Model Comparison

DeepSeek-V3 vs Gemma 2 9B

DeepSeek-V3 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek-V3 outperforms in 1 benchmarks (MMLU), while Gemma 2 9B is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Tue Apr 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Apr 21 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Google
Gemma 2 9B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

661.8B diff

DeepSeek-V3 has 661.8B more parameters than Gemma 2 9B, making it 7161.9% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Google
Gemma 2 9B
9.2Bparameters
671.0B
DeepSeek-V3
9.2B
Gemma 2 9B

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
Google
Gemma 2 9B
Input- tokens
Output- tokens
Tue Apr 21 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Gemma 2 9B uses Gemma.

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

Gemma 2 9B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Gemma 2 9B was released on 2024-06-27.

DeepSeek-V3 is 6 months newer than Gemma 2 9B.

DeepSeek-V3

Dec 25, 2024

1.3 years ago

6mo newer
Gemma 2 9B

Jun 27, 2024

1.8 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
Larger context window (131,072 tokens)
Higher MMLU score (88.5% vs 71.3%)
Google

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
Google
Gemma 2 9B

FAQ

Common questions about DeepSeek-V3 vs Gemma 2 9B

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek and Gemma 2 9B is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Gemma 2 9B scores ARC-E: 88.0%, BoolQ: 84.2%, HellaSwag: 81.9%, PIQA: 81.7%, Winogrande: 80.6%.
DeepSeek-V3 supports 131K tokens and Gemma 2 9B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (MIT + Model License (Commercial use allowed) vs Gemma). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3 is developed by DeepSeek and Gemma 2 9B is developed by Google.