Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GPT-5.6 Luna (xhigh) | Qwen3 Next 80B A3B Instruct |
|---|---|---|
| Input ($/M tokens) | $0.2 | $0.15 |
| Output ($/M tokens) | $1.2 | $1.2 |
Verdict. GPT-5.6 Luna (xhigh) 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, Qwen3 Next 80B A3B Instruct is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3 Next 80B A3B Instruct makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GPT-5.6 Luna (xhigh) is strongest on Coding Index (68.6), Intelligence Index (50.1). Qwen3 Next 80B A3B Instruct leads on Intelligence Index (13.8).
Speed. Throughput is comparable — 146 tok/s vs 183 tok/s — so generation speed shouldn't drive your choice here. Look at the per-benchmark wins instead.
Provider. OpenAI and Alibaba sell to overlapping but distinct developer audiences: OpenAI 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): GPT-5.6 Luna (xhigh) costs $24.00 ($288/year); Qwen3 Next 80B A3B Instruct costs $22.50 ($270/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GPT-5.6 Luna (xhigh) ≈ $3.40/run, Qwen3 Next 80B A3B Instruct ≈ $3.15/run. At agent/realtime scale (200M input / 100M output per million requests): GPT-5.6 Luna (xhigh) ≈ $160/run, Qwen3 Next 80B A3B Instruct ≈ $150/run. Qwen3 Next 80B A3B Instruct 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 Instruct 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 (xhigh).
GPT-5.6 Luna (xhigh) wins 2 out of 12 benchmarks compared to 0 for Qwen3 Next 80B A3B Instruct. See the detailed benchmark chart above for per-category results.
Qwen3 Next 80B A3B Instruct generates tokens faster at 183 tok/s vs 146 tok/s. However, Qwen3 Next 80B A3B Instruct has lower time-to-first-token (2.19s vs 43.84s).
Choose based on your priorities: Qwen3 Next 80B A3B Instruct for lower cost, GPT-5.6 Luna (xhigh) for stronger benchmark performance, and Qwen3 Next 80B A3B Instruct for faster generation. For latency-sensitive apps, check the TTFT comparison above.