Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | gpt-oss-120b (high) | NVIDIA Nemotron Nano 12B v2 VL (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.15 | $0.2 |
| Output ($/M tokens) | $0.6 | $0.6 |
Verdict. gpt-oss-120b (high) 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, 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. gpt-oss-120b (high) is strongest on Coding Index (30.4), Intelligence Index (24.1). NVIDIA Nemotron Nano 12B v2 VL (Reasoning) leads on Intelligence Index (8.8).
Speed. On throughput, gpt-oss-120b (high) generates tokens at 153 tok/s versus 38 tok/s — about 75% faster. On time-to-first-token, gpt-oss-120b (high) responds in 850ms vs 10490ms, 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-oss-120b (high) costs $13.50 ($162/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): gpt-oss-120b (high) ≈ $1.95/run, NVIDIA Nemotron Nano 12B v2 VL (Reasoning) ≈ $2.20/run. At agent/realtime scale (200M input / 100M output per million requests): gpt-oss-120b (high) ≈ $90/run, NVIDIA Nemotron Nano 12B v2 VL (Reasoning) ≈ $100/run. gpt-oss-120b (high) 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-oss-120b (high) 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).
gpt-oss-120b (high) wins 2 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.
gpt-oss-120b (high) generates tokens faster at 153 tok/s vs 38 tok/s. gpt-oss-120b (high) also has lower time-to-first-token (0.85s vs 10.49s).
Choose based on your priorities: gpt-oss-120b (high) for lower cost, gpt-oss-120b (high) for stronger benchmark performance, and gpt-oss-120b (high) for faster generation. For latency-sensitive apps, check the TTFT comparison above.