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Mistral NeMo Instruct vs QvQ-72B-Preview

QvQ-72B-Preview leads the LLM Stats Score 7.9 to -4.8.

Mistral AI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

QvQ-72B-Preview leads the overall LLM Stats Score 7.9 to -4.8, ranking #282 overall.

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

Choose Mistral NeMo Instruct

  • you want predictable pricing at $0.02/M input and $0.03/M output

Choose QvQ-72B-Preview

  • overall performance matters — it scores 7.9 and ranks #282 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Dec 2024

At a glance

The differences that matter most.

Core performance indexes
-4.8
#353
7.9
#282
-5.0
#345
8.2
#275
Cost, coverage & limits
Benchmark wins
Input price
$0.02 / M
— / M
Output price
$0.03 / M
— / M
Context window
131,072

Individual benchmarks

8 reported for Mistral NeMo Instruct · 4 for QvQ-72B-Preview

No common benchmarks found

Mistral NeMo Instruct 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

61.4B diff

QvQ-72B-Preview has 61.4B more parameters than Mistral NeMo Instruct, making it 511.7% larger.

Mistral AI
Mistral NeMo Instruct
12.0Bparameters
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
73.4Bparameters
12.0B
Mistral NeMo Instruct
73.4B
QvQ-72B-Preview

Context Window

Maximum input and output token capacity

Only Mistral NeMo Instruct specifies input context (131,072 tokens). Only Mistral NeMo Instruct specifies output context (131,072 tokens).

Mistral AI
Mistral NeMo Instruct
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
Input- tokens
Output- tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

QvQ-72B-Preview supports multimodal inputs, whereas Mistral NeMo Instruct does not.

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

Mistral NeMo Instruct

Text
Images
Audio
Video

QvQ-72B-Preview

Text
Images
Audio
Video

License

Usage and distribution terms

Mistral NeMo Instruct is licensed under Apache 2.0, while QvQ-72B-Preview uses Qwen.

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

Mistral NeMo Instruct

Apache 2.0

Open weights

QvQ-72B-Preview

Qwen

Open weights

Release Timeline

When each model was launched

Mistral NeMo Instruct was released on 2024-07-18, while QvQ-72B-Preview was released on 2024-12-25.

QvQ-72B-Preview is 5 months newer than Mistral NeMo Instruct.

Mistral NeMo Instruct

Jul 18, 2024

2.2 years ago

QvQ-72B-Preview

Dec 25, 2024

1.7 years ago

5mo 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 Mistral NeMo Instruct and QvQ-72B-Preview side-by-side, then vote on the output you prefer.

Mistral NeMo Instruct
✓ Preferred
QvQ-72B-Preview
Open in Playground

FAQ

Common questions about Mistral NeMo Instruct vs QvQ-72B-Preview.

Which is better, Mistral NeMo Instruct or QvQ-72B-Preview?

QvQ-72B-Preview leads the LLM Stats Score 7.9 to -4.8. Mistral NeMo Instruct is made by Mistral AI 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 Mistral NeMo Instruct compare to QvQ-72B-Preview in benchmarks?

Mistral NeMo Instruct scores HellaSwag: 83.5%, Winogrande: 76.8%, TriviaQA: 73.8%, CommonSenseQA: 70.4%, MMLU: 68.0%. QvQ-72B-Preview scores MathVista: 71.4%, MMMU: 70.3%, MathVision: 35.9%, OlympiadBench: 20.4%.

What are the context window sizes for Mistral NeMo Instruct and QvQ-72B-Preview?

Mistral NeMo Instruct supports 131K 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 Mistral NeMo Instruct and QvQ-72B-Preview?

Key differences include LLM Stats Score (-4.8 vs 7.9), multimodal support (no vs yes), licensing (Apache 2.0 vs Qwen). See the full comparison above for benchmark-by-benchmark results.

Who makes Mistral NeMo Instruct and QvQ-72B-Preview?

Mistral NeMo Instruct is developed by Mistral AI and QvQ-72B-Preview is developed by Alibaba Cloud / Qwen Team.