Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.1 Nemotron Instruct 70B | GPT-5.6 Luna (low) |
|---|---|---|
| Input ($/M tokens) | $1.2 | $0.2 |
| Output ($/M tokens) | $1.2 | $1.2 |
Verdict. GPT-5.6 Luna (low) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GPT-5.6 Luna (low) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GPT-5.6 Luna (low) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 3.1 Nemotron Instruct 70B is strongest on Intelligence Index (7.4). GPT-5.6 Luna (low) leads on Coding Index (44.2), Intelligence Index (33.9).
Speed. On throughput, GPT-5.6 Luna (low) generates tokens at 138 tok/s versus 108 tok/s — about 21% faster. On time-to-first-token, GPT-5.6 Luna (low) responds in 1740ms vs 3330ms, which matters most for chat-style UIs.
Provider. NVIDIA and OpenAI sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while OpenAI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Llama 3.1 Nemotron Instruct 70B costs $54.00 ($648/year); GPT-5.6 Luna (low) costs $24.00 ($288/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.1 Nemotron Instruct 70B ≈ $8.40/run, GPT-5.6 Luna (low) ≈ $3.40/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.1 Nemotron Instruct 70B ≈ $360/run, GPT-5.6 Luna (low) ≈ $160/run. GPT-5.6 Luna (low) becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Data from Artificial Analysis API — 12 benchmarks
GPT-5.6 Luna (low) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.45/M tokens vs $1.20/M for Llama 3.1 Nemotron Instruct 70B.
GPT-5.6 Luna (low) wins 2 out of 12 benchmarks compared to 0 for Llama 3.1 Nemotron Instruct 70B. See the detailed benchmark chart above for per-category results.
GPT-5.6 Luna (low) generates tokens faster at 138 tok/s vs 108 tok/s. However, GPT-5.6 Luna (low) has lower time-to-first-token (1.74s vs 3.33s).
Choose based on your priorities: GPT-5.6 Luna (low) for lower cost, GPT-5.6 Luna (low) for stronger benchmark performance, and GPT-5.6 Luna (low) for faster generation. For latency-sensitive apps, check the TTFT comparison above.