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DeepSeek-V3.2 (Thinking) vs Gemma 4 26B-A4B

DeepSeek-V3.2 (Thinking) and Gemma 4 26B-A4B are closely matched at 32.7 and 29.7 on the LLM Stats Score. Gemma 4 26B-A4B is 2.3x cheaper per token.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V3.2 (Thinking) and Gemma 4 26B-A4B are closely matched on the overall LLM Stats Score at 32.7 and 29.7.

In the 4 individual benchmarks reported for both models, DeepSeek-V3.2 (Thinking) wins 3; this is a narrower head-to-head signal than the composite indexes.

On price, Gemma 4 26B-A4B is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemma 4 26B-A4B also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.

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

Choose DeepSeek-V3.2 (Thinking)

  • you value its reported benchmark strengths — it wins 3 of 4 exact shared results

Choose Gemma 4 26B-A4B

  • cost matters — it's about 2.3x cheaper per token
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Apr 2026

At a glance

The differences that matter most.

DeepSeek-V3.2 (Thinking)
DeepSeek
Gemma 4 26B-A4B
Google
Core performance indexes
LLM Stats Score
32.7
#102
29.7
#124
Reasoning
32.7
#100
29.4
#119
Agents
11.2
#109
12.8
#97
Cost, coverage & limits
Benchmark wins
3 of 4
1 of 4
Input price
$0.28 / M
$0.07 / M
Output price
$0.42 / M
$0.34 / M
Context window
131,072
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V3.2 (Thinking)
Gemma 4 26B-A4B
Math
30.3#76
25.4#111
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

14 reported for DeepSeek-V3.2 (Thinking) · 12 for Gemma 4 26B-A4B

4 shared

DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (GPQA, Humanity's Last Exam, MMLU-Pro), while Gemma 4 26B-A4B is better at 1 benchmark (t2-bench).

DeepSeek-V3.2 (Thinking) shows notably better performance in the majority of benchmarks.

Wed Sep 09 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Gemma 4 26B-A4B costs less

For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 4.0x more expensive than Gemma 4 26B-A4B ($0.07/1M tokens).

For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 1.2x more expensive than Gemma 4 26B-A4B ($0.34/1M tokens).

In conclusion, DeepSeek-V3.2 (Thinking) is more expensive than Gemma 4 26B-A4B.*

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

Lowest available price from all providers
Wed Sep 09 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Google
Gemma 4 26B-A4B
Input tokens$0.07
Output tokens$0.34
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

659.8B diff

DeepSeek-V3.2 (Thinking) has 659.8B more parameters than Gemma 4 26B-A4B, making it 2618.3% larger.

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Google
Gemma 4 26B-A4B
25.2Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
25.2B
Gemma 4 26B-A4B

Context Window

Maximum input and output token capacity

Gemma 4 26B-A4B accepts 262,144 input tokens compared to DeepSeek-V3.2 (Thinking)'s 131,072 tokens. Gemma 4 26B-A4B can generate longer responses up to 262,144 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 4 26B-A4B
Input262,144 tokens
Output262,144 tokens
Wed Sep 09 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemma 4 26B-A4B supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.

Gemma 4 26B-A4B 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 4 26B-A4B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Gemma 4 26B-A4B uses Apache 2.0.

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

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Gemma 4 26B-A4B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Gemma 4 26B-A4B was released on 2026-04-02.

Gemma 4 26B-A4B is 4 months newer than DeepSeek-V3.2 (Thinking).

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

9 months ago

Gemma 4 26B-A4B

Apr 2, 2026

5 months ago

4mo newer

Knowledge Cutoff

When training data ends

Gemma 4 26B-A4B has a documented knowledge cutoff of 2025-01-01, while DeepSeek-V3.2 (Thinking)'s cutoff date is not specified.

We can confirm Gemma 4 26B-A4B's training data extends to 2025-01-01, but cannot make a direct comparison without DeepSeek-V3.2 (Thinking)'s cutoff date.

DeepSeek-V3.2 (Thinking)

—

Gemma 4 26B-A4B

Jan 2025

Provider Availability

DeepSeek-V3.2 (Thinking) is available from DeepSeek. Gemma 4 26B-A4B is available from DeepInfra, Novita.

DeepSeek-V3.2 (Thinking)

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

Gemma 4 26B-A4B

deepinfra logo
Deepinfra
Input Price:Input: $0.07/1MOutput Price:Output: $0.34/1M
novita logo
Novita
Input Price:Input: $0.13/1MOutput Price:Output: $0.40/1M
* 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 4 26B-A4B side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Thinking)
✓ Preferred
Gemma 4 26B-A4B
Open in Playground

FAQ

Common questions about DeepSeek-V3.2 (Thinking) vs Gemma 4 26B-A4B.

Which is better, DeepSeek-V3.2 (Thinking) or Gemma 4 26B-A4B?

DeepSeek-V3.2 (Thinking) and Gemma 4 26B-A4B are closely matched on the LLM Stats Score at 32.7 and 29.7. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Gemma 4 26B-A4B 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-V3.2 (Thinking) compare to Gemma 4 26B-A4B 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 4 26B-A4B scores AIME 2026: 88.3%, MMMLU: 86.3%, t2-bench: 85.5%, MMLU-Pro: 82.6%, MathVision: 82.4%.

Is DeepSeek-V3.2 (Thinking) cheaper than Gemma 4 26B-A4B?

Gemma 4 26B-A4B is 4.0x cheaper for input tokens. DeepSeek-V3.2 (Thinking) costs $0.28/M input and $0.42/M output via deepseek. Gemma 4 26B-A4B costs $0.07/M input and $0.34/M output via deepinfra.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Gemma 4 26B-A4B?

DeepSeek-V3.2 (Thinking) supports 131K tokens and Gemma 4 26B-A4B supports 262K 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 4 26B-A4B?

Key differences include LLM Stats Score (32.7 vs 29.7), context window (131K vs 262K), input pricing ($0.28 vs $0.07/M), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.2 (Thinking) and Gemma 4 26B-A4B?

DeepSeek-V3.2 (Thinking) is developed by DeepSeek and Gemma 4 26B-A4B is developed by Google.

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