a7om_com

The gap depends heavily on which models you're comparing and through which channel. We track normalized pricing across 2,614 inference SKUs and 47 vendors and the spread between cheapest and most expensive option for functionally similar models regularly exceeds 30x. Direct from model developer vs third-party platform alone averages 7x on input tokens. The frontier model vs budget model gap adds another layer on top of that. The 37x figure is plausible depending on the exact comparison. Full data at a7om.com if useful.

The point about pricing and vendor dependency is worth adding some data to. "Dangerous asymmetry of power regarding pricing" is real but most enterprises have no independent benchmark to even know when they are being overcharged. We track normalized inference pricing across 47 vendors weekly and the spread for functionally similar models regularly exceeds 30x. Customization is a great moat but knowing what the market actually charges for inference is step one. a7om.com

The Qwen 3.5 MoE local performance numbers are striking but the cloud pricing picture for the same models is equally interesting. On inference platforms right now Qwen class models are running significantly cheaper than closed equivalents, open source has about an 81% pricing advantage on equivalent platforms. The local vs cloud crossover math gets genuinely interesting for MoE models because the sparse expert loading that makes Hypura useful locally is the same property that makes cloud inference cheaper per token. Worth knowing both sides of that equation when deciding where to run.

One angle worth considering is cost. If you're using LLMs heavily for language learning the inference bill adds up fast. Output tokens run 3.74x more than input on average across the market right now. For iterative back and forth sessions with a model that cost gap compounds quickly. Prompt caching helps but only about 1 in 5 models actually offer it.

The commoditization point is real and the data backs it up. Output token prices across major vendors have been compressing steadily for months. The real question is whether hyperscalers can hold margin on inference long enough to justify frontier model valuations at IPO. The spread between what it costs to run inference and what vendors charge is narrowing faster than most people realize.