Before getting too excited, take a look at the intelligence vs cost matrix: https://artificialanalysis.ai/models?intelligence-index-toke...
Like 96% vs 93% or something
DeepSWE will let you chart turns taken or tokens used, and FrontierCode will chart tokens. If you use that, you can see Sol high and Terra max get about the same DeepSWE number, but Terra max takes twice the turns. Luna max scores a smidgen lower with even more turns.
Smaller models relying on lots reasoning may "scale down" better on easier tasks, because unlike size, reasoning effort is dynamic: the model can see the task looks easy and stop. On DeepSWE, the cost curves for the three 5.6 models are almost on top of each other, but on FrontierCode Extended, the version of FrontierCode with the most everyday tasks in the mix, there's a spread of costs at the ~55% level.
The recent Laguna S 2.1 model (118B, 8B active) puts up surprising coding numbers for its size, and the lab behind it specifically credits its "way of working (persistence, verification, willingness to backtrack)". Some other open models that folks report getting good mileage out of seem to get there partly by throwing a lot of reasoning at the problem.
There is a little bit of a question, if some models rely on getting it wrong a bit more at first and external checks catching the problems, of whether they're also more frequently getting things wrong they can't self-verify (say, quality of UI or API design) and then it falls to the human to find it. Still, getting the results they're getting at all is neat.
Some benchmarks historically favored reporting only on the max variants, maybe because they want to show the frontier? but that is not always what you need for practical decision. (AA has the full effort sweep for Opus 5 and Sol/Luna/Terra at least.) And at least FrontierCode finds Opus 5 taking a hit in performance above 'medium'.
I am not trying to pick a winner here. I'm probably not going to use tiny models on max for everything, but I think it's cool that you can get so much more out of a small model by amping up reasoning, tool use, and persistence.
In general, smart models work fine with any tools, dumber models need better tools to achieve same results but better tools can eat more context/take more turns
I've gotten decent results with Llama 3.1 8b on Hugging Face tester with Exa MCP since it seems to dump sufficient context into WebSearch/WebFetch type calls even a crappy model almost always gets back what it needs as long as it calls the tool at least once. I had Claude Code look at previous sessions with SearXNG vibe MCP compared to Exa MCP and results got better when it modified SearXNG to work very similarly to Exa. Ended up with this https://gist.github.com/nijave/604c43e3e0fdcd60f5280d3a6b109... although it's really only optimized for "search" not "fetch" at this point. Fetch is basic niquests without Javascript or anything clever
Terra is great for the humans to talk to.
Sol is really only useful if you need to do more delicate things like synthesis of multiple competing pieces of information.
A system that uses all three variants will massively outperform a system that just uses the biggest model for everything.
No wonder why Tibo can afford to hit the reset button liberally.
Would you like all products everywhere be priced like their producers want?
If they do, they do it secretly and when governments find about that they are going to receive a big fine, together with shops that colluded with them.
In capitalism there are many many competitions going on at the same time. Both models can compete and inference providers can compete for costs.
In your apple example if at the same stores you saw "open" apples having different prices and having sales you might question if the costs of those frontier apples are not being manipulated.