GPT-5.6 Luna vs Nvidia Nemotron 3.5 Super VL 120B A12B
As of November 12, 2026, GPT-5.6 Luna costs 33% more per input token than Nvidia Nemotron 3.5 Super VL 120B A12B ($0.20 vs $0.15 per 1M) and has a 922K-token context window versus 1M.
GPT-5.6 Luna vs Nvidia Nemotron 3.5 Super VL 120B A12B side by side
| GPT-5.6 Luna | Nvidia Nemotron 3.5 Super VL 120B A12B | |
|---|---|---|
| Input, per 1M tokens | $0.20 | $0.15 |
| Output, per 1M tokens | $1.20 | $0.50 |
| Cache read, per 1M tokens | $0.02 | $0.02 |
| Cache write, per 1M tokens | $0.25 | – |
| Context window | 922,000 tokens | 1,049,000 tokens |
| Max output | 128,000 tokens | 943,718 tokens |
| Image input | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output (JSON) | Yes | Yes |
| Prompt caching | Yes | Yes |
| Reasoning | Yes | Yes |
| Providers on LiteLLM | 14 | 3 |
| Price shown is from | OpenAI | Weights & Biases |
| Added to LiteLLM | July 9, 2026 | October 16, 2026 |
Cheapest provider for each
- GPT-5.6 Luna: Databricks at $0.20 input and $1.20 output per 1M tokens (
databricks/databricks-gpt-5-6-luna) - Nvidia Nemotron 3.5 Super VL 120B A12B: Weights & Biases at $0.15 input and $0.50 output per 1M tokens (
wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B)
When to pick which
Pick GPT-5.6 Luna for
- Available from 14 providers through LiteLLM, vs 3.
Pick Nvidia Nemotron 3.5 Super VL 120B A12B for
- Costs 25% less per input token: $0.15 vs $0.20 per 1M.
- Larger context window: 1,049,000 vs 922,000 tokens.
From differences in the data above only; where the data is equal, no reason is given.
Call GPT-5.6 Luna or Nvidia Nemotron 3.5 Super VL 120B A12B with LiteLLM
Both use the OpenAI format through LiteLLM, so switching is a model name change. Set each provider's API key in the environment first.
from litellm import completion
messages = [{"role": "user", "content": "Hello!"}]
# GPT-5.6 Luna
response = completion(model="gpt-5.6-luna", messages=messages)
# Nvidia Nemotron 3.5 Super VL 120B A12B
response = completion(model="wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B", messages=messages)
# GPT-5.6 Luna, falling back to Nvidia Nemotron 3.5 Super VL 120B A12B if the call fails
response = completion(model="gpt-5.6-luna", messages=messages, fallbacks=["wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B"])FAQ
Which is cheaper, GPT-5.6 Luna or Nvidia Nemotron 3.5 Super VL 120B A12B?
Nvidia Nemotron 3.5 Super VL 120B A12B is cheaper at 3 input tokens per output token: $0.2375 per 1M tokens blended ($0.15 input, $0.50 output), versus $0.45 ($0.20 and $1.20) for GPT-5.6 Luna, as of November 12, 2026.
Which has the larger context window, GPT-5.6 Luna or Nvidia Nemotron 3.5 Super VL 120B A12B?
Nvidia Nemotron 3.5 Super VL 120B A12B has the larger context window: 1,049,000 tokens, versus 922,000 for GPT-5.6 Luna.
Can I switch from GPT-5.6 Luna to Nvidia Nemotron 3.5 Super VL 120B A12B without changing my code?
Yes. Through LiteLLM both use the OpenAI format, so switching means changing the model name from "gpt-5.6-luna" to "wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B", or pointing a LiteLLM gateway model alias at the other model. You can also use one as a fallback for the other.