Model Comparison

DeepSeek-V3.1 vs Gemma 3 12B

DeepSeek-V3.1 significantly outperforms across most benchmarks. Gemma 3 12B is 7.2x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V3.1 outperforms in 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, SimpleQA), while Gemma 3 12B is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Mon Apr 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Gemma 3 12B costs less

For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 5.4x more expensive than Gemma 3 12B ($0.05/1M tokens).

For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 10.0x more expensive than Gemma 3 12B ($0.10/1M tokens).

In conclusion, DeepSeek-V3.1 is more expensive than Gemma 3 12B.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Mon Apr 13 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.1
Input tokens$0.27
Output tokens$1.00
Best providerDeepinfra
Google
Gemma 3 12B
Input tokens$0.05
Output tokens$0.10
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

659.0B diff

DeepSeek-V3.1 has 659.0B more parameters than Gemma 3 12B, making it 5491.7% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Google
Gemma 3 12B
12.0Bparameters
671.0B
DeepSeek-V3.1
12.0B
Gemma 3 12B

Context Window

Maximum input and output token capacity

DeepSeek-V3.1 accepts 163,840 input tokens compared to Gemma 3 12B's 131,072 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Gemma 3 12B is limited to 131,072 tokens.

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Google
Gemma 3 12B
Input131,072 tokens
Output131,072 tokens
Mon Apr 13 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3 12B supports multimodal inputs, whereas DeepSeek-V3.1 does not.

Gemma 3 12B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.1

Text
Images
Audio
Video

Gemma 3 12B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while Gemma 3 12B uses Gemma.

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

DeepSeek-V3.1

MIT

Open weights

Gemma 3 12B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Gemma 3 12B was released on 2025-03-12.

Gemma 3 12B is 2 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

Gemma 3 12B

Mar 12, 2025

1.1 years ago

2mo 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

Provider Availability

DeepSeek-V3.1 is available from DeepInfra, Novita. Gemma 3 12B is available from DeepInfra.

DeepSeek-V3.1

deepinfra logo
Deepinfra
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M
novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

Gemma 3 12B

deepinfra logo
Deepinfra
Input Price:Input: $0.05/1MOutput Price:Output: $0.10/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Higher GPQA score (74.9% vs 40.9%)
Higher LiveCodeBench score (56.4% vs 24.6%)
Higher MMLU-Pro score (83.7% vs 60.6%)
Higher SimpleQA score (93.4% vs 6.3%)
Google
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Google
Gemma 3 12B

FAQ

Common questions about DeepSeek-V3.1 vs Gemma 3 12B

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Gemma 3 12B is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Gemma 3 12B scores GSM8k: 94.4%, IFEval: 88.9%, DocVQA: 87.1%, BIG-Bench Hard: 85.7%, HumanEval: 85.4%.
Gemma 3 12B is 5.4x cheaper for input tokens. DeepSeek-V3.1 costs $0.27/M input and $1.00/M output via deepinfra. Gemma 3 12B costs $0.05/M input and $0.10/M output via deepinfra.
DeepSeek-V3.1 supports 164K tokens and Gemma 3 12B supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (164K vs 131K), input pricing ($0.27 vs $0.05/M), multimodal support (no vs yes), licensing (MIT vs Gemma). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.1 is developed by DeepSeek and Gemma 3 12B is developed by Google.