Developers Weigh In: Is GPT-5 a Hit or Miss?

The AI Coding Showdown: Is OpenAI's GPT-5 a Game Changer or a Cost-Effective Compromise? Imagine pouring hours into a complex coding challenge, only to have an AI assistant not just understand your vision, but also craft elegant, bug-free code in a flash. That's the dream OpenAI dangled last week with the launch of **GPT-5**, positioning it as the ultimate "true coding collaborator." They weren't just making a statement; they were throwing down the gauntlet, aiming directly at Anthropic's **Claude Code**, a rising star among **software engineers** for **AI-assisted coding**. But in the high-stakes arena of **AI models**, promises are cheap. The real test comes when **developers** get their hands on the code. And the early verdict on GPT-5? It's a fascinating, complex, and sometimes frustrating mixed bag. The Hype vs. The Reality: A Developer's First Impressions OpenAI's vision was clear: a powerhouse **AI code generation** tool excelling at high-quality code and **agentic software tasks**. Sounds revolutionary, right? Yet, many developers tell a different story. While GPT-5 shines with its **technical reasoning** and impressive ability to plan out intricate coding tasks, some seasoned engineers find that Anthropic's newest contenders—**Claude Opus** and **Sonnet reasoning models**—still outmaneuver it, producing cleaner, more efficient code. One glaring issue? GPT-5's verbosity. Depending on whether you choose low, medium, or high verbosity settings, this **LLM** can get... talkative. While sometimes helpful, this often translates to **unnecessary or redundant lines of code**, turning a potential time-saver into a cleanup project. This isn't just anecdotal chatter. The very **benchmarks** OpenAI used to tout GPT-5's **performance** have faced sharp criticism. One research firm went as far as to label an OpenAI graphic boasting about its capabilities a "chart crime." What's really going on behind the numbers? Keep reading. The Price Tag Paradox: Is "Cheap" Truly Cheaper? Here’s where GPT-5 undeniably pulls ahead, at least on paper: its cost-effectiveness. Sayash Kapoor, a computer science doctoral student and researcher at Princeton University, co-author of "AI Snake Oil," puts it bluntly: "GPT-5 is mostly outperformed by other AI models in our tests, but it’s really cheap." Kapoor and his team have been rigorously **benchmarking** GPT-5 since its public release. Their standard test—evaluating how well a **language model** can reproduce the results of 45 scientific papers—costs a mere $30 to run with GPT-5 at medium verbosity. The kicker? Running the *exact same test* with Anthropic’s powerful **Opus 4.1** model balloons to a staggering $400. In total, Kapoor's team has poured around $20,000 into testing GPT-5, a testament to its accessibility. But is a low price always the best deal? The Accuracy Abyss: A Sobering Look at Performance

Image 1

Kapoor’s extensive tests reveal a crucial trade-off. While GPT-5's price point is attractive, its **accuracy** currently lags behind some of its rivals. In reproducing those scientific papers: * **Claude’s premium model** achieved a 51% accuracy rating. * **GPT-5 (medium verbosity)** only hit 27% accuracy. *(Note: This is an indirect comparison, as Opus 4.1 is Anthropic's top-tier model, and Kapoor hasn't yet tested GPT-5 at its highest verbosity setting.)* OpenAI spokesperson Lindsay McCallum clarified that GPT-5 was trained on "real-world coding tasks" with **early testers** from startups and enterprises. They highlight their internal "thinking" model, which employs more deliberate reasoning and scored highest internally. However, even OpenAI admits its "main" GPT-5 model fell short of previously released models on its *own* accuracy scale. Anthropic's spokesperson, Amie Rotherham, offered a broader perspective: "Performance claims and pricing models often look different once **developers** start using them in production environments." She added that for reasoning-heavy tasks, where **AI models** consume many tokens, "price per outcome matters more than price per token." This hints at a deeper economic consideration for businesses. Real-World Wins: Where GPT-5 Truly Shines Despite the mixed **benchmarks** and cost-accuracy discussions, some **software engineers** are finding GPT-5 to be a powerful ally for specific use cases. Jenny Wang, an engineer, investor, and creator of the personal styling agent Alta, lauded GPT-5's ability to tackle complex coding tasks in a single shot. She compared its prowess to OpenAI’s o3 and 4o, which she frequently uses for **code generation** and simpler fixes. Wang put GPT-5 to the test, asking it to generate a press page for her company's website, complete with specific design elements. The result? GPT-5 nailed it in one go, a task that previously required multiple prompt revisions. Her only caveat: "It hallucinated the URLs." Another developer, speaking anonymously, praised GPT-5 for its capacity to solve deep technical problems. For a hobby project involving a programmatic network analysis tool requiring security isolation, GPT-5 impressed with its recommendations and realistic timelines. "I’m impressed," the developer shared. Even some of OpenAI’s enterprise partners are publicly endorsing GPT-5. Companies like Cursor, Windsurf, and Notion have vouched for its **coding and reasoning skills**. Notion even shared on X that GPT-5 is "fast, thorough, and handles complex work 15 percent better than other models we’ve tested." So, is GPT-5 a breakthrough or a bust? It seems the answer lies in *what* you ask it to do. The Skeptics Speak: Lingering Doubts and Redundancy Just days after its release, social media buzzed with complaints. Many developers felt GPT-5's **coding abilities** were "behind the curve" for what was hyped as a state-of-the-art model from the world's most talked-about **AI company**.

Image 2

Kieran Klassen, a developer building an AI assistant for email inboxes, remarked that GPT-5 "seems like something that would have been released a year ago," comparing its coding to Anthropic's Sonnet 3.5, which launched months earlier. Amir Salihefendić, founder of Doist, called it "pretty underwhelming" and "especially bad at coding," drawing parallels to the "Llama 4 moment"—another **AI model** that disappointed some. Mckay Wrigley, another prominent developer on X, hailed GPT-5 as a "phenomenal everyday chat model" but maintained he would "still be using Claude Code + Opus" for his coding needs. The "exhaustive" nature of GPT-5 also surfaced again. While Jenny Wang found it capable for frontend tasks, she acknowledged its "more redundant" output, suggesting it "could have come up with a cleaner or shorter solution." Sayash Kapoor, however, reminds us that the verbosity *can* be adjusted, allowing users to trade chatty output for better performance or lower costs. The Evolving AI Landscape: Beyond Holistic Improvements Itamar Friedman, cofounder and CEO of **AI-coding platform** Qodo, believes some critiques stem from shifting expectations. He describes the era before 2022 as "BCE" (Before ChatGPT Era), a time when **AI models** improved holistically. Now, in the post-ChatGPT world, new models often excel in specific niches. "Claude Sonnet 3.5, for example, was the one model to rule them all on coding. And Google Gemini got really good at code review," Friedman points out. He suggests that GPT-5, rather than offering a universal leap forward, has refined "a few key sub-tasks." This signals a more specialized future for **AI models**, where developers might pick and choose based on individual project needs. Benchmark Battles: The Unseen Truth The controversy around OpenAI's **benchmark tests** extends beyond "chart crimes." SemiAnalysis, a research firm, noted that OpenAI only ran 477 out of the typical 500 tests in SWE-bench, a standard **AI industry framework** for evaluating **large language models**. OpenAI maintains it consistently uses a fixed subset of 477 validated tasks on its internal infrastructure. They also acknowledge that changes in GPT-5’s verbosity settings can "lead to variation in eval performance," a nuance often overlooked in headline-grabbing **performance claims**. The Bottom Line: OpenAI's Strategic Play? Sayash Kapoor encapsulates the intricate challenges facing frontier **AI companies**: "When model developers train new models, they’re introducing new constraints, too, and have to consider many factors: how users expect the AI to behave and how it performs at certain tasks like agentic coding, all while managing the cost." His conclusion offers a compelling perspective: "In some sense, I believe OpenAI knew it wouldn’t break all of those benchmarks, so it made something that would generally please a wide range of people." So, what does this mean for *you*? GPT-5 might not be the single "one model to rule them all" in **AI-assisted coding**, especially against specialized rivals like Claude. But its impressive **cost-effectiveness** and proven ability to handle complex, one-shot tasks make it a compelling tool in a developer's arsenal.

Image 3

Is it the future of coding? Perhaps not in the way we envisioned a holistic leap. But it's certainly a powerful, budget-friendly contender, redefining what **AI models** can achieve in your daily workflow. The future of **AI code generation** is less about a single champion and more about a diverse team of specialized collaborators. Are you ready to pick yours?