We Moved Up One Model Size, and the Result Flippederic

In the last post we ran a small writing test and the newest small model lost. Gemini Flash wrote our short copy better than Claude Haiku 5.5, the fresh small model from a frontier lab. We promised a follow-up with one change: keep Flash, but swap the small Claude model for a bigger one from the same lab.

Here is that round. The result flipped, and it flipped on both of the things we actually care about.

The same test, one size up

Nothing else changed. Same three jobs, a tweet, a forty-to-sixty word gallery caption, and a one-hundred-and-fifty word blog intro in the same house voice. Same brief, same token budget, same afternoon. The only difference from round one is that Claude Haiku 5.5 stepped aside and Claude Sonnet 5.5, one size up, took its place against the same Gemini Flash.

What came back

Two things jump out. The bigger model was faster on every job, not slower. And this time it also wrote the copy we would put our name on, across all three.

Both models hit the word count almost exactly, so that tiebreaker from round one disappeared. The gap showed up where it did last time, in the caption. We asked both to describe the same image of Chang'e rising toward the moon in pale-blue silk.

Flash wrote:

The composition keeps the scale hard and quiet: a solitary figure leaving Earth behind under a field of pinprick stars.

Sonnet wrote:

Luminous clouds drift past her feet as she climbs, one hand reaching toward the light she is about to inhabit.

Flash does the same thing it did in round one. It steps back and describes the composition, the way a critic standing next to the frame would. Sonnet stays inside the picture and ends on a line we would actually print under the image. On the blog intro the difference was rhythm. Sonnet broke it into short beats that land, including a two-word paragraph that does real work. Flash was good, just a little flatter.

The variable was size, not the logo

Look at what moved between the two rounds. Not the lab. Not which model was newer. The only thing we changed was model size, and the writing we would ship flipped from Flash to Claude.

That is the whole point. If we had picked by lab reputation, we would have been wrong in round one, when the frontier lab's small model lost. If we had picked by newest release, we would have been wrong in round one too. The axis that actually predicted the writing was none of those. It was the size tier, measured against our own three jobs. You cannot read that off a spec sheet or a leaderboard. You get it by running the thing.

The small-equals-fast assumption broke too

There is a second habit worth naming. We assume the smaller model is the faster one. Here the bigger model answered quicker on all three jobs. Flash's latency includes heavy server-side thinking, and Sonnet simply came back sooner. Another default that did not survive contact with the test. If speed is a hard constraint for you, that is exactly the kind of thing you want to measure rather than assume.

So which one do we use

The honest answer is that it depends on the job and the budget. For high-volume throwaway copy where nobody reads the single output closely, the cheapest fast model is fine and the cost difference adds up. For anything that carries our name, the step up paid for itself here, and it did not cost us speed. The value of running both rounds is not a trophy for one model. It is that we now know the shape of the tradeoff instead of guessing at it.

Reliability is its own feature

One run tells you what a model can do. It does not tell you what it does every time. So we ran the intro job five times for each model and watched the spread. The gap that showed up matters more than the single-run result.

Flash never opened the same way twice. Every run was a different intro, some sharp, some flat. Sonnet opened with the same clean three-beat hook on all five runs, the one that ends "it isn't one." That is the difference between a model that can write the good version and a model that reliably does. For copy that carries your name, the second one is worth more before you have read a single word of it.

This also explains something from round one. Flash's intro there opened with a hook we praised, and we could not reproduce it once in five tries. It was a lucky draw at a temperature of 0.7, not a trait. Sonnet's quiet advantage is that it does not lean on luck.

The same limits as before

This stays directional, not a benchmark. One sample per job for the head-to-head above, five for the intro. Flash ran at a temperature of 0.7 while both Claude models ran at their own default, because the temperature control is now deprecated on them. The latency numbers include server-side thinking on both sides. None of that changes the takeaway, because the takeaway is not "use this model." It is "run your own jobs before you decide."

Two rounds, two surprises, one habit. Before you lock a model into a job that matters, spend twenty minutes and let your own work pick the winner. Ours kept choosing differently than the leaderboard would have.

The test harness was a short script that sends the same prompt to two APIs and prints the answers side by side. If there is interest, we will write up how we wired it, and how we decide which jobs are worth sending to the bigger model.