Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | DeepSeek V3.2 Exp (Non-reasoning) | Llama Nemotron Super 49B v1.5 (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.28 | $0.4 |
| Output ($/M tokens) | $0.42 | $0.4 |
Verdict. DeepSeek V3.2 Exp (Non-reasoning) wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, Llama Nemotron Super 49B v1.5 (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Llama Nemotron Super 49B v1.5 (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. DeepSeek V3.2 Exp (Non-reasoning) is strongest on Intelligence Index (21.7). Llama Nemotron Super 49B v1.5 (Non-reasoning) leads on Intelligence Index (8.5).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. DeepSeek and NVIDIA sell to overlapping but distinct developer audiences: DeepSeek tends to ship frontier reasoning models with premium positioning, while NVIDIA 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): DeepSeek V3.2 Exp (Non-reasoning) costs $14.70 ($176/year); Llama Nemotron Super 49B v1.5 (Non-reasoning) costs $18.00 ($216/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): DeepSeek V3.2 Exp (Non-reasoning) ≈ $2.24/run, Llama Nemotron Super 49B v1.5 (Non-reasoning) ≈ $2.80/run. At agent/realtime scale (200M input / 100M output per million requests): DeepSeek V3.2 Exp (Non-reasoning) ≈ $98/run, Llama Nemotron Super 49B v1.5 (Non-reasoning) ≈ $120/run. DeepSeek V3.2 Exp (Non-reasoning) 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
DeepSeek V3.2 Exp (Non-reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.32/M tokens vs $0.40/M for Llama Nemotron Super 49B v1.5 (Non-reasoning).
DeepSeek V3.2 Exp (Non-reasoning) wins 1 out of 12 benchmarks compared to 0 for Llama Nemotron Super 49B v1.5 (Non-reasoning). See the detailed benchmark chart above for per-category results.
Llama Nemotron Super 49B v1.5 (Non-reasoning) generates tokens faster at 90 tok/s vs — tok/s. However, Llama Nemotron Super 49B v1.5 (Non-reasoning) has lower time-to-first-token (6.92s vs —s).
Choose based on your priorities: DeepSeek V3.2 Exp (Non-reasoning) for lower cost, DeepSeek V3.2 Exp (Non-reasoning) for stronger benchmark performance, and Llama Nemotron Super 49B v1.5 (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.