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DeepSeek-V4-Flash-0731 vs Gemma 3n E2B Instructed LiteRT (Preview)

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to -9.6.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4-Flash-0731 leads the overall LLM Stats Score 44.7 to -9.6, 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

Choose Gemma 3n E2B Instructed LiteRT (Preview)

  • 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 LiteRT (Preview)
Google
Core performance indexes
LLM Stats Score
44.7
#35
-9.6
#364
Reasoning
42.3
#45
-9.8
#357
Coding
33.0
#36
-4.4
#255
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 · 28 for Gemma 3n E2B Instructed LiteRT (Preview)

No common benchmarks found

DeepSeek-V4-Flash-0731 and Gemma 3n E2B Instructed LiteRT (Preview)don'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

302.1B diff

DeepSeek-V4-Flash-0731 has 302.1B more parameters than Gemma 3n E2B Instructed LiteRT (Preview), making it 15816.2% larger.

DeepSeek
DeepSeek-V4-Flash-0731
304.0Bparameters
Google
Gemma 3n E2B Instructed LiteRT (Preview)
1.9Bparameters
304.0B
DeepSeek-V4-Flash-0731
1.9B
Gemma 3n E2B Instructed LiteRT (Preview)

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 LiteRT (Preview)
Input- tokens
Output- tokens
Sun Sep 13 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

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

Gemma 3n E2B Instructed LiteRT (Preview) 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 LiteRT (Preview)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Flash-0731 is licensed under MIT, while Gemma 3n E2B Instructed LiteRT (Preview) uses Gemma.

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 LiteRT (Preview)

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Gemma 3n E2B Instructed LiteRT (Preview) was released on 2025-05-20.

DeepSeek-V4-Flash-0731 is 15 months newer than Gemma 3n E2B Instructed LiteRT (Preview).

DeepSeek-V4-Flash-0731

Jul 31, 2026

1 months ago

1.2yr newer
Gemma 3n E2B Instructed LiteRT (Preview)

May 20, 2025

1.3 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed LiteRT (Preview) 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 LiteRT (Preview)'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 LiteRT (Preview)

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 LiteRT (Preview) side-by-side, then vote on the output you prefer.

DeepSeek-V4-Flash-0731
✓ Preferred
Gemma 3n E2B Instructed LiteRT (Preview)
Open in Playground

FAQ

Common questions about DeepSeek-V4-Flash-0731 vs Gemma 3n E2B Instructed LiteRT (Preview).

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

DeepSeek-V4-Flash-0731 leads the LLM Stats Score 44.7 to -9.6. DeepSeek-V4-Flash-0731 is made by DeepSeek and Gemma 3n E2B Instructed LiteRT (Preview) 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 LiteRT (Preview) 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 LiteRT (Preview) scores PIQA: 78.9%, BoolQ: 76.4%, ARC-E: 75.8%, HellaSwag: 72.2%, Winogrande: 66.8%.

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

DeepSeek-V4-Flash-0731 supports 1.0M tokens and Gemma 3n E2B Instructed LiteRT (Preview) 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 LiteRT (Preview)?

Key differences include LLM Stats Score (44.7 vs -9.6), multimodal support (no vs yes), licensing (MIT vs Gemma). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Flash-0731 and Gemma 3n E2B Instructed LiteRT (Preview)?

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

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