GPT-5.6 Luna vs. Claude Sonnet 5: Quality per Dollar
GPT-5.6 Luna vs. Claude Sonnet 5: Quality per Dollar
We ran GPT-5.6 Luna and Claude Sonnet 5 through the same three prompts on Sontairo's live production chat path. The sample covered an algorithm task, a numerical programming task, and a database-concurrency explanation.
The result
Luna matched Sonnet's correctness in all three checks, returned answers faster, and used substantially less budget.
| Metric | GPT-5.6 Luna | Claude Sonnet 5 | Luna advantage |
|---|---|---|---|
| Correct responses | 3/3 | 3/3 | Matched quality |
| Average end-to-end response | 3.94s | 6.83s | 42% faster |
| Average time to first token | 0.43s | 2.24s | 81% faster |
| Average throughput | 83.7 tokens/s | 40.0 tokens/s | 2.1× higher |
| Average cost per task | $0.000233 | $0.00632 | About 27× lower |
Task-by-task speed
| Task | GPT-5.6 Luna | Claude Sonnet 5 |
|---|---|---|
| Binary search | 3.63s | 5.61s |
| Fibonacci implementation | 3.84s | 6.93s |
| Database locking explanation | 4.35s | 7.94s |
Why Luna is the cost-effective choice
Quality per dollar matters more than price alone. In this sample, Luna did not trade away correctness to reach the lower cost: it passed the same checks as Sonnet, began responding much sooner, and completed each task faster. That combination makes Luna a strong default for everyday coding, analysis, and operational work where teams need high-quality output without premium-model overhead.
Luna is served through Sontairo's OpenRouter path, so model routing, metering, and usage remain consistent with the rest of the chat experience.
Try GPT-5.6 Luna in a new chat
Methodology note
This was a small, three-prompt live production sample, not a universal model ranking. Costs reflect the actual outputs generated for these tasks, and latency, token usage, and answer quality will vary by prompt, workload, provider conditions, and evaluation method. We will keep expanding the benchmark set as Luna sees more real-world use.