Nemotron 3.5 Super VL Open vs 
GPT-4o-mini Paid
Nemotron 3.5 Super VL vs GPT-4o-mini compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
$0.5 /M outputNo per-token fees if you self-host
$0.6 /M outputManaged API (no infra to run)
Nemotron 3.5 Super VL vs GPT-4o-mini specs
| Spec | Nemotron 3.5 Super VL | GPT-4o-mini | Winner |
|---|---|---|---|
| Maker | NVIDIA | OpenAI | – |
| Type | Open-weight | Proprietary | |
| Context window | 1M tokens | 128K tokens | |
| Input price | $0.15/M · free self-host | $0.15/M | = Tie |
| Output price | $0.5/M · free self-host | $0.6/M | |
| Vision / multimodal | Yes | Yes | = Tie |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | |
| License | NVIDIA Open Model | Proprietary |
Price gap & when to choose each
- You want to self-host or run on your cloud
- You need the longer 1M context window
- You prioritize data privacy & control
- You want the lowest operating costs
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | Nemotron 3.5 Super VL | GPT-4o-mini |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✓ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✓ | ✗ |
Benchmarks: Nemotron 3.5 Super VL vs GPT-4o-mini
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How Nemotron 3.5 Super VL and GPT-4o-mini score
| Criterion | Nemotron 3.5 Super VL | GPT-4o-mini |
|---|---|---|
| Cost-efficiency | 5.0 | 5.0 |
| Context window | 5.0 | 3.5 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 5.0 | 5.0 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
What each model is
Nemotron 3.5 Super VL Open
NVIDIA Nemotron 3.5 Super VL is an open multimodal mixture-of-experts model from NVIDIA, with 12B active parameters out of 120B total. It takes text, images, documents, GUI screenshots, and video...
GPT-4o-mini Paid
GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...
Other models in these families
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Frequently asked questions
Is Nemotron 3.5 Super VL as good as GPT-4o-mini?
Nemotron 3.5 Super VL is open-weight and competitive on many tasks, but GPT-4o-mini may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Can I run Nemotron 3.5 Super VL locally?
Yes. Nemotron 3.5 Super VL has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. GPT-4o-mini is API-only and cannot be self-hosted.
Nemotron 3.5 Super VL vs GPT-4o-mini — which should I pick in 2026?
Choose Nemotron 3.5 Super VL if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose GPT-4o-mini if you want frontier capability through a managed API with zero infrastructure to run.
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