Directory · Model comparison
GPT-5.6 Luna vs Qwen3.6-Flash
Side-by-side API economics and benchmarks. Per-1M input and output rates, context window, and verified metrics.
Tale of the tape
Head-to-head specs pulled from each model profile.
| Metric | GPT-5.6 Luna | Qwen3.6-Flash |
|---|---|---|
| Input cost / 1M | $0.20 | $0.25 |
| Output cost / 1M | $1.20 | $1.50 |
| Context window | 1,050,000 tokens | 1,000,000 tokens |
| MMLU score | — | — |
Cost verdict
Enter monthly input and output volumes to see total cost savings.
GPT-5.6 Luna
- Input: 1M × $0.20 = $0.20
- Output: 1M × $1.20 = $1.20
- Total
- $1.40
Qwen3.6-Flash
- Input: 1M × $0.25 = $0.25
- Output: 1M × $1.50 = $1.50
- Total
- $1.75
Total cost savings
$0.35
GPT-5.6 Luna saves $0.35 per month vs the other model at this volume.
Model routing savings
Uses this page's verified rates. Qwen3.6-Flash is the premium route (higher combined per-1M cost); GPT-5.6 Luna handles routine traffic.
All premium
Qwen3.6-Flash
$87.50
100% of input and output on the premium route
Routed stack
80% → GPT-5.6 Luna
20% → Qwen3.6-Flash
$73.50
- Routine slice
- $56.00
- Premium slice
- $17.50
Monthly savings
$14.00
16% lower
Rates pulled from this page's model database entries (qwen3-6-flash, gpt-5-6-luna) — same per-1M input/output figures as Tale of the tape. Production routing needs quality gates and monitoring.
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