qwen 1.5B uses MIT.

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

DeepSeek R1 Distill Qwen 1.5B

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while DeepSeek R1 Distill Qwen 1.5B was released on 2025-01-20.

DeepSeek R1 Distill Qwen 1.5B is 1 month newer than DeepSeek-V3.

DeepSeek-V3

Dec 25, 2024

1.4 years ago

DeepSeek R1 Distill Qwen 1.5B

Jan 20, 2025

1.3 years ago

3w 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

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher GPQA score (59.1% vs 33.8%)
Higher LiveCodeBench score (37.6% vs 16.9%)
Higher MATH-500 score (90.2% vs 83.9%)
DeepSeek
Higher AIME 2024 score (52.7% vs 39.2%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
DeepSeek
DeepSeek R1 Distill Qwen 1.5B

FAQ

Common questions about DeepSeek-V3 vs DeepSeek R1 Distill Qwen 1.5B.

Which is better, DeepSeek-V3 or DeepSeek R1 Distill Qwen 1.5B?

DeepSeek-V3 shows notably better performance in the majority of benchmarks. DeepSeek-V3 is made by DeepSeek and DeepSeek R1 Distill Qwen 1.5B is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3 compare to DeepSeek R1 Distill Qwen 1.5B in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. DeepSeek R1 Distill Qwen 1.5B scores MATH-500: 83.9%, AIME 2024: 52.7%, GPQA: 33.8%, LiveCodeBench: 16.9%.

What are the context window sizes for DeepSeek-V3 and DeepSeek R1 Distill Qwen 1.5B?

DeepSeek-V3 supports 131K tokens and DeepSeek R1 Distill Qwen 1.5B 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 DeepSeek R1 Distill Qwen 1.5B?

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