Head-to-head showdown: NVIDIA Llama 3.1 Nemotron 70B ($0.35 in / $0.70 out per 1M) vs GPT-5.4 Workhorse ($2.50 in / $15.00 out per 1M). NVIDIA Llama 3.1 Nemotron 70B is 16.7× cheaper across standard token mixes, with 128K vs 256K context windows.
nvidia/llama-3.1-nemotron-70b-instruct · nvidia
gpt-5.4 · openai
Benchmark
| Workload Scenario | NVIDIA Llama 3.1 Nemotron 70B | GPT-5.4 Workhorse | Price Delta |
|---|---|---|---|
| 1M input tokens (raw text) | $0.35 | $2.50 | −$2.15 |
| 1M output tokens (generation) | $0.70 | $15.00 | −$14.30 |
| 1M tokens · 70% input / 30% output mix | $0.455 | $6.25 | −$5.795 |
| Standard chat turn (4K in / 800 out, 50% cached) | $0.00196 | $0.0175 | −$0.0155 |
| Monthly scale (10K requests / day) | $588.00 | $5,250.00 | −$4,662.00 |
Negative difference = NVIDIA Llama 3.1 Nemotron 70B is cheaper. Positive = GPT-5.4 Workhorse 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-5.4 Workhorse: 256K) may justify the premium for your task.
FAQ
On input, NVIDIA Llama 3.1 Nemotron 70B is cheaper ($0.35/M vs $2.50/M). On output, NVIDIA Llama 3.1 Nemotron 70B is cheaper ($0.70/M vs $15.00/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 $0.0175 on GPT-5.4 Workhorse — NVIDIA Llama 3.1 Nemotron 70B is 8.9× cheaper for that workload.
NVIDIA Llama 3.1 Nemotron 70B supports 128,000 tokens (8.2K max output); GPT-5.4 Workhorse supports 256,000 (32.8K max output). GPT-5.4 Workhorse fits 2.0× more context, which matters for long documents and agents.