Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 Next 80B A3B (Reasoning) | GPT-5.6 Luna (low) |
|---|---|---|
| Input ($/M tokens) | $0.15 | $0.2 |
| Output ($/M tokens) | $1.2 | $1.2 |
Verdict. GPT-5.6 Luna (low) takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, GPT-5.6 Luna (low) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). GPT-5.6 Luna (low) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 Next 80B A3B (Reasoning) is strongest on Coding Index (17.4), Intelligence Index (11.2). GPT-5.6 Luna (low) leads on Coding Index (44.2), Intelligence Index (21.5).
Speed. On throughput, Qwen3 Next 80B A3B (Reasoning) generates tokens at 197 tok/s versus 101 tok/s — about 49% faster. On time-to-first-token, GPT-5.6 Luna (low) responds in 2220ms vs 2380ms, which matters most for chat-style UIs.
Provider. Alibaba and OpenAI sell to overlapping but distinct developer audiences: Alibaba 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): Qwen3 Next 80B A3B (Reasoning) costs $22.50 ($270/year); GPT-5.6 Luna (low) costs $24.00 ($288/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 Next 80B A3B (Reasoning) ≈ $3.15/run, GPT-5.6 Luna (low) ≈ $3.40/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 Next 80B A3B (Reasoning) ≈ $150/run, GPT-5.6 Luna (low) ≈ $160/run. Qwen3 Next 80B A3B (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 Next 80B A3B (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.41/M tokens vs $0.45/M for GPT-5.6 Luna (low).
GPT-5.6 Luna (low) wins 2 out of 12 benchmarks compared to 0 for Qwen3 Next 80B A3B (Reasoning). See the detailed benchmark chart above for per-category results.
Qwen3 Next 80B A3B (Reasoning) generates tokens faster at 197 tok/s vs 101 tok/s. However, GPT-5.6 Luna (low) has lower time-to-first-token (2.22s vs 2.38s).
Choose based on your priorities: Qwen3 Next 80B A3B (Reasoning) for lower cost, GPT-5.6 Luna (low) for stronger benchmark performance, and Qwen3 Next 80B A3B (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.