Nvidia Nemotron 3.5 Super VL 120B A12B vs Qwen3 Next 80B A3B Instruct
As of November 12, 2026, Qwen3 Next 80B A3B Instruct and Nvidia Nemotron 3.5 Super VL 120B A12B cost the same per input token ($0.15 per 1M), but Qwen3 Next 80B A3B Instruct costs 140% more per output token ($1.20 vs $0.50) and has a 256K-token context window versus 1M.
Nvidia Nemotron 3.5 Super VL 120B A12B vs Qwen3 Next 80B A3B Instruct side by side
| Nvidia Nemotron 3.5 Super VL 120B A12B | Qwen3 Next 80B A3B Instruct | |
|---|---|---|
| Input, per 1M tokens | $0.15 | $0.15 |
| Output, per 1M tokens | $0.50 | $1.20 |
| Cache read, per 1M tokens | $0.02 | – |
| Cache write, per 1M tokens | – | – |
| Context window | 1,049,000 tokens | 262,144 tokens |
| Max output | 943,718 tokens | 65,536 tokens |
| Image input | Yes | – |
| Tool calling | Yes | Yes |
| Structured output (JSON) | Yes | Yes |
| Prompt caching | Yes | – |
| Reasoning | Yes | Yes |
| Providers on LiteLLM | 3 | 11 |
| Price shown is from | Weights & Biases | Alibaba Cloud Model Studio |
| Added to LiteLLM | October 16, 2026 | September 2025 |
Cheapest provider for each
- 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) - Qwen3 Next 80B A3B Instruct: DeepInfra at $0.09 input and $1.10 output per 1M tokens (
deepinfra/Qwen/Qwen3-Next-80B-A3B-Instruct)
When to pick which
Pick Nvidia Nemotron 3.5 Super VL 120B A12B for
- Costs 58% less per output token: $0.50 vs $1.20 per 1M.
- Larger context window: 1,049,000 vs 262,144 tokens.
Pick Qwen3 Next 80B A3B Instruct for
- Available from 11 providers through LiteLLM, vs 3.
From differences in the data above only; where the data is equal, no reason is given.
Call Nvidia Nemotron 3.5 Super VL 120B A12B or Qwen3 Next 80B A3B Instruct 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!"}]
# Nvidia Nemotron 3.5 Super VL 120B A12B
response = completion(model="wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B", messages=messages)
# Qwen3 Next 80B A3B Instruct
response = completion(model="dashscope/qwen3-next-80b-a3b-instruct", messages=messages)
# Nvidia Nemotron 3.5 Super VL 120B A12B, falling back to Qwen3 Next 80B A3B Instruct if the call fails
response = completion(model="wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B", messages=messages, fallbacks=["dashscope/qwen3-next-80b-a3b-instruct"])FAQ
Which is cheaper, Nvidia Nemotron 3.5 Super VL 120B A12B or Qwen3 Next 80B A3B Instruct?
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 $0.4125 ($0.15 and $1.20) for Qwen3 Next 80B A3B Instruct, as of November 12, 2026.
Which has the larger context window, Nvidia Nemotron 3.5 Super VL 120B A12B or Qwen3 Next 80B A3B Instruct?
Nvidia Nemotron 3.5 Super VL 120B A12B has the larger context window: 1,049,000 tokens, versus 262,144 for Qwen3 Next 80B A3B Instruct.
Can I switch from Nvidia Nemotron 3.5 Super VL 120B A12B to Qwen3 Next 80B A3B Instruct without changing my code?
Yes. Through LiteLLM both use the OpenAI format, so switching means changing the model name from "wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B" to "dashscope/qwen3-next-80b-a3b-instruct", or pointing a LiteLLM gateway model alias at the other model. You can also use one as a fallback for the other.