Model Comparison

Gemma 2 9B vs DeepSeek-V3

DeepSeek-V3 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

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

DeepSeek-V3 significantly outperforms across most benchmarks.

Tue Mar 31 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 Mar 31 2026 • llm-stats.com
Google
Gemma 2 9B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
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.

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

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).

Google
Gemma 2 9B
Input- tokens
Output- tokens
DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Tue Mar 31 2026 • llm-stats.com

License

Usage and distribution terms

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

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

Gemma 2 9B

Gemma

Open weights

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

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

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

Gemma 2 9B

Jun 27, 2024

1.8 years ago

DeepSeek-V3

Dec 25, 2024

1.3 years ago

6mo 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→

Key Takeaways

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

Detailed Comparison

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

FAQ

Common questions about Gemma 2 9B vs DeepSeek-V3

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