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DeepSeek-V4.1-Flash-1017 vs Gemma 4 31B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 33.0. Gemma 4 31B is 3.4x cheaper per token.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 33.0, ranking #15 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, Gemma 4 31B is roughly 3.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 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-V4.1-Flash-1017

  • overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Oct 2026

Choose Gemma 4 31B

  • cost matters — it's about 3.4x cheaper per token

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Gemma 4 31B
Google
Core performance indexes
LLM Stats Score
51.0
#15
33.0
#121
Reasoning
48.6
#18
33.5
#113
Agents
38.6
#9
13.7
#112
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.30 / M
$0.09 / M
Output price
$1.20 / M
$0.34 / M
Context window
1,048,576
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V4.1-Flash-1017
Gemma 4 31B
Math
31.8#67
29.5#90
Vision
32.2#26
19.7#89
Multimodal
35.0#14
22.4#74
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 12 for Gemma 4 31B

1 shared

DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (Humanity's Last Exam), while Gemma 4 31B is better at 0 benchmarks.

DeepSeek-V4.1-Flash-1017 significantly outperforms across most benchmarks.

Mon Oct 19 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Gemma 4 31B costs less

For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 3.3x more expensive than Gemma 4 31B ($0.09/1M tokens).

For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 3.5x more expensive than Gemma 4 31B ($0.34/1M tokens).

In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than Gemma 4 31B.*

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

Lowest available price from all providers
Mon Oct 19 2026 • llm-stats.com
DeepSeek
DeepSeek-V4.1-Flash-1017
Input tokens$0.30
Output tokens$1.20
Best providerDeepSeek
Google
Gemma 4 31B
Input tokens$0.09
Output tokens$0.34
Best providerDeepinfra
Notice missing or incorrect data?

Model Size

Parameter count comparison

732.5B diff

DeepSeek-V4.1-Flash-1017 has 732.5B more parameters than Gemma 4 31B, making it 2386.0% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Google
Gemma 4 31B
30.7Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
30.7B
Gemma 4 31B

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Gemma 4 31B's 262,144 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Gemma 4 31B is limited to 262,144 tokens.

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Google
Gemma 4 31B
Input262,144 tokens
Output262,144 tokens
Mon Oct 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Gemma 4 31B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Gemma 4 31B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Gemma 4 31B uses Apache 2.0.

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

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Gemma 4 31B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Gemma 4 31B was released on 2026-04-02.

DeepSeek-V4.1-Flash-1017 is 7 months newer than Gemma 4 31B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

0 days ago

6mo newer
Gemma 4 31B

Apr 2, 2026

6 months ago

Knowledge Cutoff

When training data ends

Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while DeepSeek-V4.1-Flash-1017's cutoff date is not specified.

We can confirm Gemma 4 31B's training data extends to 2025-01-01, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.

DeepSeek-V4.1-Flash-1017

—

Gemma 4 31B

Jan 2025

Provider Availability

DeepSeek-V4.1-Flash-1017 is available from DeepSeek. Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together.

DeepSeek-V4.1-Flash-1017

deepseek logo
DeepSeek
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

Gemma 4 31B

deepinfra logo
Deepinfra
Input Price:Input: $0.09/1MOutput Price:Output: $0.34/1M
friendli logo
FriendliAI
Input Price:Input: $0.14/1MOutput Price:Output: $0.40/1M
novita logo
Novita
Input Price:Input: $0.14/1MOutput Price:Output: $0.40/1M
together logo
Together
Input Price:Input: $0.39/1MOutput Price:Output: $0.97/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Gemma 4 31B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Gemma 4 31B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Gemma 4 31B.

Which is better, DeepSeek-V4.1-Flash-1017 or Gemma 4 31B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 33.0. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Gemma 4 31B 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-V4.1-Flash-1017 compare to Gemma 4 31B in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. Gemma 4 31B scores AIME 2026: 89.2%, MMMLU: 88.4%, t2-bench: 86.4%, MathVision: 85.6%, MMLU-Pro: 85.2%.

Is DeepSeek-V4.1-Flash-1017 cheaper than Gemma 4 31B?

Gemma 4 31B is 3.3x cheaper for input tokens. DeepSeek-V4.1-Flash-1017 costs $0.30/M input and $1.20/M output via deepseek. Gemma 4 31B costs $0.09/M input and $0.34/M output via deepinfra.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Gemma 4 31B?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Gemma 4 31B 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-V4.1-Flash-1017 and Gemma 4 31B?

Key differences include LLM Stats Score (51.0 vs 33.0), context window (1.0M vs 262K), input pricing ($0.30 vs $0.09/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Gemma 4 31B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Gemma 4 31B is developed by Google.

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