AD
Track all your projects in one dashboard. Get 📊stats, 🔥heatmaps and 👀recordings in one self-hosted dashboard.
uxwizz.com
#300

Nemotron 3.5 Super VL

NVIDIA Release: 2026-10-15 Tested on: 2026-10-16 00:19 nvidia/nemotron-3.5-super-vl::none
120B total (12B active)MoEOpen source
(high) (medium) (none)

Summary

Nemotron 3.5 Super VL scores 5.5 on AI BENCHY and ranks #300. It has 9.7 reliability, a 43.5% pass rate, $0.040 total cost, and 46.97s average response time.

What makes Nemotron 3.5 Super VL unique: It stands out most in Domain specific, where it ranks #1, while Coding is its weakest area at #13.

Model facts

Researched on 2026-10-15

Reported
Parameters
120B total (12B active)
Architecture
MoE
Availability
Open source
License
OpenMDW-1.1

Hybrid Mamba-2/Transformer MoE with a C-RADIO vision encoder. NVIDIA publishes weights, implementation, training data and recipes under the permissive OpenMDW-1.1 license. The model card documents reasoning off, medium and full; this route is the paid exact ID, not the free alias.

Score

5.5

Consistency

8.7

Total Output Tokens

3,892

Total Input Tokens

251,253

Input Price

$0.150 / 1M

Output Price

$0.500 / 1M

Cache Read Price

$0.020 / 1M

Cache Write Price

N/A

Cache prices apply to input tokens. Reads reuse cached prompts; writes store them and can cost extra. Output tokens use the output price.

Tests Correct

Wrong Tests: 15

Attempt pass rate: 43.5%

Flaky tests

4

Flaky tests had mixed outcomes across runs (at least one pass and one fail).

Response Time (avg)

46.97s

Response Time (max): 872.07s

Response Time (total): 1080.29s

Hamster playing table tennis

Prompt: Create a detailed SVG illustration of a hamster playing table tennis.

#300 Nemotron 3.5 Super VL

none
Cost
$0.001
Time
3.9s
Tokens
1,061 tok

Charts

Choose the first model, then click a second model to open a side-by-side page.

Total Output Tokens

Score vs Total Output Tokens

Quick Compare

Category Breakdown

Category Score Consistency Tests Correct
Agentic 5.0 10.0
Anti-AI Tricks 6.5 10.0
Coding 3.6 7.0
Combined 3.0 10.0
Data parsing and extraction 10.0 10.0
Domain specific 5.9 7.2
General Intelligence 5.1 10.0
Instructions following 10.0 10.0
Puzzle Solving 4.6 5.6
Tool Calling 10.0 10.0
Trivia 3.0 10.0

Compared models