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Llama-3.3 Nemotron Super 49B v1 vs Qwen3 VL 4B Thinking

Llama-3.3 Nemotron Super 49B v1 and Qwen3 VL 4B Thinking are closely matched at 13.1 and 12.9 on the LLM Stats Score.

NVIDIA · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Llama-3.3 Nemotron Super 49B v1 and Qwen3 VL 4B Thinking are closely matched on the overall LLM Stats Score at 13.1 and 12.9.

The models split the 2 individual benchmarks reported for both models evenly.

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

Choose Llama-3.3 Nemotron Super 49B v1

  • you are already invested in the NVIDIA ecosystem

Choose Qwen3 VL 4B Thinking

  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

Llama-3.3 Nemotron Super 49B v1
NVIDIA
Qwen3 VL 4B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
13.1
#246
12.9
#249
Reasoning
13.2
#239
14.0
#231
Cost, coverage & limits
Benchmark wins
1 of 2
1 of 2
Input price
— / M
$0.10 / M
Output price
— / M
$1.00 / M
Context window
—
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Llama-3.3 Nemotron Super 49B v1
Qwen3 VL 4B Thinking
Math
11.7#237
15.6#216
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

7 reported for Llama-3.3 Nemotron Super 49B v1 · 48 for Qwen3 VL 4B Thinking

2 shared

Llama-3.3 Nemotron Super 49B v1 outperforms in 1 benchmarks (GPQA), while Qwen3 VL 4B Thinking is better at 1 benchmark (AIME 2025).

Both models are evenly matched across the benchmarks.

Sun Sep 20 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

45.9B diff

Llama-3.3 Nemotron Super 49B v1 has 45.9B more parameters than Qwen3 VL 4B Thinking, making it 1147.5% larger.

NVIDIA
Llama-3.3 Nemotron Super 49B v1
49.9Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
4.0Bparameters
49.9B
Llama-3.3 Nemotron Super 49B v1
4.0B
Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 4B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 4B Thinking specifies output context (262,144 tokens).

NVIDIA
Llama-3.3 Nemotron Super 49B v1
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input262,144 tokens
Output262,144 tokens
Sun Sep 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3 VL 4B Thinking supports multimodal inputs, whereas Llama-3.3 Nemotron Super 49B v1 does not.

Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Llama-3.3 Nemotron Super 49B v1

Text
Images
Audio
Video

Qwen3 VL 4B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Llama-3.3 Nemotron Super 49B v1 is licensed under Llama 3.1 Community License, while Qwen3 VL 4B Thinking uses Apache 2.0.

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

Llama-3.3 Nemotron Super 49B v1

Llama 3.1 Community License

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Llama-3.3 Nemotron Super 49B v1 was released on 2025-03-18, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 6 months newer than Llama-3.3 Nemotron Super 49B v1.

Llama-3.3 Nemotron Super 49B v1

Mar 18, 2025

1.5 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

12 months ago

6mo newer

Knowledge Cutoff

When training data ends

Llama-3.3 Nemotron Super 49B v1 has a documented knowledge cutoff of 2023-12-31, while Qwen3 VL 4B Thinking's cutoff date is not specified.

We can confirm Llama-3.3 Nemotron Super 49B v1's training data extends to 2023-12-31, but cannot make a direct comparison without Qwen3 VL 4B Thinking's cutoff date.

Llama-3.3 Nemotron Super 49B v1

Dec 2023

Qwen3 VL 4B Thinking

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Llama-3.3 Nemotron Super 49B v1 and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.

Llama-3.3 Nemotron Super 49B v1
✓ Preferred
Qwen3 VL 4B Thinking
Open in Playground

FAQ

Common questions about Llama-3.3 Nemotron Super 49B v1 vs Qwen3 VL 4B Thinking.

Which is better, Llama-3.3 Nemotron Super 49B v1 or Qwen3 VL 4B Thinking?

Llama-3.3 Nemotron Super 49B v1 and Qwen3 VL 4B Thinking are closely matched on the LLM Stats Score at 13.1 and 12.9. Llama-3.3 Nemotron Super 49B v1 is made by NVIDIA and Qwen3 VL 4B Thinking 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 Llama-3.3 Nemotron Super 49B v1 compare to Qwen3 VL 4B Thinking in benchmarks?

Llama-3.3 Nemotron Super 49B v1 scores MATH-500: 96.6%, MT-Bench: 91.7%, MBPP: 91.3%, Arena Hard: 88.3%, BFCL v2: 73.7%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

What are the context window sizes for Llama-3.3 Nemotron Super 49B v1 and Qwen3 VL 4B Thinking?

Llama-3.3 Nemotron Super 49B v1 supports an unknown number of tokens and Qwen3 VL 4B Thinking 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 Llama-3.3 Nemotron Super 49B v1 and Qwen3 VL 4B Thinking?

Key differences include LLM Stats Score (13.1 vs 12.9), multimodal support (no vs yes), licensing (Llama 3.1 Community License vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Llama-3.3 Nemotron Super 49B v1 and Qwen3 VL 4B Thinking?

Llama-3.3 Nemotron Super 49B v1 is developed by NVIDIA and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.

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