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DeepSeek-V4-Flash-0731 vs Gemma 3n E2B Instructed

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to -10.6.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to -10.6, ranking #35 overall.

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

Choose DeepSeek-V4-Flash-0731

  • overall performance matters — it scores 44.7 and ranks #35 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Jul 2026
  • you need open weights you can self-host or fine-tune

Choose Gemma 3n E2B Instructed

  • you are already invested in the Google ecosystem

At a glance

The differences that matter most.

DeepSeek-V4-Flash-0731
DeepSeek
Gemma 3n E2B Instructed
Google
Core performance indexes
LLM Stats Score
44.7
#35
-10.6
#367
Reasoning
42.3
#45
-11.0
#359
Coding
33.0
#36
-4.4
#256
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.06 / M
— / M
Output price
$0.18 / M
— / M
Context window
1,048,576
—

Individual benchmarks

9 reported for DeepSeek-V4-Flash-0731 · 18 for Gemma 3n E2B Instructed

No common benchmarks found

DeepSeek-V4-Flash-0731 and Gemma 3n E2B Instructeddon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

296.0B diff

DeepSeek-V4-Flash-0731 has 296.0B more parameters than Gemma 3n E2B Instructed, making it 3700.0% larger.

DeepSeek
DeepSeek-V4-Flash-0731
304.0Bparameters
Google
Gemma 3n E2B Instructed
8.0Bparameters
304.0B
DeepSeek-V4-Flash-0731
8.0B
Gemma 3n E2B Instructed

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Flash-0731 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-0731 specifies output context (1,048,576 tokens).

DeepSeek
DeepSeek-V4-Flash-0731
Input1,048,576 tokens
Output1,048,576 tokens
Google
Gemma 3n E2B Instructed
Input- tokens
Output- tokens
Sun Sep 13 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemma 3n E2B Instructed supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.

Gemma 3n E2B Instructed can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4-Flash-0731

Text
Images
Audio
Video

Gemma 3n E2B Instructed

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Flash-0731 is licensed under MIT, while Gemma 3n E2B Instructed uses a proprietary license.

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

DeepSeek-V4-Flash-0731

MIT

Open weights

Gemma 3n E2B Instructed

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Gemma 3n E2B Instructed was released on 2025-06-26.

DeepSeek-V4-Flash-0731 is 13 months newer than Gemma 3n E2B Instructed.

DeepSeek-V4-Flash-0731

Jul 31, 2026

1 months ago

1.1yr newer
Gemma 3n E2B Instructed

Jun 26, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed has a documented knowledge cutoff of 2024-06-01, while DeepSeek-V4-Flash-0731's cutoff date is not specified.

We can confirm Gemma 3n E2B Instructed's training data extends to 2024-06-01, but cannot make a direct comparison without DeepSeek-V4-Flash-0731's cutoff date.

DeepSeek-V4-Flash-0731

—

Gemma 3n E2B Instructed

Jun 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against DeepSeek-V4-Flash-0731 and Gemma 3n E2B Instructed side-by-side, then vote on the output you prefer.

DeepSeek-V4-Flash-0731
✓ Preferred
Gemma 3n E2B Instructed
Open in Playground

FAQ

Common questions about DeepSeek-V4-Flash-0731 vs Gemma 3n E2B Instructed.

Which is better, DeepSeek-V4-Flash-0731 or Gemma 3n E2B Instructed?

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to -10.6. DeepSeek-V4-Flash-0731 is made by DeepSeek and Gemma 3n E2B Instructed 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-V4-Flash-0731 compare to Gemma 3n E2B Instructed in benchmarks?

DeepSeek-V4-Flash-0731 scores Terminal-Bench 2.1: 82.7%, CyberGym: 76.7%, Toolathlon: 70.3%, DSBench-FullStack: 68.7%, DSBench-Hard: 59.6%. Gemma 3n E2B Instructed scores HumanEval: 66.5%, MMLU: 60.1%, Global-MMLU-Lite: 59.0%, MBPP: 56.6%, Global-MMLU: 55.1%.

What are the context window sizes for DeepSeek-V4-Flash-0731 and Gemma 3n E2B Instructed?

DeepSeek-V4-Flash-0731 supports 1.0M tokens and Gemma 3n E2B Instructed supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4-Flash-0731 and Gemma 3n E2B Instructed?

Key differences include LLM Stats Score (44.7 vs -10.6), multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Flash-0731 and Gemma 3n E2B Instructed?

DeepSeek-V4-Flash-0731 is developed by DeepSeek and Gemma 3n E2B Instructed is developed by Google.

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