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Nemotron 3.5 Super VL (120B A12B) vs Phi-4-multimodal-instruct

Nemotron 3.5 Super VL (120B A12B) leads the LLM Stats Score 35.0 to 2.5. Phi-4-multimodal-instruct is 6.4x cheaper per token.

NVIDIA · Microsoft · Updated for 2026

Which is better?

Nemotron 3.5 Super VL (120B A12B) leads the overall LLM Stats Score 35.0 to 2.5, ranking #108 overall.

On price, Phi-4-multimodal-instruct is roughly 6.4x 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 — it leads those capability indexes
  • you process long inputs — it offers a 262,144 token context window
  • you want the most recent training data — it shipped Oct 2026

Choose Phi-4-multimodal-instruct

  • cost matters — it's about 6.4x cheaper per token

At a glance

The differences that matter most.

Nemotron 3.5 Super VL (120B A12B)
NVIDIA
Phi-4-multimodal-instruct
Microsoft
Core performance indexes
LLM Stats Score
35.0
#108
2.5
#336
Reasoning
36.2
#94
-0.5
#344
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.20 / M
$0.05 / M
Output price
$1.00 / M
$0.10 / M
Context window
262,144
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
Nemotron 3.5 Super VL (120B A12B)
Phi-4-multimodal-instruct
Vision
20.3#86
1.5#191
Multimodal
22.0#76
3.5#160
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

19 reported for Nemotron 3.5 Super VL (120B A12B) · 15 for Phi-4-multimodal-instruct

No common benchmarks found

Nemotron 3.5 Super VL (120B A12B) and Phi-4-multimodal-instructdon'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

Pricing Analysis

Price comparison per million tokens

Phi-4-multimodal-instruct costs less

For input processing, Nemotron 3.5 Super VL (120B A12B) ($0.20/1M tokens) is 4.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).

For output processing, Nemotron 3.5 Super VL (120B A12B) ($1.00/1M tokens) is 10.0x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).

In conclusion, Nemotron 3.5 Super VL (120B A12B) is more expensive than Phi-4-multimodal-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
Microsoft
Phi-4-multimodal-instruct
Input tokens$0.05
Output tokens$0.10
Best providerDeepinfra
Notice missing or incorrect data?

Model Size

Parameter count comparison

114.4B diff

Nemotron 3.5 Super VL (120B A12B) has 114.4B more parameters than Phi-4-multimodal-instruct, making it 2042.9% larger.

NVIDIA
Nemotron 3.5 Super VL (120B A12B)
120.0Bparameters
Microsoft
Phi-4-multimodal-instruct
5.6Bparameters
120.0B
Nemotron 3.5 Super VL (120B A12B)
5.6B
Phi-4-multimodal-instruct

Context Window

Maximum input and output token capacity

Nemotron 3.5 Super VL (120B A12B) accepts 262,144 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. Nemotron 3.5 Super VL (120B A12B) can generate longer responses up to 262,144 tokens, while Phi-4-multimodal-instruct is limited to 128,000 tokens.

NVIDIA
Nemotron 3.5 Super VL (120B A12B)
Input262,144 tokens
Output262,144 tokens
Microsoft
Phi-4-multimodal-instruct
Input128,000 tokens
Output128,000 tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Nemotron 3.5 Super VL (120B A12B) and Phi-4-multimodal-instruct support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Nemotron 3.5 Super VL (120B A12B)

Text
Images
Audio
Video

Phi-4-multimodal-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 Phi-4-multimodal-instruct uses MIT.

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

Phi-4-multimodal-instruct

MIT

Open weights

Release Timeline

When each model was launched

Nemotron 3.5 Super VL (120B A12B) was released on 2026-10-15, while Phi-4-multimodal-instruct was released on 2025-02-01.

Nemotron 3.5 Super VL (120B A12B) is 21 months newer than Phi-4-multimodal-instruct.

Nemotron 3.5 Super VL (120B A12B)

Oct 15, 2026

5 days ago

1.7yr newer
Phi-4-multimodal-instruct

Feb 1, 2025

1.7 years ago

Knowledge Cutoff

When training data ends

Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while Nemotron 3.5 Super VL (120B A12B)'s cutoff date is not specified.

We can confirm Phi-4-multimodal-instruct's training data extends to 2024-06-01, but cannot make a direct comparison without Nemotron 3.5 Super VL (120B A12B)'s cutoff date.

Nemotron 3.5 Super VL (120B A12B)

—

Phi-4-multimodal-instruct

Jun 2024

Provider Availability

Nemotron 3.5 Super VL (120B A12B) is available from DeepInfra. Phi-4-multimodal-instruct is available from DeepInfra.

Nemotron 3.5 Super VL (120B A12B)

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

Phi-4-multimodal-instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.05/1MOutput Price:Output: $0.10/1M
* 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 Phi-4-multimodal-instruct side-by-side, then vote on the output you prefer.

Nemotron 3.5 Super VL (120B A12B)
✓ Preferred
Phi-4-multimodal-instruct
Open in Playground

FAQ

Common questions about Nemotron 3.5 Super VL (120B A12B) vs Phi-4-multimodal-instruct.

Which is better, Nemotron 3.5 Super VL (120B A12B) or Phi-4-multimodal-instruct?

Nemotron 3.5 Super VL (120B A12B) leads the LLM Stats Score 35.0 to 2.5. Nemotron 3.5 Super VL (120B A12B) is made by NVIDIA and Phi-4-multimodal-instruct is made by Microsoft. 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 Phi-4-multimodal-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%. Phi-4-multimodal-instruct scores ScienceQA Visual: 97.5%, DocVQA: 93.2%, MMBench: 86.7%, POPE: 85.6%, OCRBench: 84.4%.

Is Nemotron 3.5 Super VL (120B A12B) cheaper than Phi-4-multimodal-instruct?

Phi-4-multimodal-instruct is 4.0x cheaper for input tokens. Nemotron 3.5 Super VL (120B A12B) costs $0.20/M input and $1.00/M output via deepinfra. Phi-4-multimodal-instruct costs $0.05/M input and $0.10/M output via deepinfra.

What are the context window sizes for Nemotron 3.5 Super VL (120B A12B) and Phi-4-multimodal-instruct?

Nemotron 3.5 Super VL (120B A12B) supports 262K tokens and Phi-4-multimodal-instruct supports 128K 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 Phi-4-multimodal-instruct?

Key differences include LLM Stats Score (35.0 vs 2.5), context window (262K vs 128K), input pricing ($0.20 vs $0.05/M), licensing (OpenMDW License v1.1 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Nemotron 3.5 Super VL (120B A12B) and Phi-4-multimodal-instruct?

Nemotron 3.5 Super VL (120B A12B) is developed by NVIDIA and Phi-4-multimodal-instruct is developed by Microsoft.

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