Nemotron 3.5 Super VL Open vs 
GPT-6 Luna (batch) Paid
Nemotron 3.5 Super VL vs GPT-6 Luna (batch) compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
GPT-6 Luna (batch) — full profile ›
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
$0.25 /M outputManaged API (no infra to run)
Nemotron 3.5 Super VL vs GPT-6 Luna (batch) specs
| Spec | Nemotron 3.5 Super VL | GPT-6 Luna (batch) | Winner |
|---|---|---|---|
| Maker | NVIDIA | OpenAI | – |
| Type | Open-weight | Proprietary | |
| Context window | 1M tokens | 1.1M tokens | |
| Input price | $0.15/M · free self-host | $0.05/M | |
| Output price | $0.5/M · free self-host | $0.25/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
GPT-6 Luna (batch) is ~2.0× cheaper than Nemotron 3.5 Super VL on output tokens ($0.25 vs $0.5 per M tokens).
- You want to self-host or run on your cloud
- You prioritize data privacy & control
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You need the longer 1.1M context window
- You want the lower output price
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | Nemotron 3.5 Super VL | GPT-6 Luna (batch) |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✓ | ✓ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✓ | ✓ |
Benchmarks: Nemotron 3.5 Super VL vs GPT-6 Luna (batch)
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-6 Luna (batch) score
| Criterion | Nemotron 3.5 Super VL | GPT-6 Luna (batch) |
|---|---|---|
| Cost-efficiency | 5.0 | 5.0 |
| Context window | 5.0 | 5.0 |
| 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-6 Luna (batch) Paid
GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol. It is suited for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic...
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-6 Luna (batch)?
Nemotron 3.5 Super VL is open-weight and competitive on many tasks, but GPT-6 Luna (batch) 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-6 Luna (batch) is API-only and cannot be self-hosted.
Nemotron 3.5 Super VL vs GPT-6 Luna (batch) — 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-6 Luna (batch) if you want frontier capability through a managed API with zero infrastructure to run.
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