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Nemotron 3.5 Super VL (120B A12B) vs Qwen2.5 7B Instruct

Nemotron 3.5 Super VL (120B A12B) leads the LLM Stats Score 35.0 to 2.1. Qwen2.5 7B Instruct is 1.3x cheaper per token.

NVIDIA · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Nemotron 3.5 Super VL (120B A12B) leads the overall LLM Stats Score 35.0 to 2.1, ranking #108 overall.

In the 1 individual benchmarks reported for both models, Nemotron 3.5 Super VL (120B A12B) wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, Qwen2.5 7B Instruct is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Nemotron 3.5 Super VL (120B A12B) also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.

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

Choose Nemotron 3.5 Super VL (120B A12B)

  • overall performance matters — it scores 35.0 and ranks #108 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Oct 2026

Choose Qwen2.5 7B Instruct

  • cost matters — it's about 1.3x cheaper per token

At a glance

The differences that matter most.

Nemotron 3.5 Super VL (120B A12B)
NVIDIA
Qwen2.5 7B Instruct
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
35.0
#108
2.1
#337
Reasoning
36.2
#94
2.3
#330
Coding
21.0
#108
3.0
#239
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.20 / M
$0.30 / M
Output price
$1.00 / M
$0.30 / M
Context window
262,144
131,072

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Nemotron 3.5 Super VL (120B A12B)
Qwen2.5 7B Instruct
Math
28.1#101
8.1#272
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

19 reported for Nemotron 3.5 Super VL (120B A12B) · 14 for Qwen2.5 7B Instruct

1 shared

Nemotron 3.5 Super VL (120B A12B) outperforms in 1 benchmarks (GPQA), while Qwen2.5 7B Instruct is better at 0 benchmarks.

Nemotron 3.5 Super VL (120B A12B) significantly outperforms across most benchmarks.

Tue Oct 20 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Qwen2.5 7B Instruct costs less

For input processing, Nemotron 3.5 Super VL (120B A12B) ($0.20/1M tokens) is 1.5x cheaper than Qwen2.5 7B Instruct ($0.30/1M tokens).

For output processing, Nemotron 3.5 Super VL (120B A12B) ($1.00/1M tokens) is 3.3x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).

In conclusion, Nemotron 3.5 Super VL (120B A12B) is more expensive than Qwen2.5 7B Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Oct 20 2026 • llm-stats.com
NVIDIA
Nemotron 3.5 Super VL (120B A12B)
Input tokens$0.20
Output tokens$1.00
Best providerDeepinfra
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
Input tokens$0.30
Output tokens$0.30
Best providerTogether
Notice missing or incorrect data?

Model Size

Parameter count comparison

112.4B diff

Nemotron 3.5 Super VL (120B A12B) has 112.4B more parameters than Qwen2.5 7B Instruct, making it 1476.9% larger.

NVIDIA
Nemotron 3.5 Super VL (120B A12B)
120.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
7.6Bparameters
120.0B
Nemotron 3.5 Super VL (120B A12B)
7.6B
Qwen2.5 7B Instruct

Context Window

Maximum input and output token capacity

Nemotron 3.5 Super VL (120B A12B) accepts 262,144 input tokens compared to Qwen2.5 7B Instruct's 131,072 tokens. Nemotron 3.5 Super VL (120B A12B) can generate longer responses up to 262,144 tokens, while Qwen2.5 7B Instruct is limited to 8,192 tokens.

NVIDIA
Nemotron 3.5 Super VL (120B A12B)
Input262,144 tokens
Output262,144 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 7B Instruct
Input131,072 tokens
Output8,192 tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Nemotron 3.5 Super VL (120B A12B) supports multimodal inputs, whereas Qwen2.5 7B Instruct does not.

Nemotron 3.5 Super VL (120B A12B) can handle both text and other forms of data like images, making it suitable for multimodal applications.

Nemotron 3.5 Super VL (120B A12B)

Text
Images
Audio
Video

Qwen2.5 7B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Nemotron 3.5 Super VL (120B A12B) is licensed under OpenMDW License v1.1, while Qwen2.5 7B Instruct uses Apache 2.0.

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

Nemotron 3.5 Super VL (120B A12B)

OpenMDW License v1.1

Open weights

Qwen2.5 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Nemotron 3.5 Super VL (120B A12B) was released on 2026-10-15, while Qwen2.5 7B Instruct was released on 2024-09-19.

Nemotron 3.5 Super VL (120B A12B) is 25 months newer than Qwen2.5 7B Instruct.

Nemotron 3.5 Super VL (120B A12B)

Oct 15, 2026

4 days ago

2.1yr newer
Qwen2.5 7B Instruct

Sep 19, 2024

2.1 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

Provider Availability

Nemotron 3.5 Super VL (120B A12B) is available from DeepInfra. Qwen2.5 7B Instruct is available from Together.

Nemotron 3.5 Super VL (120B A12B)

deepinfra logo
Deepinfra
Input Price:Input: $0.20/1MOutput Price:Output: $1.00/1M

Qwen2.5 7B Instruct

together logo
Together
Input Price:Input: $0.30/1MOutput Price:Output: $0.30/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against Nemotron 3.5 Super VL (120B A12B) and Qwen2.5 7B Instruct side-by-side, then vote on the output you prefer.

Nemotron 3.5 Super VL (120B A12B)
✓ Preferred
Qwen2.5 7B Instruct
Open in Playground

FAQ

Common questions about Nemotron 3.5 Super VL (120B A12B) vs Qwen2.5 7B Instruct.

Which is better, Nemotron 3.5 Super VL (120B A12B) or Qwen2.5 7B Instruct?

Nemotron 3.5 Super VL (120B A12B) leads the LLM Stats Score 35.0 to 2.1. Nemotron 3.5 Super VL (120B A12B) is made by NVIDIA and Qwen2.5 7B 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 Nemotron 3.5 Super VL (120B A12B) compare to Qwen2.5 7B Instruct in benchmarks?

Nemotron 3.5 Super VL (120B A12B) scores GPQA: 89.5%, LiveCodeBench v6: 88.2%, CharXiv-R: 80.8%, MathVision: 78.2%, BrowseComp: 78.0%. Qwen2.5 7B Instruct scores GSM8k: 91.6%, MT-Bench: 87.5%, HumanEval: 84.8%, MBPP: 79.2%, MATH: 75.5%.

Is Nemotron 3.5 Super VL (120B A12B) cheaper than Qwen2.5 7B Instruct?

Nemotron 3.5 Super VL (120B A12B) is 1.5x cheaper for input tokens. Nemotron 3.5 Super VL (120B A12B) costs $0.20/M input and $1.00/M output via deepinfra. Qwen2.5 7B Instruct costs $0.30/M input and $0.30/M output via together.

What are the context window sizes for Nemotron 3.5 Super VL (120B A12B) and Qwen2.5 7B Instruct?

Nemotron 3.5 Super VL (120B A12B) supports 262K tokens and Qwen2.5 7B Instruct supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Nemotron 3.5 Super VL (120B A12B) and Qwen2.5 7B Instruct?

Key differences include LLM Stats Score (35.0 vs 2.1), context window (262K vs 131K), input pricing ($0.20 vs $0.30/M), multimodal support (yes vs no), licensing (OpenMDW License v1.1 vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Nemotron 3.5 Super VL (120B A12B) and Qwen2.5 7B Instruct?

Nemotron 3.5 Super VL (120B A12B) is developed by NVIDIA and Qwen2.5 7B Instruct is developed by Alibaba Cloud / Qwen Team.

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