Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 Next 80B A3B Instruct | 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 Instruct is strongest on Intelligence Index (13.8). GPT-5.6 Luna (low) leads on Coding Index (44.2), Intelligence Index (33.9).
Speed. On throughput, Qwen3 Next 80B A3B Instruct generates tokens at 170 tok/s versus 109 tok/s — about 36% faster. On time-to-first-token, GPT-5.6 Luna (low) responds in 1730ms vs 2190ms, 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 Instruct 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 Instruct ≈ $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 Instruct ≈ $150/run, GPT-5.6 Luna (low) ≈ $160/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 (low).
GPT-5.6 Luna (low) 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 170 tok/s vs 109 tok/s. However, GPT-5.6 Luna (low) has lower time-to-first-token (1.73s vs 2.19s).
Choose based on your priorities: Qwen3 Next 80B A3B Instruct for lower cost, GPT-5.6 Luna (low) for stronger benchmark performance, and Qwen3 Next 80B A3B Instruct for faster generation. For latency-sensitive apps, check the TTFT comparison above.