GPT-5.6 costs and claims: every figure from a sentence that names it

13 of the 629 figures in these field notes come from a sentence that names GPT-5.6. Each one is quoted verbatim from the write-up it appeared in, with the day that write-up went out.

Some of these are about GPT-5.6 and some only measure against it — a competitor’s price quoted next to GPT-5.6’s belongs here too, because that is the sentence someone searching for the comparison is looking for. The sentence tells you which is which.

This is not a GPT-5.6 price sheet. Nothing here is read off a vendor page. These are figures a working developer wrote down, and the date is the day the piece was published — not the day the price was in force. Model prices move fast: read every row as of its own date, and treat an old one as a lead rather than a quote.

The figures

$1.43 — “The price spread is wide even among the Western flagships, which becomes obvious on ReactBench, where one run with GPT 5.6 Sol costs about $1.43 while one run with Fable 5 costs $9.05, which means that a single Fable 5 run comes to a bit more than six times as much as the GPT 5.6 Sol run does.” (2026-09-25) →
54% — “Through its internal multi-agent system, Sol achieves 54% higher token efficiency on agentic coding tasks compared to peer models, showing how strategic design choices can transform from a development challenge into a cost-saving advantage.” (2026-09-17) →
$1.43 — “One GPT-5.6 Sol run might cost $1.43, while other models could cost upwards of $9.00 for the same task.” (2026-09-15) →
54% — “Sol operates in an ultra-mode with internal multi-agents, which improves task completion accuracy and token efficiency; in agentic coding tasks it is 54% more token-efficient than comparable models.” (2026-08-21) →
43.1% — “On ReactBench, GPT 5.6 Sol and Fable 5 posted Pass@1 scores of 43.1% and 41.2%.” (2026-08-21) →
95% — “Luna answers fastest, hits 95% accuracy on basic question answering, and costs the least per batch.” (2026-08-19) →
54% — “Sol is the heavy one, with a claimed 54% better token efficiency than models at the same level and an Ultra mode that runs 4 sub-agents in parallel.” (2026-08-19) →
2 hours 41 minutes 35 seconds — “Sol’s Ultra mode took 2 hours 41 minutes 35 seconds to build one detailed 3D scene, running its sub-agents in parallel the whole time, and a run of that length is not something a fallback can politely interrupt halfway through.” (2026-08-19) →
$1.43 per run — “One front-end benchmark put GPT-5.6 Sol at $1.43 per run against $9.05 for Fable 5.” (2026-08-19) →
43.1% — “ReactBench tests showed that GPT 5.6 Sol and Fable 5 had Pass@1 scores of only 43.1% and 41.2% respectively, indicating problems in real-world React projects.” (2026-08-12) →
$1.43 per run — “In contrast, GPT-5.6 Sol, at $1.43 per run, achieves 43.1% accuracy in the same tests, suggesting that while cheaper models may save money upfront, they often result in longer, more costly development processes.” (2026-08-12) →
$1.43 per run — “For instance, GPT-5.6 Sol, while more expensive at $1.43 per run, shows superior performance with a 43.1% accuracy rate in the same ReactBench tests, which shows that cheaper models may save money upfront but can lead to longer development cycles due to frequent errors and rework.” (2026-08-12) →
$1.43 — “On ReactBench, one run of GPT-5.6 Sol costs about $1.43 while Fable 5 costs $9.05 for the same work — a gap that no per-token price list would have predicted, because the expensive model is not six times more expensive per token, it is six times less efficient at finishing the job.” (2026-08-10) →

All 629 figures, every kind · JSON · CSV

Where these 13 came from

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All write-ups