OpenAI's GPT 5.1: Price-Performance Gains Meet Real-World Coding Headaches for Developers

OpenAI has released GPT 5.1, positioning it as a significant leap in large language model capabilities, described as not merely a faster iteration of GPT-5 but the “highest precision” model ever tested. Initial benchmarks and community reactions, including from Artificial Analysis, highlight notable advancements. GPT 5.1 CodeX reportedly surpassed Sonnet 4.5 in SWEBench performance, while being 26 times cheaper than Sonnet and over 20 times more cost-effective than its predecessor, GPT-5 CodeX. OpenAI claims an extended lead on the Artificial Analysis Intelligence Index, with GPT 5.1 gaining two points, solidifying its position as the “smartest model” on their benchmarks. Beyond raw intelligence, the models boast improved UI generation, reduced reasoning token usage (from 82 million to 76 million), and enhanced throughput, with CodeX Mini reaching up to 71 TPS. Developers also benefit from extended prompt caching, now lasting 24 hours, and a “significantly better” writing style over the API compared to the ChatGPT interface.

Despite the promising statistics, one developer’s extensive hands-on experience with various GPT 5.1 flavors (including standard, high, high fast, CodeX, and CodeX Mini) presents a more nuanced picture. While acknowledging potential cost savings and slight improvements in UI generation, daily use often found GPT 5.1 to be “about as good as five,” sometimes faster, but occasionally “actively worse.” Specific issues encountered during coding tasks were stark: the CodeX CLI insisted on npm despite a bun.lock file, bizarrely used perl with regexes for code edits, and exhibited protracted 12-30 minute runs for simple style passes. In IDE environments like Cursor, the model sometimes made no changes, echoed noop into dev/null, or became stuck in non-terminating commands. While a complex AI SDK migration was successfully executed by GPT 5.1 high with a detailed plan, other tests, such as complex UI token highlighting or Tailwind V4 setup, frequently resulted in errors, crashes, or incorrect implementations across most models, including 5.1 variants. This led to the observation that results are “non-deterministic,” with even Haiku 4.5 unexpectedly excelling in one specific UI task, casting doubt on the practical utility of current benchmarks. The overall impression is one of “frustrating” inconsistency and “weirdness,” underscoring a gap between impressive benchmark performance and reliable, consistent developer utility.