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GPT-5.6 Sol in production

Measured over 34 real billed calls on this platform. The cost numbers were roughly what we expected. The latency numbers were not.

The short version

Median cost per call was $0.08331, mean $0.08507 across 34 calls. The gap between those two figures is a read on how stable your prompt prefix is: heavy cache reuse pushes the mean well above the median, because a minority of calls rebuild the cache while most read it.
First token averaged 11,953 ms — the slowest of the GPT models we measured, including the more expensive gpt-6-astra. Price and latency did not move together.
List price is $5.00 per 1M input, $30.00 per 1M output.
For short, high-frequency calls, gpt-5.6-terra did the same work at about a third of the cost per call in our measurements.

Measured against its siblings

Same platform, same billing system, real invoices. This is the comparison that is hard to find anywhere else, because it requires having actually run all of them:

Model	Calls	Avg cost / call	Avg first token	Avg output
`gpt-6-astra`	14	$0.1047	2,176 ms	372 tok
`gpt-5.6-sol`	29	$0.0796	11,873 ms	975 tok
`gpt-5.6-terra`	13	$0.0233	7,767 ms	124 tok

The most expensive model was the fastest to first token by a factor of five. If you assumed the bigger model would be slower, that assumption does not hold here.

Two caveats worth stating plainly. These are different workloads, not a controlled benchmark — average output length differs by a factor of eight across the three rows, and that alone moves both cost and duration. And the sample sizes are small. Treat this as a directional signal from real traffic, not a lab result.

What it actually cost

Per call	Cost at list price
Cheapest	$0.00000
Median	$0.08331
Mean	$0.08507
Most expensive	$0.26128

34 calls · 328,905 input tokens · 35,159 output · 386,176 cache-read · $2.89 total at list price. Regenerated from billing data.

Median $0.08331 against mean $0.08507. Where those two diverge sharply — as they do on claude-sonnet-5 — it is cache behaviour talking, not the model: most calls read a cached prefix cheaply while a few rebuild it expensively, and the mean carries the rebuilds.

Latency, and what to do about it

First token averaged 11,953 ms across these calls, and the slowest was 71,337 ms. Total durations ran to 135 seconds on long generations.

Do not put this model behind a synchronous UI without streaming. Twelve seconds to first token is a long time to look at a spinner.
Set client timeouts above 120 seconds for long generations, or you will cut off legitimate work. A default 30-second timeout will produce failures that look like errors but are not.
Stream if you can. First token is what a user feels; total duration is what a batch job waits on. Streaming turns the first number into the perceived latency.

When terra or luna is the better pick

The three GPT-5.6 tiers differ enormously in price: $5.00 / $30.00 for sol, $2.00 / $12.00 for terra, $0.49 / $2.01 for luna. That is a ten-times spread from top to bottom, for the same API shape and one word different in your request.

Classification, extraction, routing, tagging, reformatting — luna. These are high-volume and low-judgement, and they dominate token counts in most pipelines.
Agent loops and bulk work where quality matters but is not the product — terra. In our measurements it averaged about a third of sol's cost per call.
Final answers a user reads, or reasoning that has defeated the cheaper tiers — sol.

Routing the routine steps down a tier usually moves blended cost closer to the cheap model than the expensive one, because routine calls dominate the count.

Calling it

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY", base_url="https://apiclan.us/v1")

resp = client.chat.completions.create(
    model="gpt-5.6-sol",
    messages=[{"role": "user", "content": "Hello"}],
    stream=True,          # first token is what the user feels
)

The model string keeps the dot: gpt-5.6-sol. A misspelled model name returns a 404 from the edge, which means the request never reaches the gateway and never appears in your usage log — it looks exactly like a bad key. If a call fails and nothing shows up in your usage history at all, check the spelling and the base URL first.

What you pay here

Per 1M tokens	List price	APICLAN	You save
Input	$5.00	$1.00	80%
Output	$30.00	$6.00	80%

Calculator and the rest of the family on the price page.

Questions people actually ask

Why is it slower than the more expensive model?

We measured it, we did not cause it, and we cannot see inside the provider's routing. What we can say is that in our traffic the correlation between price and first-token time was negative. Measure it on your own workload before designing around either assumption.

Sol or terra?

Terra unless output quality is the product. It averaged about a third of the cost per call here. Run both on twenty real inputs from your own product and read the outputs — that settles it faster than any benchmark.

My requests keep timing out at 30 seconds.

That is your client's default, not a failure. Long generations in this sample ran to 135 seconds. Raise the timeout or switch to streaming.

Start using it

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Prices quoted on this page are regenerated automatically from live billing data. Third-party terms are quoted from that party's own published documentation.