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DeepSeek-V3 vs Gemma 2 9B

DeepSeek-V3 leads the LLM Stats Score 15.7 to -4.9.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V3 leads the overall LLM Stats Score 15.7 to -4.9, ranking #234 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V3 wins 1; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V3

  • overall performance matters — it scores 15.7 and ranks #234 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Dec 2024

Choose Gemma 2 9B

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V3
DeepSeek
Gemma 2 9B
Google
Core performance indexes
LLM Stats Score
15.7
#234
-4.9
#360
Reasoning
14.8
#233
-5.2
#353
Coding
6.2
#198
-11.0
#271
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.27 / M
— / M
Output price
$0.89 / M
— / M
Context window
131,072
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V3
Gemma 2 9B
Math
18.0#189
-3.8#315
Legal
19.9#67
-0.1#185
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V3 · 16 for Gemma 2 9B

1 shared

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

DeepSeek-V3 significantly outperforms across most benchmarks.

Sun Sep 27 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

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
Sun Sep 27 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

1.8 years ago

6mo newer
Gemma 2 9B

Jun 27, 2024

2.2 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→

Judge for yourself.

Run your own prompts against DeepSeek-V3 and Gemma 2 9B side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Gemma 2 9B
Open in Playground

FAQ

Common questions about DeepSeek-V3 vs Gemma 2 9B.

Which is better, DeepSeek-V3 or Gemma 2 9B?

DeepSeek-V3 leads the LLM Stats Score 15.7 to -4.9. DeepSeek-V3 is made by DeepSeek and Gemma 2 9B is made by Google. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3 compare to Gemma 2 9B in benchmarks?

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%.

What are the context window sizes for DeepSeek-V3 and Gemma 2 9B?

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.

What are the main differences between DeepSeek-V3 and Gemma 2 9B?

Key differences include LLM Stats Score (15.7 vs -4.9), licensing (MIT + Model License (Commercial use allowed) vs Gemma). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Gemma 2 9B?

DeepSeek-V3 is developed by DeepSeek and Gemma 2 9B is developed by Google.

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