This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on all things vertical video, follow David Pierce. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started For a while, every social and media platform […]

I have been running some experiments with smaller open-weight LLMs on multiple-choice questions of Swedish medical licensing exams. On a dataset called MedQA-SWE, GPT-4 scored 84% accuracy in 2024 and o3 scored 88% in 2025 on a smaller, overlapping dataset. With post-training (SFT) on data from earlier years, I got MedGemma-1.5-4B to a passing score of 60% on the final year’s exam. Find the implementation here: https://github.com/tarolangner/medqaswe_medgemma_sft But even though they were released just three months later, Gemma4-E4B and Qwen3.5-4B are flat out superior already, at 77% with no post-training at all. With reasoning enabled, the latter can get to 87% accuracy. It can even push a bit further if no length cap is put on the reasoning traces, but some of them spiral into repetitive loops about formatting that fill the entire context length without giving any answer. Here, I found it helpful to use an ‘early exit’ thinking intervention proposed in the S-GRPO paper that simply injects a phrase and closes the thinking trace at a predetermined sequence length. I also tried their proposed reinforcement learning method to get shorter reasoning traces, but with only minor gains (probably somewhat underdimensioned training setup). Curiously, Qwen3.5-4B does all reasoning in English despite the Swedish prompt, questions and answer options. But it really seems like the language is no obstacle, even though it’s often estimated to be just 1% of LLM training data. I also have a more detailed write-up on the details and experiments here for anyone interested: https://tensorlabbet.com/2026/07/19/medqaswe_post_training/ submitted by /u/AccomplishedCat4770 [link] [留言]

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Opus 5 runs at 5 and 25 per million tokens against Fable 5 at 10 and 50, so the top tier now costs double for a much smaller gap Thinking is on by default on Opus 5, which silently changes what a tight max_tokens setting does to your output Disabling thinking now returns an error above high effort, so any xhigh or max route that turns it off needs an audit before you migrate Prompt caching starts at 512 tokens on Opus 5, half the Opus 4.8 floor, so short reusable prompts cache with no code change In June I worked through whether Claude Fable 5 was worth double the price of Opus 4.8 and concluded that it usually was, for hard work. Claude Opus 5 landed on July 24 at Opus 4.8's exact price and closed most of that gap. So the answer changed, and a few of the changes will throw errors in code that worked last week. The Price Gap Held, the Capability Gap Closed Opus 5 costs 5 and 25 per million tokens, input and output. That is identical to Opus 4.8 and exactly half of Fable 5 at 10 and 50. Anthropic did not raise the sticker price on the tier it improved, which is the single most consequential fact in this release. What that buys, on the numbers: 79.2 percent on SWE-bench Pro against Fable 5's 80.3, and a CursorBench 3.2 result Anthropic describes as landing within 0.5 percent of Fable 5's peak at max effort, at half the cost per task. On OSWorld 2.0 it goes past Fable 5's best computer-use result at just over a third of the cost. A 1.1 point deficit on the headline coding row, for half the money. Last month the equivalent comparison had an 11 point spread. That is what actually changed, and it flips the default: Fable 5 used to be the reasonable choice for anything hard, and now it has to argue for itself on each task. There is a quieter cost lever too. The minimum cacheable prompt on Opus 5 is 512 tokens, down from 1024 on Opus 4.8. Prompts I had written off as too short to cache now create entries with no code change at all. If you run a lot of small repeated calls,