GPT-5.5: Six Weeks, Doubled Price, and the Most Honest Benchmark Story of the Year

OpenAI released GPT-5.5 on April 23, 2026, and I want to be completely direct about what the day felt like from the outside. It felt anticlimactic in the best way possible. This is not because the model lacks impressive features. It is clearly a massive achievement. The subdued reaction is mostly because the AI release cadence in April 2026 has been so relentless that even a model that tops the global intelligence index lands without the shockwave it would have generated six months ago.

GPT-5.5 arrives exactly six weeks after GPT-5.4. This is an extremely fast turnaround that underscores just how fiercely frontier AI labs are competing for enterprise customers. It also highlights how their models are increasingly evolving through continuous, incremental updates rather than massive generational leaps.

Greg Brockman, OpenAI's president, called it a new class of intelligence for real work and powering agents, describing the launch as a big step towards more intuitive computing. Mark Chen, chief research officer, pointed out that GPT-5.5 shows meaningful gains on scientific and technical research workflows and highlighted its massive potential for drug discovery.

I wanted to look past the press briefing language and dive into the actual benchmark data. Here is what I found.

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The Intelligence Index: OpenAI Takes the Lead Back

GPT-5.5 tops the Artificial Analysis Intelligence Index with a score of 60, edging three points ahead of Claude Opus 4.7 and Gemini 3.1 Pro Preview (which were both sitting at 57). The release ends a brief but notable period where OpenAI had failed to outright claim the top spot upon launch, as GPT-5.4 had only managed a three-way tie.

Three points might sound marginal, but in the context of how this leaderboard has moved in 2026, it is actually quite significant. Gemini 3.1 Pro held the top spot in February. The three-way tie in March was the first time in years that no single model was clearly ahead of the pack. GPT-5.5 breaks that tie and gives OpenAI a clear number-one position for the first time since GPT-5.3.

Whether that lead holds through May depends entirely on when Google ships Gemini 3.2 and whether Anthropic's Claude Mythos ever gets a public release. For now, GPT-5.5 is the undisputed benchmark leader.

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The Full Benchmark Picture

I want to give you the complete table rather than cherry-picking stats. The selectively chosen numbers found in most AI launch coverage are exactly why benchmark comparisons have become so frustrating to rely on.

Here is the official benchmark table OpenAI published at launch:

Here are the benchmarks that actually matter for deciding whether GPT-5.5 fits your workflow:

Note: This table is dynamically generated from our AI Tools database. Values shown are from the latest available benchmarks.

For the complete picture, including GPT-5.5 Pro, all tiered scores, and every benchmark OpenAI published, see the live comparison table on Renovate QR.

A few things stand out immediately when you read this table as a whole rather than scanning for the highest GPT-5.5 numbers.

The long-context jump is the real story. On the MRCR v2 benchmark, which tests how reliably a model can locate multiple pieces of hidden information across very long texts, GPT-5.5 jumps to 74.0% at context lengths of 512K to 1M tokens. This is up from 36.6% for GPT-5.4. On the Graphwalks BFS test with one million tokens, GPT-5.5 leaps from 9.4% to 45.4%. That is not just an incremental improvement. That is a model that can finally use its entire context window accurately rather than just guessing.

Claude still owns SWE-Bench Pro. On SWE-Bench Pro, which tests real GitHub issue resolution, Claude Opus 4.7 beats GPT-5.5 with a score of 64.3% versus 58.6%. OpenAI noted that Anthropic itself acknowledged signs of memorization in some SWE-bench tasks, and that is a fair point. However, the gap is meaningful, and Claude Code remains the benchmark leader for the specific task of resolving real GitHub issues.

The tool-use gap with Claude is real. On MCP Atlas, a tool-use benchmark run by Scale AI, GPT-5.5 scores 75.3%, trailing both Claude Opus 4.7 at 79.1% and Gemini 3.1 Pro at 78.2%. For agentic workflows where tool-calling reliability matters, this is a crucial detail to know before you route your production traffic.

GDPval barely moved. GPT-5.5 scores 84.9% on GDPval, which is only a marginal improvement over GPT-5.4's 83.0%. A full overview of all benchmarks is available. If GDPval accurately measures what it claims to, GPT-5.5 may not be a major leap for everyday professional tasks. This is the benchmark that measures performance across 44 real occupations. For the majority of users whose primary use case is daily knowledge work, this marginal improvement is the most honest signal to base your expectations on.

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The Hallucination Number You Should Not Ignore

GPT-5.5 posts the highest-ever accuracy at 57% on AA-Omniscience, but it carries a staggering 86% hallucination rate. It answers incredibly confidently even when it is completely wrong. Claude Opus 4.7 sits at 36%, while Gemini 3.1 Pro Preview sits at 50%. (Source: Artificial Analysis AA-Omniscience benchmark.)

This is the number that most of the launch coverage buried in a footnote. I am putting it near the top of the benchmarks section because for anyone considering GPT-5.5 for legal, financial, medical, or compliance applications, this is the most important row in the entire table.

The Bank of New York tested GPT-5.5 in recent weeks alongside early access to models from rivals like Anthropic. CIO Leigh-Ann Russell said the improvements were highly meaningful. She noted that what they were seeing from 5.5 was not just better response quality, but also an impressive resistance to hallucinations.

That quote from BNY's CIO, which was attributed to OpenAI's April 23 press briefing, describes a vastly different experience from what the AA-Omniscience number shows. The most likely explanation is that the AA-Omniscience benchmark tests a specific type of factual recall under adversarial conditions, while BNY's internal evaluations test the model on domain-specific finance queries where GPT-5.5 happens to perform more reliably. Benchmark hallucination rates and real-world hallucination rates measure very different things.

The practical guidance here is simple. Run GPT-5.5 on your actual data before drawing conclusions from either the 86% hallucination rate or BNY's positive experience. Both data points are real, but they describe entirely different scenarios.

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The Four Areas OpenAI Is Most Excited About

OpenAI explicitly named four focus areas in their launch materials. Here is what the data shows for each category.

Agentic Coding

This is GPT-5.5's strongest area relative to the competition. On Terminal-Bench 2.0, a coding benchmark for agentic workflows, GPT-5.5 scores 82.7%. This sits seven percentage points above GPT-5.4's 75.1%. Anthropic's Claude Opus 4.7 hits 69.4% and Google's Gemini 3.1 Pro lands at 68.5%.

The Terminal-Bench gap over Claude and Gemini is the largest benchmark lead GPT-5.5 holds over any competitor. For teams running Claude Code or Cursor today and evaluating whether to switch, this is the number that deserves your attention.

The Expert-SWE result (73.1% vs GPT-5.4's 68.5%) reflects a similar pattern of genuine improvement on complex coding tasks that require sustained multi-step reasoning over extended time horizons. OpenAI describes Expert-SWE tasks as having an estimated 20-hour completion time. This is exactly the class of problem where GPT-5.5's improved long-context handling translates directly into better outcomes.

Computer Use

GPT-5.5 scored 78.7% on OSWorld-Verified, which measures computer environment operation capabilities. OpenAI's chief research officer specifically called out the model's improvements in navigating computer work compared to predecessors. This connects directly to the super app thesis. Greg Brockman framed the entire GPT-5.5 release as a step toward a unified AI interface where the model can autonomously operate software on your behalf, rather than just generating text about how you should operate it yourself.

The OSWorld result is competitive but it is not the top of the market. Grok Computer's autonomous desktop agent and OpenAI's own computer use capabilities are converging toward similar performance levels, and the real differentiation will eventually come from integration depth rather than benchmark scores.

Knowledge Work

GDPval at 84.9% shows meaningful capability but a relatively marginal improvement over GPT-5.4. I keep returning to this benchmark because it tests the widest range of real-world professional tasks. A 1.9-point improvement over GPT-5.4 is the honest signal for anyone whose primary use case is standard office productivity, writing, research, and analysis.

If you are a GPT-5.4 power user who loves how the model handles your specific workflow, GPT-5.5 will be meaningfully better in a few specific areas like long documents or code generation, but it will feel roughly the same for general writing and instruction following.

Scientific Research

This is where GPT-5.5 breaks new ground that no previous commercial model has reached. The model shows massive performance boosts in scientific research applications, achieving 25.0% on GeneBench compared to GPT-5.4's 19.0%, and an impressive 80.5% on BixBench for bioinformatics analysis.

Mark Chen stated that GPT-5.5 shows meaningful gains on technical research workflows and noted that the company genuinely feels it could help expert scientists make progress, highlighting drug discovery as a key area.

On FrontierMath, GPT-5.5 scores 51.7% on Tiers 1 through 3, up from GPT-5.4's 47.6%. More impressively, it hits 35.4% on the hardest Tier 4 category, sitting well ahead of Claude Opus 4.7 at 22.9% and Gemini 3.1 Pro at 16.7%. GPT-5.5 Pro pushes that Tier 4 score even higher to 39.6%.

For most everyday users, these scientific research improvements are just background noise. But for research institutions, biotech companies, and developers building AI tools for scientific workflows, these numbers are the most important revelation in the entire launch.

Two Benchmarks the Launch Coverage Missed

The official OpenAI table includes two benchmarks that almost no mainstream coverage mentioned.

Toolathlon tests multi-tool orchestration across extended workflows. GPT-5.5 scored 55.6% versus 54.6% for GPT-5.4, and 48.8% for Gemini 3.1 Pro. This marginal improvement is an honest signal that tool-calling did not see a dramatic upgrade, and Claude and Gemini still maintain their edge here.

CyberGym tests cybersecurity task performance. GPT-5.5 scored 81.8% compared to GPT-5.4's 79.0% and Claude Opus 4.7's 73.1%. This benchmark directly ties to the Trusted Access for Cyber program and the model's High safety rating. GPT-5.5 leads Claude on this test by 8.7 points, which perfectly explains why OpenAI structured a dedicated access tier just for security professionals.

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The Pricing Situation, Honestly Explained

GPT-5.5's API pricing is $5 per million input tokens and $30 per million output tokens, featuring a 1 million token context window. This effectively doubles the price of GPT-5.4.

Let me walk through what that actually costs at real-world usage volumes.

Monthly usage	GPT-5.4 cost	GPT-5.5 cost (headline)	GPT-5.5 cost (with efficiency gains)
10M input / 2M output	$295	$590	~$470
100M input / 20M output	$2,950	$5,900	~$4,720
1B input / 200M output	$29,500	$59,000	~$47,200

The efficiency gain is very real. A roughly 40% reduction in output token usage keeps the net cost increase to around 20%. For workloads that are heavy on text generation, this matters significantly because output tokens are priced at $30 per million while input is only $5 per million.

The five effort levels add another layer of cost control that GPT-5.4 simply lacked. Using medium effort for routine tasks and reserving the xhigh tier only for genuinely difficult problems gives you a practical, tiered model within a single API endpoint. This means you do not need complex external routing logic to switch between cheap and expensive models for different query types.

In fact, GPT-5.5 running on medium effort matches Claude Opus 4.7 at roughly a quarter of the total cost. This is exactly the framing OpenAI wants developers to understand. The headline doubling of the price hides a much more nuanced cost story once you account for token efficiency and effort-level routing.

For practical guidance, if your current GPT-5.4 API bill is under $500 per month, the cost difference is basically noise. You should just migrate to GPT-5.5 and enjoy the capability improvements. If you are spending $10,000 a month or more, the migration requires an honest cost calculation using your actual token ratios and query complexity distribution before you commit.

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How to Access and Migrate

Model strings:

• Standard: gpt-5.5-20260423
• Latest alias: gpt-5.5-latest
• Pro variant: gpt-5.5-pro-20260423

Basic usage:

import openai

client = openai.OpenAI()

response = client.chat.completions.create(
    model="gpt-5.5-20260423",
    messages=[
        {"role": "user", "content": "Your prompt here"}
    ]
)
print(response.choices[0].message.content)

With effort level control:

response = client.chat.completions.create(
    model="gpt-5.5-20260423",
    messages=[
        {"role": "user", "content": "Complex multi-step analysis task..."}
    ],
    reasoning_effort="high"  # options: non-reasoning, low, medium, high, xhigh
)

Cost-optimized routing pattern (recommended for high-volume):

def get_model_for_task(task_complexity: str) -> str:
    routing = {
        "simple": "gpt-5.5-20260423",     # use with reasoning_effort="low"
        "standard": "gpt-5.5-20260423",   # use with reasoning_effort="medium"
        "complex": "gpt-5.5-20260423",    # use with reasoning_effort="high"
        "research": "gpt-5.5-pro-20260423" # use for scientific/frontier tasks
    }
    return routing.get(task_complexity, "gpt-5.5-20260423")

Migration from GPT-5.4: The API interface is completely backward compatible. You just need to replace gpt-5.4 with gpt-5.5-20260423 in your model parameter. No prompt changes are required for the vast majority of use cases. However, you should run your test suite on the new model before switching production traffic, particularly for long-context applications where the improved coherence can occasionally produce different outputs than you were used to.

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The Cybersecurity Access Program

OpenAI rated the model's biological and cybersecurity capabilities as "High" under its Preparedness Framework and is offering specialized access through its Trusted Access for Cyber program for verified security professionals.

This is the part of the launch that the broader tech industry will watch most carefully over the coming weeks. One reporter during the press briefing asked if GPT-5.5 would possess capabilities similar to Mythos, the highly restricted cybersecurity tool recently announced by Anthropic. Mia Glaese, a member of OpenAI's technical staff, noted that GPT-5.5 would indeed have a significant impact on how the company approaches deploying its models toward digital defense.

The key distinction from Anthropic's approach is striking. GPT-5.5 is publicly available with a High cybersecurity rating, while Claude Mythos was restricted entirely to Project Glasswing partners due to its Critical classification. OpenAI is betting that controlled access through the Trusted Access for Cyber program is a sufficient mitigation strategy. That judgment will either prove correct or become the defining security narrative of the coming months. The model did not reach the Critical level under the Preparedness Framework, which would have required more restrictive access controls.

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The Context: What April 2026 Has Become

This is the third major frontier model release in a single week. Claude Opus 4.7 launched on April 16. Kimi K2.6 launched on April 21. GPT Image 2 launched on April 22, and GPT-5.5 followed on April 23.

OpenAI also proudly stated there are currently 4 million active Codex users and 9 million paying business users on ChatGPT. ChatGPT itself boasts more than 900 million weekly active users and over 50 million premium subscribers (per OpenAI reporting from early 2026). OpenAI undoubtedly hopes those figures will crush the social media narrative suggesting the company has lost consumer traction or fallen behind Anthropic in the race for enterprise dominance.

The weekly active user numbers are genuinely staggering. Having 900 million weekly users means ChatGPT has become foundational internet infrastructure in the exact same category as Google Search. The real competitive battle at these numbers is no longer about benchmark scores. It is about ecosystem lock-in, enterprise contract renewals, and whether developers choose to route their new agentic pipelines toward OpenAI or look elsewhere.

GPT-5.5 wins the overall intelligence benchmark. Claude leads heavily on tool-use reliability. Gemini leads on multimodal functions and Google Workspace integration. This three-way parity is the defining story of 2026, and this release certainly does not end it. It simply reshuffles the deck slightly in OpenAI's favor, setting up the next inevitable reshuffling whenever Gemini 3.2 or Mythos finally arrive.

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My Honest Assessment

GPT-5.5 is the best general-purpose AI model available today on overall benchmarks, but that sentence comes with real and important qualifications.

For production coding workflows, Claude Opus 4.7 easily maintains its SWE-Bench Pro lead. For tool-calling reliability at scale, both Claude and Gemini still hold measurable edges. For knowledge-intensive applications where hallucination resistance is strictly non-negotiable, the 86% AA-Omniscience hallucination rate is a severe deployment constraint that you must evaluate against your specific use case before committing your budget.

Where GPT-5.5 is genuinely the right choice without any qualification is in long-context document analysis, advanced mathematics, scientific research, and agentic terminal coding workflows. It is also the undeniable winner for any application where the five-tier effort system lets you dynamically optimize your cost and quality on the fly.

The price doubling is significant but it is not prohibitive once you account for the efficiency gains. Run the math on your own token usage ratios before deciding whether the 20% real-world cost increase is worth the capability upgrade for your specific team.

For most developers, the path forward is clear. You should migrate your systems, benchmark your specific use cases, and set your reasoning effort to medium as the default. Switch to high or xhigh only for the small subset of tasks where you actually need the deepest level of thought.

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Compare GPT-5.5 Against Other Frontier Models

Are you trying to decide between GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and Kimi K2.6 for your specific workflow? The benchmarks above cover a lot of ground but they do not tell the whole story.

Compare AI models side by side on Renovate QR

The /tools directory has detailed pricing, context windows, benchmark scores, and deployment notes for every model covered in this article, and it is updated the same day new releases land.

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This article was written and published on April 23, 2026, the exact day of GPT-5.5's release. Benchmark data is sourced from OpenAI's official release materials and independent coverage from The Decoder, TechCrunch, Fortune, and OfficeChai. We will update the page with independent third-party benchmark verification as soon as it becomes available.