GPT-5 is Here: Everything You Need to Know About OpenAI’s Latest AI Breakthrough
August 7, 2025
OpenAI releases GPT-5 with unified multimodal capabilities, 50% cheaper API pricing, and state-of-the-art performance across coding, reasoning, and multimodal tasks. Get the complete technical breakdown.
Revolutionary AI Release or Incremental Update? GPT-5’s Complex Launch Story
OpenAI unveiled GPT-5 during a Thursday livestream, marking what the company called a qualitative shift in artificial intelligence capability, ending months of anticipation and delivering what CEO Sam Altman claims is “the best model in the world”. This comprehensive guide breaks down everything businesses and developers need to know about the technical specifications, pricing, and strategic implications of this major AI release.
However, the first 24 hours of GPT-5’s release have told a more nuanced story than OpenAI’s marketing materials suggested. While the model demonstrates clear improvements in specific domains like coding, early user feedback reveals a mixed reception that highlights both the promises and limitations of current AI technology.
What Makes GPT-5 Different: A Unified AI System
GPT-5 is a unified system with a smart, efficient model that answers most questions, a deeper reasoning model (GPT-5 thinking) for harder problems, and a real-time router that quickly decides which to use based on conversation type, complexity, tool needs, and your explicit intent. This represents a fundamental shift from previous models where users had to manually select between different AI systems.
The model automatically switches between a fast response mode for simple queries and a deeper “thinking” mode for complex problems. When you select GPT-5 in ChatGPT, you’re using a system that can automatically decide whether to use its Chat or Thinking mode for your request. For complex tasks, GPT-5 switches to GPT-5 Thinking, applying deeper reasoning before answering.
Pricing Revolution: GPT-5 Undercuts Competition Dramatically
One of GPT-5’s most significant advantages lies in its aggressive pricing strategy. The top-level GPT-5 API costs $1.25 per 1 million tokens of input, and $10 per 1 million tokens for output, representing a dramatic cost reduction from its predecessor.
This pricing strategy has industry observers predicting we could be witnessing the start of a much-awaited LLM price war. The discount for token caching is significant too: 90% off on input tokens that have been used within the previous few minutes, which provides substantial cost benefits for applications with repeated context patterns.
GPT-5 Pricing Comparison:
- GPT-5: $1.25/1M input, $10/1M output
- Claude Opus 4.1: $15/1M input, $75/1M output
- Gemini 2.5 Pro: $1.25/1M input (matching GPT-5)
OpenAI is really undercutting Anthropic’s Claude Opus 4.1, which starts at \(15 per 1 million input tokens and \)75 per 1 million output tokens. This pricing strategy has developers touting GPT-5’s pricing as much better than competing models, with some calling OpenAI’s fees for the model “a pricing killer”.
API Flexibility: Three Model Sizes for Different Use Cases
For developers, GPT-5 is coming to OpenAI’s API in three sizes — gpt-5, gpt-5-mini, and gpt-5-nano — which will spend more or less time “reasoning” through tasks. This tiered approach allows businesses to optimize for their specific cost and performance requirements:
- GPT-5: Full performance for complex tasks
- GPT-5-mini: Offers a balanced 400 billion parameter configuration that retains 92% of the full model’s performance while reducing computational requirements by 60%
- GPT-5-nano: At 50 billion parameters, targets edge deployment and real-time applications, achieving response times under 100ms while maintaining 85% of benchmark accuracy
Enhanced Developer Controls and Features
GPT-5 introduces several new API features that provide developers with unprecedented control:
Verbosity Control: GPT-5 supports a new verbosity parameter (values: low, medium, high) to help control whether answers are short and to the point or long and comprehensive.
Reasoning Effort Management: GPT-5’s reasoning_effort parameter can now take a minimal value to get answers back faster, without extensive reasoning first.
Custom Tools: OpenAI has added a new tool type—custom tools—to let GPT-5 call tools with plaintext instead of JSON. Custom tools support constraining by developer-supplied context-free grammars.
Early User Impressions: Excellence in Coding, Challenges Elsewhere
The initial response to GPT-5 reveals a model with distinct strengths and weaknesses. Developers and early testers have provided nuanced feedback that paints a complex picture of the new system’s capabilities.
Coding: GPT-5’s Clear Strength
Early testers believe GPT-5 is unequivocally the best coding model in the world. The model demonstrates significant improvements in software development tasks, with GPT-5 scoring 74.9% on SWE-bench Verified — a test of real-world coding tasks pulled from GitHub — on its first attempt.
GPT-5 is OpenAI’s strongest coding model to date, showing particular improvements in complex front-end generation and debugging larger repositories. It can often create beautiful and responsive websites, apps, and games with an eye for aesthetic sensibility in just one prompt.
Mixed Results for General Tasks
However, user experiences outside of coding have been more varied. In the first 24 hours after its release, the new model was met with mixed reviews. Some users reported issues with basic tasks that highlighted ongoing AI limitations.
Several posts on social media quickly turned to complaints about numerous factual errors, weak math skills, and — in a few cases — even basic spelling mistakes. When asked simple questions like counting letters in “blueberry,” GPT-5 initially said “three” in one test. When told to “think harder,” however, GPT-5 appeared to engage its more advanced reasoning model and came up with the correct answer.
A Tale of Two Experiences
The variance in user experiences appears partly related to which version of GPT-5 users were accessing. OpenAI CEO Sam Altman responded to feedback saying there was an issue with the system. “GPT-5 will seem smarter starting today,” he said. “Yesterday, the autoswitcher broke and was out of commission for a chunk of the day, and the result was GPT-5 seemed way dumber.”
Expert reviewer Simon Willison, who had early access, provided a more positive assessment: It’s my new favorite model. It’s still an LLM—it’s not a dramatic departure from what we’ve had before—but it rarely screws up and generally feels competent or occasionally impressive.
What This Means for Multi-Model AI Platforms
The GPT-5 launch highlights a crucial challenge facing businesses: navigating the rapidly evolving AI landscape where no single model excels at everything. Part of the confusion was due to the architecture of the model. GPT-5 would include an ‘autoswitcher’ for the various model sizes, depending on its task.
This complexity underscores why businesses benefit from platforms that provide access to multiple AI models through a single interface. Rather than being locked into one provider’s strengths and weaknesses, teams can leverage the best tool for each specific task:
- GPT-5 for coding and software development
- Claude for creative writing and complex reasoning
- Gemini for research and analysis tasks
The dramatic pricing differences between models also make cost management more critical than ever. With GPT-5 priced at \(1.25/1M input tokens compared to Claude's \)15/1M, businesses using multiple models need transparent, unified billing to understand their true AI costs.
Technical Improvements and Limitations
OpenAI has made significant technical advances with GPT-5, particularly in areas that have historically plagued large language models:
Reduced Hallucinations
OpenAI’s evaluations suggest that GPT-5 models are substantially less likely to make incorrect claims than their predecessor models, o3 and GPT-4o. If that advancement holds up to scrutiny, it could help pave the way for more reliable and trustworthy agents.
Less Sycophantic Behavior
In targeted sycophancy evaluations using prompts specifically designed to elicit sycophantic responses, GPT-5 meaningfully reduced sycophantic replies (from 14.5% to less than 6%).
Enhanced Safety Measures
The model incorporates new safety training approaches, including “safe-completions” that provide more nuanced responses to potentially sensitive queries rather than binary refusals.
The Business Reality: Incremental Progress, Not Revolution
Despite OpenAI’s claims of “PhD-level intelligence,” many experts view GPT-5 as more evolutionary than revolutionary. Whereas o1 was a major technological advancement, GPT-5 is, above all else, a refined product. During a press briefing, Sam Altman compared GPT-5 to Apple’s Retina displays… Much like an unprecedentedly crisp screen, GPT-5 will furnish a more pleasant and seamless user experience.
The “PhD expert” in question excels at mathematics and coding benchmarks, and can out-compete other models in tests of reasoning and logic. But in reality, outperforming other AIs (and perhaps other humans) in a narrow range of predominately science and maths based tasks does not make it a “PhD expert” – it just makes it competent in those particular areas.
Looking Forward: The AI Cost Management Challenge
The GPT-5 launch illuminates a critical trend in AI development: the growing importance of cost-effective model selection and management. If competitors follow with price cuts, we could be witnessing the start of a much-awaited LLM price war. There’s no doubt a price war would be welcome… Silicon Valley has been hoping that the LLM price-to-performance ratio will eventually improve.
For businesses, this creates both opportunities and complexities:
Opportunities:
- Dramatically reduced costs for AI integration
- Access to more powerful models at competitive prices
- Greater feasibility for AI-powered workflows
Challenges:
- Navigating multiple models with different strengths
- Managing costs across various AI providers
- Ensuring team-wide visibility into AI usage and expenses
Conclusion: The Multi-Model Future
GPT-5’s launch represents both the promise and the current limitations of AI technology. While it delivers significant improvements in coding and cost-effectiveness, the mixed user reactions highlight that we’re still in an era where different models excel in different areas.
The future belongs to businesses that can effectively leverage multiple AI models, selecting the right tool for each task while maintaining cost transparency and team collaboration. As the AI landscape continues to evolve rapidly, platforms that provide unified access to multiple models—with transparent, usage-based pricing—will become increasingly valuable for organizations looking to harness AI’s full potential without the complexity of managing multiple vendor relationships.
The GPT-5 release may not be the revolutionary leap some expected, but it’s another important step toward more accessible, affordable AI that can genuinely transform business operations when implemented thoughtfully and strategically.
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