Brilliance with weakness: What ChatGPT's GPT-5.5 really delivers – top performer and problem child at the same time

Brilliant, but flawed: Why OpenAI's GPT-5.5 could become a threat to businesses

Better than Claude and Gemini? Where GPT-5.5 triumphs – and where it fails miserably

OpenAI has released GPT-5.5, its most ambitious AI model to date – a true technological powerhouse that breaks almost all existing benchmark records. However, this milestone comes with a significant drawback: in addition to doubled API prices, the system struggles with an alarming hallucination rate of 86 percent. While the model excels in areas such as mathematics and abstract problem-solving, it invents facts more frequently than its direct competitors Anthropic or Google when faced with knowledge gaps. So, is GPT-5.5 the hoped-for foundation for OpenAI's planned super-app, or a risky tool that presents companies with entirely new challenges? A detailed analysis of its strengths, weaknesses, and strategic implications.

Ranked number one, with an 86 percent hallucination rate – that's not a contradiction, but the real problem

On April 23, 2026, OpenAI released its highly anticipated model GPT-5.5, internally codenamed "Spud," marking one of the most ambitious AI releases in the company's history. This model is the company's first completely re-trained Large Language Model since GPT-4.5 – not a fine-tuning update, not an extension of existing weights, but a base model developed from the ground up, with correspondingly high expectations for performance improvements.

The benchmark figures presented by OpenAI at launch are indeed impressive. On the GDPval benchmark, which measures performance across 44 real-world job tasks from nine leading industries, GPT-5.5 achieves 84.9 percent – ​​the highest score ever recorded on this benchmark. On Terminal-Bench 2.0, a test for multi-step command-line workflows, the model scores 82.7 percent, while Claude Opus 4.7 remains at 69.4 percent and Google's Gemini 3.1 Pro reaches 68.5 percent. In the area of ​​general intelligence, GPT-5.5 achieves 91.0 percent on the GPQA benchmark and leads the Artificial Analysis Intelligence Index.

The price of progress: Doubling API costs

However, this performance increase comes with a significant price hike. OpenAI has doubled the API rates for GPT-5.5 compared to its predecessor, GPT-5.4. Where GPT-5.4 cost $2.50 per million input tokens and $15.00 per million output tokens, GPT-5.5 now costs $5.00 for input and $30.00 for output. The Pro version, which pushes mathematical benchmarks to a new level, costs $30 for input and $180 for output per million tokens – a complex query with a context of 500,000 tokens can cost over $100 for output.

OpenAI mitigates this shock with Flex and Batch pricing tiers, which enable cost savings of up to 50 percent for asynchronous or latency-tolerant workloads. Since GPT-5.5 consumes an average of 15 to 20 percent fewer tokens than its predecessor due to more compact reasoning, the actual net increase per request is estimated at 60 to 70 percent – ​​noticeable, but not quite as drastic as the nominal price difference suggests. Nevertheless, compared to its direct competitors – DeepSeek V4 Pro for $1.74 in and $3.48 out, and Gemini 3.1 Pro for $1.25 in – OpenAI has significantly widened its price gap.

The hallucination question: An 86 percent problem

And then there's the number that seriously disrupts the image of GPT-5.5 as flawless progress: 86 percent. On the same day that OpenAI celebrated its launch, Artificial Analysis – an independent AI evaluation platform – published the results of the AA Omniscience benchmark, which is specifically designed to measure how often a model confidently answers a question incorrectly, rather than admitting uncertainty.

GPT-5.5 achieves 57 percent accuracy on this benchmark – the highest accuracy ever measured for factual questions. At the same time, its hallucination rate, meaning the frequency with which the model confidently provides an incorrect answer, is 86 percent. Claude Opus 4.7 hallucinates at 36 percent on the same benchmark, and Gemini 3.1 Pro at 50 percent. So GPT-5.5 knows more than any other model – but when it doesn't know something, it invents a plausible-sounding answer more often than any competitor.

This finding is not an editorial error, a testing error, or a surprise: it describes the fundamental design dilemma of a model optimized for coherence and self-assurance. The training algorithm rewards confident, consistent answers—with the side effect of lowering the threshold for admitting uncertainty. The term Artificial Analysis uses is precise: confabulation. The model doesn't invent answers because it wants to lie, but because its training maximizes the production of coherent, task-relevant outputs, even where knowledge is lacking.

Strengths in comparison: Where GPT-5.5 actually has the edge

To complete the picture, a closer look at the benchmarks is worthwhile, where GPT-5.5 clearly comes out on top. In the ARC-AGI-2 test, which targets general intelligence and abstract problem-solving, GPT-5.5 achieves 85.0 percent compared to 73.3 percent for GPT-5.4 – an increase of 11.7 percentage points. In the complex instruction compliance test (IFEval), the score rises from 89.8 to 94.2 percent. GPT-5.5 also outperforms its predecessor in tool usage and in the MCP Atlas benchmark for agent-based workflows, scoring 75.3 percent compared to 67.2 percent for GPT-5.4.

On FrontierMath Tier 4, a test for complex mathematical tasks, GPT-5.5 achieves 35 percent, while Claude remains at 11.9 percent and Gemini at 16.7 percent. This superiority in demanding quantitative tasks makes GPT-5.5 a particularly valuable tool for mathematically intensive applications – financial modeling, scientific computing, and engineering.

Weaknesses become apparent, however, in benchmarks that closely reflect actual software development practice. On SWE-Bench Pro, the benchmark for real GitHub issue solutions, Claude Opus 4.7 scores 64 percent, while GPT-5.5 achieves 58 percent. Claude also outperforms OpenAI's new model in some test categories of the MCP-Atlas benchmark. Thus, GPT-5.5's lead is nuanced: strong in abstract reasoning and mathematics, weaker in practical software engineering tasks.

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Omnimodality and agentic architecture

GPT-5.5 was designed to be natively omnimodal – it processes text, images, audio, and video in a single, integrated model without having to attach different modalities afterward. This distinguishes it from previous approaches where image or audio processing was added as external modules, leading to inconsistencies and quality degradation at the interfaces. The fully expanded context window and improved capabilities for multi-stage, agent-based workflows are intended to make GPT-5.5 particularly attractive for enterprise applications.

This realignment is no coincidence, but a direct response to a strategic crisis. According to its own internal reports, OpenAI has been in a so-called "code red" state since December 2025, after Anthropic with Claude and Google with Gemini made significant strides. Particularly in the B2B segment, Anthropic, with its Claude models, is now considered the benchmark solution for enterprise customers who require stable, reliable, and well-documented AI solutions. OpenAI's response is a clear realignment: away from consumer-oriented creative tools like the discontinued video generator Sora, and towards productive, enterprise-focused applications.

The super app as a strategic vision

GPT-5.5 is therefore not just a model update, but the cornerstone of a much larger strategic initiative. Sam Altman, OpenAI's CEO, is said to have explained to employees that the model could truly accelerate the economy – a typical Altmanian formulation that reflects both visionary self-confidence and managing expectations towards investors.

Specifically, GPT-5.5 is intended to form the technical basis for a planned super-app that combines ChatGPT, the coding tool Codex, and its own browser into a single desktop application. This platform is meant to represent a kind of all-in-one operating system for knowledge work—an ambitious undertaking that puts OpenAI directly in competition with Microsoft, Google Workspace, and the emerging AI-native productivity platforms. GPT-5.5 must be more than just a more powerful model: it must function as a reliable, scalable, and trustworthy foundation for complex, multi-day workflows.

Market classification: The dilemma of superiority with limitations

How can GPT-5.5 be positioned in the market? The most honest answer: It is an exceptionally capable model with a clearly defined application profile and equally clear limitations. For creative work, conceptual thinking, mathematical problem-solving, and abstract reasoning tasks, GPT-5.5 is the most powerful model on the market. For any application requiring factual accuracy, source accuracy, or regulatory correctness—legal analysis, medical documentation, compliance reports, historical research—the 86 percent hallucination rate is a risk that cannot be ignored.

The doubled price also makes the model less economically attractive than alternatives for price-sensitive applications requiring large token volumes. Developers seeking a high-performance software development model will consider Claude Opus 4.7 due to its strengths in SWE-Bench. Cost-optimized applications can use DeepSeek V4 Flash, which delivers comparable coding performance at a fraction of the price.

The structural question behind the model

GPT-5.5 raises a more fundamental question that goes far beyond this single release: Can a model simultaneously combine ever more comprehensive knowledge and ever fewer hallucinations – or is the increasing confabulation rate a structural trade-off that can only be partially resolved with more training and better algorithms?

Current trends offer little cause for optimism. Reasoning models like GPT-5.2, which were explicitly optimized for reliability, have already shown measurably fewer hallucinations than their non-reasoning predecessors. GPT-5.5 appears to be heading in the opposite direction: more capacity, more knowledge, but also more self-confidence in areas where this confidence is unjustified.

This tension is not just a technical problem. It has economic and ethical implications: Companies that integrate GPT-5.5 into automated decision-making processes without incorporating explicit verification steps expose themselves to a systematic risk of error that is difficult to quantify and often remains invisible in practice – because the wrong answer sounds just as confident as the right one.

What remains of GPT-5.5

GPT-5.5 will set the benchmark for high-performance generative AI in 2026—a fact that's hard to dispute given its benchmark dominance in many categories. At the same time, it will be the model that teaches the industry that raw benchmark supremacy doesn't equate to practical reliability. Its ability to solve 44 professional tasks at an expert level is impressive—as long as no one forgets that the same model, in areas it doesn't master, is more likely to invent than it admits.

The message is clear: GPT-5.5 is not a better Claude. It's a different tool, with different strengths, different limitations, and a different economic profile. Those who recognize this can use it strategically and successfully. Those who view it as a universal answer to all AI needs will sooner or later encounter the limitations of this new intelligence with a confidently presented false answer.

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