Wire flash
User test: GPT-6 Luna costs 40% less than Haiku 5.5 on summarization
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In a test of 20 podcast excerpts, GPT-6 Luna cost 40% less than Haiku 5.5 on a summarization job, being cheaper on all 20 requests despite both models listing the same price of $0.10 per million input tokens and $0.50 per million output tokens. The cost difference arose from token counts: for the same text, Haiku counted 119,723 input tokens versus Luna's 87,055, and Haiku charged for 2.1 times Luna's output tokens, mostly hidden thinking tokens, while visible words were only 1.28 times. The user noted that on code generation the gap was 12x, and that the job determines the size. The price list does not reflect these differences.
Source report
Two models list the same price: $0.10 per 1M input tokens and $0.50 per 1M output tokens.
I ran 20 podcast excerpts through both models. Here are the results:
Cost Performance
- GPT-6 Luna cost 40% less than Haiku 5.5 on this job.
- Luna was cheaper on 20 out of 20 requests.
Task-Dependent Variance
The cost gap varies significantly by task:
- Code generation: 12x difference
- Summarization job: 40% difference
The job determines the size of the gap. In my case, the difference came from:
- Token counts for the same text
- Default "thinking" charged as output tokens
Token Count Comparison (Same Text)
| Metric | Haiku 5.5 | GPT-6 Luna | |--------|-----------|------------| | Input tokens | 119,723 | 87,055 | | Output token cost | 2.1x Luna's | — | | Visible words | 1.28x Luna's | — |
Haiku charged for 2.1x Luna's output tokens, mostly due to hidden "thinking" tokens. Visible words were only 1.28x Luna's.
Key Takeaway
The published price list reveals none of these differences. Actual costs depend heavily on task type, tokenization efficiency, and hidden processing (such as "thinking" tokens).
Source
aakashguptaNeutral / independent