Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 VL 32B (Reasoning) | NVIDIA Nemotron Nano 12B v2 VL (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.16 | $0.2 |
| Output ($/M tokens) | $0.64 | $0.6 |
Verdict. Qwen3 VL 32B (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 1.1× the per-million-token cost, NVIDIA Nemotron Nano 12B v2 VL (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). NVIDIA Nemotron Nano 12B v2 VL (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 VL 32B (Reasoning) is strongest on Intelligence Index (18.1). NVIDIA Nemotron Nano 12B v2 VL (Reasoning) leads on Intelligence Index (8.8).
Speed. On throughput, NVIDIA Nemotron Nano 12B v2 VL (Reasoning) generates tokens at 120 tok/s versus 90 tok/s — about 25% faster. On time-to-first-token, Qwen3 VL 32B (Reasoning) responds in 2680ms vs 3370ms, which matters most for chat-style UIs.
Provider. Alibaba and NVIDIA sell to overlapping but distinct developer audiences: Alibaba 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): Qwen3 VL 32B (Reasoning) costs $14.40 ($173/year); NVIDIA Nemotron Nano 12B v2 VL (Reasoning) costs $15.00 ($180/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 VL 32B (Reasoning) ≈ $2.08/run, NVIDIA Nemotron Nano 12B v2 VL (Reasoning) ≈ $2.20/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 VL 32B (Reasoning) ≈ $96/run, NVIDIA Nemotron Nano 12B v2 VL (Reasoning) ≈ $100/run. Qwen3 VL 32B (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
Qwen3 VL 32B (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.28/M tokens vs $0.30/M for NVIDIA Nemotron Nano 12B v2 VL (Reasoning).
Qwen3 VL 32B (Reasoning) wins 1 out of 12 benchmarks compared to 0 for NVIDIA Nemotron Nano 12B v2 VL (Reasoning). 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 90 tok/s. Qwen3 VL 32B (Reasoning) also has lower time-to-first-token (2.68s vs 3.37s).
Choose based on your priorities: Qwen3 VL 32B (Reasoning) for lower cost, Qwen3 VL 32B (Reasoning) for stronger benchmark performance, and NVIDIA Nemotron Nano 12B v2 VL (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.