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DeepSeek-V3.2 (Thinking) vs Gemma 3n E4B Instructed

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is 79.4x cheaper per token.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V3.2 (Thinking) outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench, MMLU-Pro), while Gemma 3n E4B Instructed is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

On price, DeepSeek-V3.2 (Thinking) is roughly 79.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V3.2 (Thinking) also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V3.2 (Thinking)

  • you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
  • cost matters — it's about 79.4x cheaper per token
  • you process long inputs — it offers a 131,072 token context window
  • you want the most recent training data — it shipped Dec 2025
  • you need open weights you can self-host or fine-tune

Choose Gemma 3n E4B Instructed

  • you want predictable pricing at $20.00/M input and $40.00/M output

At a glance

The differences that matter most.

Benchmark wins
4 of 4
0 of 4
Input price
$0.28 / M
$20.00 / M
Output price
$0.42 / M
$40.00 / M
Context window
131,072
32,000
Released
Dec 2025
Jun 2025
License
MIT
Proprietary

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V3.2 (Thinking) outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench, MMLU-Pro), while Gemma 3n E4B Instructed is better at 0 benchmarks.

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

Wed Aug 26 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.2 (Thinking) costs less

For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 71.4x cheaper than Gemma 3n E4B Instructed ($20.00/1M tokens).

For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 95.2x cheaper than Gemma 3n E4B Instructed ($40.00/1M tokens).

In conclusion, Gemma 3n E4B Instructed is more expensive than DeepSeek-V3.2 (Thinking).*

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

Lowest available price from all providers
Wed Aug 26 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Google
Gemma 3n E4B Instructed
Input tokens$20.00
Output tokens$40.00
Best providerTogether
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

677.0B diff

DeepSeek-V3.2 (Thinking) has 677.0B more parameters than Gemma 3n E4B Instructed, making it 8462.5% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Google
Gemma 3n E4B Instructed
8.0Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
8.0B
Gemma 3n E4B Instructed

Context Window

Maximum input and output token capacity

DeepSeek-V3.2 (Thinking) accepts 131,072 input tokens compared to Gemma 3n E4B Instructed's 32,000 tokens. DeepSeek-V3.2 (Thinking) can generate longer responses up to 65,536 tokens, while Gemma 3n E4B Instructed is limited to 32,000 tokens.

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Google
Gemma 3n E4B Instructed
Input32,000 tokens
Output32,000 tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E4B Instructed supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.

Gemma 3n E4B Instructed 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 3n E4B Instructed

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Gemma 3n E4B Instructed uses a proprietary license.

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

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Gemma 3n E4B Instructed

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Gemma 3n E4B Instructed was released on 2025-06-26.

DeepSeek-V3.2 (Thinking) is 5 months newer than Gemma 3n E4B Instructed.

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

8 months ago

5mo newer
Gemma 3n E4B Instructed

Jun 26, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E4B Instructed has a documented knowledge cutoff of 2024-06-01, while DeepSeek-V3.2 (Thinking)'s cutoff date is not specified.

We can confirm Gemma 3n E4B Instructed's training data extends to 2024-06-01, but cannot make a direct comparison without DeepSeek-V3.2 (Thinking)'s cutoff date.

DeepSeek-V3.2 (Thinking)

Gemma 3n E4B Instructed

Jun 2024

Provider Availability

DeepSeek-V3.2 (Thinking) is available from DeepSeek. Gemma 3n E4B Instructed is available from Together.

DeepSeek-V3.2 (Thinking)

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

Gemma 3n E4B Instructed

together logo
Together
Input Price:Input: $20.00/1MOutput Price:Output: $40.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Thinking) and Gemma 3n E4B Instructed side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Thinking)
✓ Preferred
Gemma 3n E4B Instructed
Open in Playground

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Gemma 3n E4B Instructed.

Which is better, DeepSeek-V3.2 (Thinking) or Gemma 3n E4B Instructed?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Gemma 3n E4B Instructed 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 3n E4B Instructed 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 3n E4B Instructed scores HumanEval: 75.0%, MGSM: 67.0%, MMLU: 64.9%, Global-MMLU-Lite: 64.5%, MBPP: 63.6%.

Is DeepSeek-V3.2 (Thinking) cheaper than Gemma 3n E4B Instructed?

DeepSeek-V3.2 (Thinking) is 71.4x cheaper for input tokens. DeepSeek-V3.2 (Thinking) costs $0.28/M input and $0.42/M output via deepseek. Gemma 3n E4B Instructed costs $20.00/M input and $40.00/M output via together.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Gemma 3n E4B Instructed?

DeepSeek-V3.2 (Thinking) supports 131K tokens and Gemma 3n E4B Instructed supports 32K 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 3n E4B Instructed?

Key differences include context window (131K vs 32K), input pricing ($0.28 vs $20.00/M), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Gemma 3n E4B Instructed?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Gemma 3n E4B Instructed is developed by Google.