Head-to-head showdown: NVIDIA Llama 3.1 Nemotron 70B ($0.35 in / $0.70 out per 1M) vs GPT-5.6 Cyber ($12.50 in / $75.00 out per 1M). NVIDIA Llama 3.1 Nemotron 70B is 83.3× cheaper across standard token mixes, with 128K vs 1.1M context windows.
nvidia/llama-3.1-nemotron-70b-instruct · nvidia
gpt-5.6-cyber · openai
Benchmark
| Workload Scenario | NVIDIA Llama 3.1 Nemotron 70B | GPT-5.6 Cyber | Price Delta |
|---|---|---|---|
| 1M input tokens (raw text) | $0.35 | $12.50 | −$12.15 |
| 1M output tokens (generation) | $0.70 | $75.00 | −$74.30 |
| 1M tokens · 70% input / 30% output mix | $0.455 | $31.25 | −$30.795 |
| Standard chat turn (4K in / 800 out, 50% cached) | $0.00196 | $0.0875 | −$0.0855 |
| Monthly scale (10K requests / day) | $588.00 | $26,250.00 | −$25,662.00 |
Negative difference = NVIDIA Llama 3.1 Nemotron 70B is cheaper. Positive = GPT-5.6 Cyber is cheaper.
Scaling Curve
Total cost of a token volume at 70/30 input/output split (uncached). Log-log scale.
Verdict
NVIDIA Llama 3.1 Nemotron 70B is cheaper on both input and output rates, so it costs less at every input/output mix. Price alone still isn't the whole decision: capability, latency and context limits (NVIDIA Llama 3.1 Nemotron 70B: 128K, GPT-5.6 Cyber: 1.1M) may justify the premium for your task.
FAQ
On input, NVIDIA Llama 3.1 Nemotron 70B is cheaper ($0.35/M vs $12.50/M). On output, NVIDIA Llama 3.1 Nemotron 70B is cheaper ($0.70/M vs $75.00/M). The same model is cheaper on both sides, so it wins at every mix.
A chat-style request (4,000 input + 800 output tokens, 50% cached) costs $0.00196 on NVIDIA Llama 3.1 Nemotron 70B and $0.0875 on GPT-5.6 Cyber — NVIDIA Llama 3.1 Nemotron 70B is 44.6× cheaper for that workload.
NVIDIA Llama 3.1 Nemotron 70B supports 128,000 tokens (8.2K max output); GPT-5.6 Cyber supports 1,050,000 (128K max output). GPT-5.6 Cyber fits 8.2× more context, which matters for long documents and agents.