What one run costs
A typical run of this task sends about 10K tokens and gets back 1.2K, over 5 calls — 56K tokens in total. Those are the numbers below, against the published prices.
| Model | Vendor | Per run | Can do what this needs? | Price evidence |
|---|---|---|---|---|
| GPT-5 Nano | OpenAI | $0.00490 | not stated | Vendor page |
| Gemini 2.5 Flash Lite | $0.00740 | not stated | Vendor page | |
| Claude Haiku 5.5 | Anthropic | $0.00800 | confirmed | Vendor page |
| GPT-6 Luna | OpenAI | $0.00800 | not stated | Vendor page |
| GPT-5.6 Luna | OpenAI | $0.0172 | not stated | Vendor page |
| GPT-5.4 Nano | OpenAI | $0.0175 | not stated | Vendor page |
| Gemini 3.1 Flash Lite | $0.0215 | not stated | Vendor page | |
| GPT-5 Mini | OpenAI | $0.0245 | not stated | Vendor page |
Paste the whole thing: the full error, the code around it, what you expected and what happened. Most unhelpful answers come from a one-line stack trace with no context.
This is a reasoning task under uncertainty, and the gap between tiers is real. A cheap model will confidently name the first plausible cause; a reasoning model (A model trained to produce a long internal working-out before its answer. More accurate on hard problems, slower and dearer.) will more often work through why that cannot be it.
Ask for two or three candidate causes ranked, rather than one answer. The ranking tells you where to look even when the top guess is wrong.
What we would pick
This section is our judgement, not a figure read off a page. Everything above is arithmetic on published prices; this is an opinion, and it is labelled as one.