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DeepSeek-V3 vs Qwen2.5 14B Instruct

DeepSeek-V3 leads the LLM Stats Score 15.8 to 3.9.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-V3 leads the overall LLM Stats Score 15.8 to 3.9, ranking #215 overall.

In the 4 individual benchmarks reported for both models, DeepSeek-V3 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-V3

  • overall performance matters — it scores 15.8 and ranks #215 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
  • you want the most recent training data — it shipped Dec 2024

Choose Qwen2.5 14B Instruct

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

At a glance

The differences that matter most.

Core performance indexes
15.8
#215
3.9
#292
14.9
#216
3.3
#289
6.5
#183
4.0
#205
Cost, coverage & limits
Benchmark wins
4 of 4
0 of 4
Input price
$0.27 / M
— / M
Output price
$1.10 / M
— / M
Context window
131,072

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V3
Qwen2.5 14B Instruct
18.2#174
10.7#242
20.1#79
5.4#164
20.1#64
6.0#145
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V3 · 16 for Qwen2.5 14B Instruct

4 shared

DeepSeek-V3 outperforms in 4 benchmarks (GPQA, MMLU, MMLU-Pro, MMLU-Redux), while Qwen2.5 14B Instruct is better at 0 benchmarks.

DeepSeek-V3 significantly outperforms across most benchmarks.

Thu Sep 03 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

656.3B diff

DeepSeek-V3 has 656.3B more parameters than Qwen2.5 14B Instruct, making it 4464.6% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 14B Instruct
14.7Bparameters
671.0B
DeepSeek-V3
14.7B
Qwen2.5 14B Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 14B Instruct
Input- tokens
Output- tokens
Thu Sep 03 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Qwen2.5 14B Instruct uses Apache 2.0.

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

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Qwen2.5 14B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Qwen2.5 14B Instruct was released on 2024-09-19.

DeepSeek-V3 is 3 months newer than Qwen2.5 14B Instruct.

DeepSeek-V3

Dec 25, 2024

1.7 years ago

3mo newer
Qwen2.5 14B Instruct

Sep 19, 2024

2.0 years ago

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-V3 and Qwen2.5 14B Instruct side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Qwen2.5 14B Instruct
Open in Playground

FAQ

Common questions about DeepSeek-V3 vs Qwen2.5 14B Instruct.

Which is better, DeepSeek-V3 or Qwen2.5 14B Instruct?

DeepSeek-V3 leads the LLM Stats Score 15.8 to 3.9. DeepSeek-V3 is made by DeepSeek and Qwen2.5 14B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3 compare to Qwen2.5 14B Instruct in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Qwen2.5 14B Instruct scores GSM8k: 94.8%, HumanEval: 83.5%, MBPP: 82.0%, MATH: 80.0%, MMLU-Redux: 80.0%.

What are the context window sizes for DeepSeek-V3 and Qwen2.5 14B Instruct?

DeepSeek-V3 supports 131K tokens and Qwen2.5 14B Instruct 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-V3 and Qwen2.5 14B Instruct?

Key differences include LLM Stats Score (15.8 vs 3.9), licensing (MIT + Model License (Commercial use allowed) vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Qwen2.5 14B Instruct?

DeepSeek-V3 is developed by DeepSeek and Qwen2.5 14B Instruct is developed by Alibaba Cloud / Qwen Team.