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DeepSeek-V3.1 vs QvQ-72B-Preview

DeepSeek-V3.1 leads the LLM Stats Score 22.1 to 7.9.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

DeepSeek-V3.1 leads the overall LLM Stats Score 22.1 to 7.9, ranking #178 overall.

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

Choose DeepSeek-V3.1

  • overall performance matters — it scores 22.1 and ranks #178 on LLM Stats
  • you want the most recent training data — it shipped Jan 2025

Choose QvQ-72B-Preview

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

At a glance

The differences that matter most.

DeepSeek-V3.1
DeepSeek
QvQ-72B-Preview
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
22.1
#178
7.9
#282
Reasoning
22.3
#171
8.2
#276
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.25 / M
— / M
Output price
$0.95 / M
— / M
Context window
163,840
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V3.1
QvQ-72B-Preview
Math
18.7#180
9.6#253
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

16 reported for DeepSeek-V3.1 · 4 for QvQ-72B-Preview

No common benchmarks found

DeepSeek-V3.1 and QvQ-72B-Previewdon'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

597.6B diff

DeepSeek-V3.1 has 597.6B more parameters than QvQ-72B-Preview, making it 814.2% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
73.4Bparameters
671.0B
DeepSeek-V3.1
73.4B
QvQ-72B-Preview

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
Input- tokens
Output- tokens
Mon Sep 21 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

QvQ-72B-Preview supports multimodal inputs, whereas DeepSeek-V3.1 does not.

QvQ-72B-Preview can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.1

Text
Images
Audio
Video

QvQ-72B-Preview

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while QvQ-72B-Preview uses Qwen.

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

DeepSeek-V3.1

MIT

Open weights

QvQ-72B-Preview

Qwen

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while QvQ-72B-Preview was released on 2024-12-25.

DeepSeek-V3.1 is 1 month newer than QvQ-72B-Preview.

DeepSeek-V3.1

Jan 10, 2025

1.7 years ago

2w newer
QvQ-72B-Preview

Dec 25, 2024

1.7 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-V3.1 and QvQ-72B-Preview side-by-side, then vote on the output you prefer.

DeepSeek-V3.1
✓ Preferred
QvQ-72B-Preview
Open in Playground

FAQ

Common questions about DeepSeek-V3.1 vs QvQ-72B-Preview.

Which is better, DeepSeek-V3.1 or QvQ-72B-Preview?

DeepSeek-V3.1 leads the LLM Stats Score 22.1 to 7.9. DeepSeek-V3.1 is made by DeepSeek and QvQ-72B-Preview 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-V3.1 compare to QvQ-72B-Preview in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. QvQ-72B-Preview scores MathVista: 71.4%, MMMU: 70.3%, MathVision: 35.9%, OlympiadBench: 20.4%.

What are the context window sizes for DeepSeek-V3.1 and QvQ-72B-Preview?

DeepSeek-V3.1 supports 164K tokens and QvQ-72B-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-V3.1 and QvQ-72B-Preview?

Key differences include LLM Stats Score (22.1 vs 7.9), multimodal support (no vs yes), licensing (MIT vs Qwen). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3.1 and QvQ-72B-Preview?

DeepSeek-V3.1 is developed by DeepSeek and QvQ-72B-Preview is developed by Alibaba Cloud / Qwen Team.

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