Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 4 Scout | Qwen3 VL 32B (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.18 | $0.16 |
| Output ($/M tokens) | $0.66 | $0.64 |
Verdict. Llama 4 Scout and Qwen3 VL 32B (Reasoning) 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, Qwen3 VL 32B (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3 VL 32B (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Llama 4 Scout is strongest on Intelligence Index (10.3), Coding Index (8.2). Qwen3 VL 32B (Reasoning) leads on Intelligence Index (18.1).
Speed. On throughput, Llama 4 Scout generates tokens at 135 tok/s versus 90 tok/s — about 33% faster. On time-to-first-token, Llama 4 Scout responds in 780ms vs 2680ms, which matters most for chat-style UIs.
Provider. Meta and Alibaba sell to overlapping but distinct developer audiences: Meta 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): Llama 4 Scout costs $15.30 ($184/year); Qwen3 VL 32B (Reasoning) costs $14.40 ($173/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Llama 4 Scout ≈ $2.22/run, Qwen3 VL 32B (Reasoning) ≈ $2.08/run. At agent/realtime scale (200M input / 100M output per million requests): Llama 4 Scout ≈ $102/run, Qwen3 VL 32B (Reasoning) ≈ $96/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.
Head-to-head deltas
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 Llama 4 Scout.
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
Llama 4 Scout generates tokens faster at 135 tok/s vs 90 tok/s. Llama 4 Scout also has lower time-to-first-token (0.78s vs 2.68s).
Choose based on your priorities: Qwen3 VL 32B (Reasoning) for lower cost, both perform similarly on benchmarks, and Llama 4 Scout for faster generation. For latency-sensitive apps, check the TTFT comparison above.