DeepSeek R1 Distill Llama 70B vs Nvidia Nemotron 3.5 Super VL 120B A12B
As of November 12, 2026, DeepSeek R1 Distill Llama 70B 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 128K-token context window versus 1M.
DeepSeek R1 Distill Llama 70B vs Nvidia Nemotron 3.5 Super VL 120B A12B side by side
| DeepSeek R1 Distill Llama 70B | Nvidia Nemotron 3.5 Super VL 120B A12B | |
|---|---|---|
| Input, per 1M tokens | $0.20 | $0.15 |
| Output, per 1M tokens | $0.60 | $0.50 |
| Cache read, per 1M tokens | – | $0.02 |
| Cache write, per 1M tokens | – | – |
| Context window | 131,072 tokens | 1,049,000 tokens |
| Max output | 131,072 tokens | 943,718 tokens |
| Image input | – | Yes |
| Tool calling | Yes | Yes |
| Structured output (JSON) | Yes | Yes |
| Prompt caching | – | Yes |
| Reasoning | Yes | Yes |
| Providers on LiteLLM | 9 | 3 |
| Price shown is from | DeepInfra | Weights & Biases |
| Added to LiteLLM | February 2025 | October 16, 2026 |
Cheapest provider for each
- DeepSeek R1 Distill Llama 70B: DeepInfra at $0.20 input and $0.60 output per 1M tokens (
deepinfra/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) - 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 DeepSeek R1 Distill Llama 70B for
- Available from 9 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 131,072 tokens.
From differences in the data above only; where the data is equal, no reason is given.
Call DeepSeek R1 Distill Llama 70B 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!"}]
# DeepSeek R1 Distill Llama 70B
response = completion(model="deepinfra/deepseek-ai/DeepSeek-R1-Distill-Llama-70B", 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)
# DeepSeek R1 Distill Llama 70B, falling back to Nvidia Nemotron 3.5 Super VL 120B A12B if the call fails
response = completion(model="deepinfra/deepseek-ai/DeepSeek-R1-Distill-Llama-70B", messages=messages, fallbacks=["wandb/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B"])FAQ
Which is cheaper, DeepSeek R1 Distill Llama 70B 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 $0.30 ($0.20 and $0.60) for DeepSeek R1 Distill Llama 70B, as of November 12, 2026.
Which has the larger context window, DeepSeek R1 Distill Llama 70B 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 131,072 for DeepSeek R1 Distill Llama 70B.
Can I switch from DeepSeek R1 Distill Llama 70B 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 "deepinfra/deepseek-ai/DeepSeek-R1-Distill-Llama-70B" 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.