Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | NVIDIA Nemotron Nano 12B v2 VL (Reasoning) | GPT-4o mini |
|---|---|---|
| Input ($/M tokens) | $0.2 | $0.15 |
| Output ($/M tokens) | $0.6 | $0.6 |
Verdict. NVIDIA Nemotron Nano 12B v2 VL (Reasoning) and GPT-4o mini split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GPT-4o mini is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GPT-4o mini makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. NVIDIA Nemotron Nano 12B v2 VL (Reasoning) is strongest on Intelligence Index (8.8). GPT-4o mini leads on Coding Index (11.4), Intelligence Index (6.7).
Speed. Throughput is comparable — 120 tok/s vs 101 tok/s — so generation speed shouldn't drive your choice here. Look at the per-benchmark wins instead.
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): NVIDIA Nemotron Nano 12B v2 VL (Reasoning) costs $15.00 ($180/year); GPT-4o mini costs $13.50 ($162/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): NVIDIA Nemotron Nano 12B v2 VL (Reasoning) ≈ $2.20/run, GPT-4o mini ≈ $1.95/run. At agent/realtime scale (200M input / 100M output per million requests): NVIDIA Nemotron Nano 12B v2 VL (Reasoning) ≈ $100/run, GPT-4o mini ≈ $90/run. GPT-4o mini 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
GPT-4o mini is cheaper overall. Its blended price (3:1 input/output ratio) is $0.26/M tokens vs $0.30/M for NVIDIA Nemotron Nano 12B v2 VL (Reasoning).
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
NVIDIA Nemotron Nano 12B v2 VL (Reasoning) generates tokens faster at 120 tok/s vs 101 tok/s. However, GPT-4o mini has lower time-to-first-token (1.03s vs 3.37s).
Choose based on your priorities: GPT-4o mini for lower cost, both perform similarly on benchmarks, and NVIDIA Nemotron Nano 12B v2 VL (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.