Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 3.1 Nemotron Instruct 70B | Solar Pro 4 |
|---|---|---|
| Input ($/M tokens) | $1.2 | $0.3 |
| Output ($/M tokens) | $1.2 | $1.2 |
Verdict. Solar Pro 4 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, Solar Pro 4 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Solar Pro 4 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). Solar Pro 4 leads on Coding Index (52.7), Intelligence Index (41.6).
Speed. On throughput, Llama 3.1 Nemotron Instruct 70B generates tokens at 108 tok/s versus 47 tok/s — about 57% faster. On time-to-first-token, Solar Pro 4 responds in 2530ms vs 3330ms, which matters most for chat-style UIs.
Provider. NVIDIA and Upstage sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while Upstage 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); Solar Pro 4 costs $27.00 ($324/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 3.1 Nemotron Instruct 70B ≈ $8.40/run, Solar Pro 4 ≈ $3.90/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 3.1 Nemotron Instruct 70B ≈ $360/run, Solar Pro 4 ≈ $180/run. Solar Pro 4 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.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Solar Pro 4 is cheaper overall. Its blended price (3:1 input/output ratio) is $0.53/M tokens vs $1.20/M for Llama 3.1 Nemotron Instruct 70B.
Solar Pro 4 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.
Llama 3.1 Nemotron Instruct 70B generates tokens faster at 108 tok/s vs 47 tok/s. However, Solar Pro 4 has lower time-to-first-token (2.53s vs 3.33s).
Choose based on your priorities: Solar Pro 4 for lower cost, Solar Pro 4 for stronger benchmark performance, and Llama 3.1 Nemotron Instruct 70B for faster generation. For latency-sensitive apps, check the TTFT comparison above.