Model Comparison

DeepSeek-V3.2 (Thinking) vs Gemma 3 27B

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. Gemma 3 27B is 2.5x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Gemma 3 27B is better at 0 benchmarks.

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Thu May 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Gemma 3 27B costs less

For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 2.8x more expensive than Gemma 3 27B ($0.10/1M tokens).

For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 2.1x more expensive than Gemma 3 27B ($0.20/1M tokens).

In conclusion, DeepSeek-V3.2 (Thinking) is more expensive than Gemma 3 27B.*

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

Lowest available price from all providers
Thu May 21 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Google
Gemma 3 27B
Input tokens$0.10
Output tokens$0.20
Best providerDeepinfra
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Model Size

Parameter count comparison

658.0B diff

DeepSeek-V3.2 (Thinking) has 658.0B more parameters than Gemma 3 27B, making it 2437.0% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Google
Gemma 3 27B
27.0Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
27.0B
Gemma 3 27B

Context Window

Maximum input and output token capacity

Both models have the same input context window of 131,072 tokens. Gemma 3 27B can generate longer responses up to 131,072 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Google
Gemma 3 27B
Input131,072 tokens
Output131,072 tokens
Thu May 21 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3 27B supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.

Gemma 3 27B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2 (Thinking)

Text
Images
Audio
Video

Gemma 3 27B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Gemma 3 27B uses Gemma.

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

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Gemma 3 27B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Gemma 3 27B was released on 2025-03-12.

DeepSeek-V3.2 (Thinking) is 9 months newer than Gemma 3 27B.

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

5 months ago

8mo newer
Gemma 3 27B

Mar 12, 2025

1.2 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

Provider Availability

DeepSeek-V3.2 (Thinking) is available from DeepSeek. Gemma 3 27B is available from DeepInfra, Novita.

DeepSeek-V3.2 (Thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

Gemma 3 27B

deepinfra logo
Deepinfra
Input Price:Input: $0.10/1MOutput Price:Output: $0.20/1M
novita logo
Novita
Input Price:Input: $0.11/1MOutput Price:Output: $0.20/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Higher GPQA score (82.4% vs 42.4%)
Higher LiveCodeBench score (83.3% vs 29.7%)
Higher MMLU-Pro score (85.0% vs 67.5%)
Google
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Thinking)
Google
Gemma 3 27B

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Gemma 3 27B.

Which is better, DeepSeek-V3.2 (Thinking) or Gemma 3 27B?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Gemma 3 27B is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.2 (Thinking) compare to Gemma 3 27B in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. Gemma 3 27B scores GSM8k: 95.9%, IFEval: 90.4%, MATH: 89.0%, HumanEval: 87.8%, BIG-Bench Hard: 87.6%.

Is DeepSeek-V3.2 (Thinking) cheaper than Gemma 3 27B?

Gemma 3 27B is 2.8x cheaper for input tokens. DeepSeek-V3.2 (Thinking) costs $0.28/M input and $0.42/M output via deepseek. Gemma 3 27B costs $0.10/M input and $0.20/M output via deepinfra.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Gemma 3 27B?

DeepSeek-V3.2 (Thinking) supports 131K tokens and Gemma 3 27B supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V3.2 (Thinking) and Gemma 3 27B?

Key differences include input pricing ($0.28 vs $0.10/M), multimodal support (no vs yes), licensing (MIT vs Gemma). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Gemma 3 27B?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Gemma 3 27B is developed by Google.