For agent and vision work, Nemotron 3.5 Super VL is the bigger, agent-focused pick, while Gemma 4 is the smaller model that's easier to run on modest hardware. That's the honest answer to Nemotron 3.5 Super VL vs Gemma 4. NVIDIA says Super VL leads Gemma-4-31B-It on a set of Indic language benchmarks, and in my video I showed NVIDIA's comparison chart against Gemma 4. Beyond that, the right choice comes down to your hardware, your task and your language.
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Gemma 4 is Google's open model family. The comparison NVIDIA published uses Gemma-4-31B-It specifically. I'm not going to invent Gemma specs or scores here. You'll get exactly what NVIDIA said, what Super VL actually is, and a simple way to pick between them for your own work.
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The verdict for agent and vision work
Here's the plain version. Super VL is NVIDIA's open multimodal model built for agents. It has 120B parameters in total, with 12B active per token. It reads text, images, documents and video, and it combines reasoning, instruction following and tool use in one model.
Gemma 4 31B is a smaller model from Google's open family. Smaller usually means easier to run, cheaper to host and friendlier to normal hardware. That's a real advantage, and it's why plenty of people will reach for Gemma first.
So the trade-off looks like this:
- Pick Super VL if you're building agents that call tools, run long loops, or start from screenshots, PDFs and charts, and you can either use it hosted or have serious memory.
- Pick Gemma 4 if you need something lighter that runs on smaller machines, and your jobs don't need a big agent-focused model.
For everything about the NVIDIA model on its own, start with my Nemotron 3.5 Super VL overview.
Nemotron 3.5 Super VL vs Gemma 4: comparison table
This table only uses what NVIDIA has published. Where NVIDIA didn't say something about Gemma 4, I've written "Not stated by NVIDIA" instead of guessing.
| Category | Nemotron 3.5 Super VL | Gemma 4 31B |
|---|---|---|
| Maker | NVIDIA | Google (open model family) |
| Model in NVIDIA's comparison | Nemotron 3.5 Super VL | Gemma-4-31B-It |
| Parameters | 120B total, 12B active per token (mixture-of-experts) | Not stated by NVIDIA |
| Architecture | Hybrid Mamba + attention, multi-token prediction | Not stated by NVIDIA |
| Inputs | Text, images, documents, video (sampled frames) | Not stated by NVIDIA |
| Context | Up to 1M tokens (depends on memory and runtime) | Not stated by NVIDIA |
| Indic benchmarks (MILU 1.5, Indic GPQA, Indic AIME, Indic FrontierMath, Indic MBPP) | Leads, per NVIDIA (no numbers given in text) | Behind Super VL, per NVIDIA |
| License | OpenMDW License v1.1 (open weights) | Not stated by NVIDIA |
| Local memory needed | Roughly 70-90GB | Not stated by NVIDIA |
Notice what's missing. NVIDIA's text doesn't give scores for the Indic benchmarks, just that Super VL leads. And it doesn't compare the two on agent benchmarks like Hermes Bench. So the published head-to-head is narrow: Indic language tasks, plus the chart I showed in the video.
For the full set of numbers NVIDIA released, including Hermes Bench, LangChain, CharXiv and OCRBench, see the Nemotron 3.5 Super VL benchmark breakdown.
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What the Indic benchmark lead actually tells you
NVIDIA says Super VL leads Gemma-4-31B-It across five Indic benchmarks: MILU 1.5, Indic GPQA, Indic AIME, Indic FrontierMath and Indic MBPP. Those cover general knowledge, hard science questions, maths and coding, in Indian languages.
If you work in Hindi or other Indian languages, that's useful. It's a signal that Super VL handles reasoning and code in those languages well. NVIDIA also says it leads Sarvam-105B on the same set.
If you work only in English, this result tells you less. It doesn't say anything direct about English agent tasks against Gemma. Don't stretch it further than it goes.
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How to choose
Most people pick a model by whichever one is trending this week. That's the old way. The new way is to match the model to three things: your hardware, your task and your language.
1. Hardware
Super VL is not a normal-laptop model. Running it locally needs about 70 to 90GB of memory. In LM Studio, the smallest GGUF is Q4_K_M at about 70GB, plus extra for images and context. Think a 128GB DGX Spark or a big-memory workstation.
If you don't have that, use Super VL hosted. It's on OpenRouter (including a free variant), and I use it free inside OpenCode by picking Nemotron 3.5 Super VL from the model drop-down. You get the big model without the big machine.
If you need something that runs on your own smaller hardware, a 31B model like Gemma 4 is the more realistic local option. NVIDIA also released smaller Nemotron 3.5 Bolt models for 24GB and 4GB systems, if you want to stay in the Nemotron family on smaller kit.
2. Task
Ask what your job actually looks like.
- Agent loops with tools: Super VL was built for this. It mixes reasoning, instruction following and tool use, and it has thinking and non-thinking modes. NVIDIA reports up to 5x higher single-GPU throughput on agentic tasks versus open models its size.
- Jobs that start with a picture or a file: Super VL reads screenshots, PDFs, charts and receipts. That's where I slot it in.
- Light, quick tasks on small hardware: a smaller model like Gemma 4 can make more sense here simply because it's easier to run.
When I tested Super VL in OpenCode, I asked it to build a beautiful to-do list app and make it entertaining and engaging. It came out working. It's not a frontier model, and it's not going to replace Opus 5.5, but it's free, open and fast.
3. Language
If your work involves Indian languages, NVIDIA's numbers point to Super VL. That's the one area where they directly published a lead over Gemma-4-31B-It. For other languages, you'll need your own testing.
Not sure which way to go for your business? Book a free AI strategy session and we'll map it out.
Test both on your own jobs
Every comparison number on this page is NVIDIA's. There's no independent replication of the Gemma head-to-head yet, and companies tend to publish the tests they win. So the only result that really counts is the one you get on your own work.
Here's a simple test plan:
- Pick five to ten real tasks you run every week.
- Include at least one that starts with an image or a PDF, and at least one that needs a tool call.
- If language matters to you, include tasks in that language.
- Run each task on both models with the same prompt.
- Score each run: did it finish, was it right, and how long did it take.
That's the same approach behind Goldie Bench: real jobs, scored plainly, no chart-reading required.
To try Super VL locally through Ollama, the library name is nemotron-3.5-super. Run it with: ollama run nemotron-3.5-super. You can also launch it inside an agent harness, for example: ollama launch hermes --model nemotron-3.5-super or ollama launch opencode --model nemotron-3.5-super.
And if you're also weighing it against NVIDIA's own bigger model, read Nemotron 3.5 Super VL vs Nemotron 3 Ultra.
Use both, not just one
You don't have to marry one model. Inside my Agent OS, I run Hermes, OpenClaw, OpenCode and Claude side by side, and I swap open models in and out depending on the job. A big agent model for heavy tasks, a lighter one for quick stuff. If I run out of tokens in OpenCode, I switch to another free model there or use Super VL in Hermes Agent.
The Agent OS guide shows how that setup fits together. If you want the Agent OS zip, the video tutorial, a 30-day roadmap, four weekly coaching calls and daily tutorials, join the AI Profit Boardroom for $69/mo. More than 3,400 members are already in there.
FAQ
Is Nemotron 3.5 Super VL better than Gemma 4?
On Indic benchmarks, yes, per NVIDIA: Super VL leads Gemma-4-31B-It on MILU 1.5, Indic GPQA, Indic AIME, Indic FrontierMath and Indic MBPP. NVIDIA gives no numbers in its text, and there's no published agent-benchmark head-to-head, so test your own jobs.
Which one is easier to run locally?
Gemma 4 31B is the smaller model, so it's generally easier to run. Super VL needs roughly 70 to 90GB of memory locally, so most people should use it hosted through OpenRouter or free inside OpenCode.
Which Gemma 4 model did NVIDIA compare against?
Gemma-4-31B-It, from Google's open model family. NVIDIA didn't publish Gemma's specs in that comparison, so I haven't listed any here.
Can I use both in the same workflow?
Yes. Use Super VL for agent loops and jobs that start with a screenshot or a PDF, and a lighter model for quick tasks. If you want help setting that up, book a free AI strategy session.
