Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | NVIDIA Nemotron Nano 9B V2 (Non-reasoning) | Qwen2.5 Turbo |
|---|---|---|
| Input ($/M tokens) | $0.05 | $0.05 |
| Output ($/M tokens) | $0.2 | $0.2 |
Verdict. NVIDIA Nemotron Nano 9B V2 (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, Qwen2.5 Turbo is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen2.5 Turbo makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. NVIDIA Nemotron Nano 9B V2 (Non-reasoning) is strongest on Intelligence Index (7.2). Qwen2.5 Turbo leads on Intelligence Index (6.0).
Speed. On throughput, NVIDIA Nemotron Nano 9B V2 (Non-reasoning) generates tokens at 168 tok/s versus 108 tok/s — about 36% faster. On time-to-first-token, NVIDIA Nemotron Nano 9B V2 (Non-reasoning) responds in 1350ms vs 2160ms, which matters most for chat-style UIs.
Provider. NVIDIA and Alibaba sell to overlapping but distinct developer audiences: NVIDIA tends to ship frontier reasoning models with premium positioning, while Alibaba 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 9B V2 (Non-reasoning) costs $4.50 ($54/year); Qwen2.5 Turbo costs $4.50 ($54/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): NVIDIA Nemotron Nano 9B V2 (Non-reasoning) ≈ $0.65/run, Qwen2.5 Turbo ≈ $0.65/run. At agent/realtime scale (200M input / 100M output per million requests): NVIDIA Nemotron Nano 9B V2 (Non-reasoning) ≈ $30/run, Qwen2.5 Turbo ≈ $30/run.
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
Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.
NVIDIA Nemotron Nano 9B V2 (Non-reasoning) wins 1 out of 12 benchmarks compared to 0 for Qwen2.5 Turbo. See the detailed benchmark chart above for per-category results.
NVIDIA Nemotron Nano 9B V2 (Non-reasoning) generates tokens faster at 168 tok/s vs 108 tok/s. NVIDIA Nemotron Nano 9B V2 (Non-reasoning) also has lower time-to-first-token (1.35s vs 2.16s).
Choose based on your priorities: both are similarly priced, NVIDIA Nemotron Nano 9B V2 (Non-reasoning) for stronger benchmark performance, and NVIDIA Nemotron Nano 9B V2 (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.