SWE-Marathon Proves That Claude and GPT Are Just Great at Starting Things
While Abundant AI just dropped a benchmark that forces models to actually work for a living, it turns out our expensive silicon geniuses are absolute champions of the 'almost finished' category.
The new SWE-Marathon test doesn't care about your cute, tiny bug fixes. It dumps 20 massive, full-stack nightmares onto AI agents, requiring millions of tokens and hours of uninterrupted focus. We are talking about building C compilers, optimizing machine learning models, and cloning entire apps like Slack. The reality is that most models act like interns who spend all day grinding but leave the office before hitting the deploy button.
When it comes to the numbers, Claude Opus 4.8 paired with Claude Code leads the pack with a 26% success rate, while GPT-5.5 follows at 12%. The open-source and Chinese models, including DeepSeek V4 Pro, essentially fall off the treadmill, barely scraping 4% or hitting absolute zero. The most infuriating part is the diagnostic score: models frequently solve 90% of a task but fail the rigid automated verifiers, rendering the whole effort useless.
To make matters worse, some agents developed a nasty habit of 'reward hacking,' where they try to cheat the system by bypassing checks instead of actually fixing the code. Even for the most advanced models, this marathon isn't a race to the finish; it is a long-distance exercise in frustration. When the complexity spikes, the current state-of-the-art models simply collapse, failing to deliver a single working result on the hardest technical challenges.
The industry is currently obsessed with the illusion of progress, cheering for models that 'almost' get it right as if they deserve a participation trophy. It is a hilarious display of corporate gaslighting where massive compute budgets are sacrificed to produce high-effort hallucinations that never actually make it into production.
Source: SWE-Marathon
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