Model Comparison

DeepSeek-V3 vs Gemma 3 1B

DeepSeek-V3 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

DeepSeek-V3 outperforms in 5 benchmarks (GPQA, IFEval, LiveCodeBench, MMLU-Pro, SimpleQA), while Gemma 3 1B is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Sun Apr 19 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
Sun Apr 19 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Google
Gemma 3 1B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

670.0B diff

DeepSeek-V3 has 670.0B more parameters than Gemma 3 1B, making it 67000.0% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Google
Gemma 3 1B
1.0Bparameters
671.0B
DeepSeek-V3
1.0B
Gemma 3 1B

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 3 1B
Input- tokens
Output- tokens
Sun Apr 19 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Gemma 3 1B 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 3 1B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Gemma 3 1B was released on 2025-03-12.

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

DeepSeek-V3

Dec 25, 2024

1.3 years ago

Gemma 3 1B

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

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher GPQA score (59.1% vs 19.2%)
Higher IFEval score (86.1% vs 80.2%)
Higher LiveCodeBench score (37.6% vs 1.9%)
Higher MMLU-Pro score (75.9% vs 14.7%)
Higher SimpleQA score (24.9% vs 2.2%)
Google

Detailed Comparison

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

FAQ

Common questions about DeepSeek-V3 vs Gemma 3 1B

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek and Gemma 3 1B 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 3 1B scores IFEval: 80.2%, GSM8k: 62.8%, Natural2Code: 56.0%, MATH: 48.0%, HumanEval: 41.5%.
DeepSeek-V3 supports 131K tokens and Gemma 3 1B 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 3 1B is developed by Google.