Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GPT-4.1 nano | Llama Nemotron Super 49B v1.5 (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.1 | $0.4 |
| Output ($/M tokens) | $0.4 | $0.4 |
Verdict. GPT-4.1 nano wins the overall benchmark matchup 2–0 across 2 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. GPT-4.1 nano is strongest on Coding Index (11.1), Intelligence Index (7.8). Llama Nemotron Super 49B v1.5 (Non-reasoning) leads on Intelligence Index (7.4).
Speed. On throughput, GPT-4.1 nano generates tokens at 187 tok/s versus 144 tok/s — about 23% faster. On time-to-first-token, GPT-4.1 nano responds in 720ms vs 3020ms, which matters most for chat-style UIs.
Provider. OpenAI and NVIDIA sell to overlapping but distinct developer audiences: OpenAI 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): GPT-4.1 nano costs $9.00 ($108/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): GPT-4.1 nano ≈ $1.30/run, Llama Nemotron Super 49B v1.5 (Non-reasoning) ≈ $2.80/run. At agent/realtime scale (200M input / 100M output per million requests): GPT-4.1 nano ≈ $60/run, Llama Nemotron Super 49B v1.5 (Non-reasoning) ≈ $120/run. GPT-4.1 nano 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-4.1 nano is cheaper overall. Its blended price (3:1 input/output ratio) is $0.17/M tokens vs $0.40/M for Llama Nemotron Super 49B v1.5 (Non-reasoning).
GPT-4.1 nano wins 2 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.
GPT-4.1 nano generates tokens faster at 187 tok/s vs 144 tok/s. GPT-4.1 nano also has lower time-to-first-token (0.72s vs 3.02s).
Choose based on your priorities: GPT-4.1 nano for lower cost, GPT-4.1 nano for stronger benchmark performance, and GPT-4.1 nano for faster generation. For latency-sensitive apps, check the TTFT comparison above.