The AI arena is free today

Open Superagent

DeepSeek-V2.5 vs Gemma 2 27B

DeepSeek-V2.5 leads the LLM Stats Score 8.4 to -0.5.

DeepSeek · Google · Updated for 2026

Which is better?

DeepSeek-V2.5 leads the overall LLM Stats Score 8.4 to -0.5, ranking #265 overall.

In the 4 individual benchmarks reported for both models, DeepSeek-V2.5 wins 4; this is a narrower head-to-head signal than the composite indexes.

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

Choose DeepSeek-V2.5

  • overall performance matters — it scores 8.4 and ranks #265 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 4 of 4 exact shared results

Choose Gemma 2 27B

  • you want the most recent training data — it shipped Jun 2024

At a glance

The differences that matter most.

Core performance indexes
8.4
#265
-0.5
#313
8.5
#259
-0.9
#310
6.5
#178
-7.9
#249
Cost, coverage & limits
Benchmark wins
4 of 4
0 of 4
Input price
$0.14 / M
— / M
Output price
$0.28 / M
— / M
Context window
8,192

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V2.5
Gemma 2 27B
14.4#210
-0.3#287
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for DeepSeek-V2.5 · 16 for Gemma 2 27B

4 shared

DeepSeek-V2.5 outperforms in 4 benchmarks (GSM8k, HumanEval, MATH, MMLU), while Gemma 2 27B is better at 0 benchmarks.

DeepSeek-V2.5 significantly outperforms across most benchmarks.

Mon Aug 31 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

208.8B diff

DeepSeek-V2.5 has 208.8B more parameters than Gemma 2 27B, making it 767.6% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Google
Gemma 2 27B
27.2Bparameters
236.0B
DeepSeek-V2.5
27.2B
Gemma 2 27B

Context Window

Maximum input and output token capacity

Only DeepSeek-V2.5 specifies input context (8,192 tokens). Only DeepSeek-V2.5 specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Google
Gemma 2 27B
Input- tokens
Output- tokens
Mon Aug 31 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Gemma 2 27B uses Gemma.

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

DeepSeek-V2.5

deepseek

Open weights

Gemma 2 27B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Gemma 2 27B was released on 2024-06-27.

Gemma 2 27B is 2 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.3 years ago

Gemma 2 27B

Jun 27, 2024

2.2 years ago

1mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against DeepSeek-V2.5 and Gemma 2 27B side-by-side, then vote on the output you prefer.

DeepSeek-V2.5
✓ Preferred
Gemma 2 27B
Open in Playground

FAQ

Common questions about DeepSeek-V2.5 vs Gemma 2 27B.

Which is better, DeepSeek-V2.5 or Gemma 2 27B?

DeepSeek-V2.5 leads the LLM Stats Score 8.4 to -0.5. DeepSeek-V2.5 is made by DeepSeek and Gemma 2 27B 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-V2.5 compare to Gemma 2 27B in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. Gemma 2 27B scores ARC-E: 88.6%, HellaSwag: 86.4%, BoolQ: 84.8%, TriviaQA: 83.7%, Winogrande: 83.7%.

What are the context window sizes for DeepSeek-V2.5 and Gemma 2 27B?

DeepSeek-V2.5 supports 8K tokens and Gemma 2 27B 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-V2.5 and Gemma 2 27B?

Key differences include LLM Stats Score (8.4 vs -0.5), licensing (deepseek vs Gemma). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V2.5 and Gemma 2 27B?

DeepSeek-V2.5 is developed by DeepSeek and Gemma 2 27B is developed by Google.