GPT-5.5
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GPT-5.5 is a large language model developed by OpenAI and released on April 23, 2026.[1][11] Known internally by its codename "Spud," it is OpenAI's first fully retrained base model since GPT-4.5,[11][13] meaning every intermediate release in the GPT-5 family (versions 5.1, 5.2, 5.3, and 5.4) was built on post-training iterations of the same underlying architecture, while GPT-5.5 represents a ground-up redesign with a new pretraining corpus, revised architecture, and training objectives oriented toward multi-step agentic task completion.[26]
The model was positioned by OpenAI as its "smartest and most intuitive" offering at the time of launch, with gains concentrated in agentic coding, computer use, long-context retrieval, and early scientific research.[1] It shipped in two reasoning configurations at launch (a standard gpt-5.5 and a compute-intensive gpt-5.5-pro), with a faster, conversational sibling, GPT-5.5 Instant, following on May 5, 2026 as the new default model in ChatGPT.[6][10] API access opened on April 24, 2026.[4]
GPT-5.5 scored 85.0% on ARC-AGI-2, 93.6% on GPQA Diamond, and 82.7% on Terminal-Bench 2.0, leading on most agentic and long-context benchmarks while trailing Claude Opus 4.7 on head-to-head coding evaluations like SWE-bench Pro.[14][17] The model carries a knowledge cutoff of December 1, 2025.[4]
OpenAI's post-GPT-5.0 release cadence produced four incremental models (5.1, 5.2, 5.3, 5.4) between August 2025 and early 2026, each representing alignment and tool-use refinements layered over the same base architecture introduced with GPT-5. That strategy allowed rapid iteration but imposed architectural limits: every capability gain came through post-training, not from raising the underlying model's ceiling.
GPT-5.5, codenamed "Spud" inside OpenAI (the company has a tradition of low-key codenames such as Strawberry and Orion to avoid pre-launch speculation), broke that pattern.[11] OpenAI retrained the base from scratch, revising the pretraining corpus, changing the attention architecture, and introducing training objectives that reward completion of multi-step tasks rather than optimizing for single-turn response quality.[26] According to OpenAI, this shift moved the ceiling on fundamentals including long-context reliability, multi-step reasoning, and token efficiency.[1]
OpenAI president Greg Brockman framed the launch as a structural shift rather than an incremental update. "In many ways, it is a step towards a new way of getting work done with a computer," he said in an interview around release, describing the model as "a beginning point" pointing at heavier agentic deployments to come.[35] Mark Chen, OpenAI's chief research officer, noted that GPT-5.5 "shows meaningful gains on scientific and technical research workflows" and could help expert scientists make progress on areas such as drug discovery.[1]
The release was accompanied by a system card published on OpenAI's Deployment Safety Hub, with cybersecurity-specific updates added on April 24, 2026.[3][21] OpenAI rated GPT-5.5's biological, chemical, and cybersecurity capabilities as High under its Preparedness Framework, one level below Critical, and initially restricted access to a GPT-5.5 Cyber variant to verified security practitioners through OpenAI's Trusted Access for Cyber program.[2][22]
| Property | Value |
|---|---|
| Developer | OpenAI |
| Release date | April 23, 2026 |
| API availability | April 24, 2026 |
| Codename | Spud |
| Architecture | Transformer (retrained base) |
| Input modalities | Text, images, audio, video, documents |
| Output modalities | Text |
| Context window (API) | ~1.05 million tokens |
| Context window (Codex) | 400,000 tokens |
| Max input tokens | 922,000 |
| Max output tokens | 128,000 |
| Knowledge cutoff | December 1, 2025 |
| Reasoning effort levels | none, low, medium (default), high, xhigh |
| Computer use | Yes (via Codex) |
GPT-5.5 shares the same text and image input stack as the broader GPT-5 family but extends it with computer-use screen reading within Codex, allowing the model to see what is on screen, click, type, and navigate interfaces directly.[4] Audio, video, and document inputs are also natively supported.
The reasoning.effort parameter, carried over from GPT-5.4, accepts five levels: none, low, medium, high, and xhigh.[4] The xhigh setting applies maximum test-time compute and is intended for the hardest asynchronous agentic tasks. At every effort level, GPT-5.5 reaches equivalent or better scores than GPT-5.4 while using fewer reasoning tokens, a token-efficiency improvement OpenAI attributes to the retrained base.[1]
GPT-5.5 Pro is the same underlying model with additional parallel test-time compute allocated to harder queries. It is not a separate training run; instead, OpenAI applies extra inference budget on a per-request basis. Pro access is restricted to Pro, Business, and Enterprise ChatGPT subscribers and to API customers willing to pay the premium per-token rate.[5]
In the Codex coding environment, GPT-5.5 runs with a 400K-token context window and supports a Fast mode that generates tokens roughly 1.5 times faster for 2.5 times the cost.[7] At launch in late April 2026, Codex access required ChatGPT authentication; API-key authentication was added soon after.[7]
| Benchmark | GPT-5.5 | GPT-5.5 Pro | GPT-5.4 | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|---|---|
| ARC-AGI-2 | 85.0% | --- | 73.3% | 75.8% | 77.1% |
| GPQA Diamond | 93.6% | --- | --- | 94.2% | 94.3% |
| HLE (no tools) | 41.4% | --- | --- | 46.9% | --- |
| SWE-bench Pro | 58.6% | --- | 57.7% | 64.3% | 54.2% |
| Terminal-Bench 2.0 | 82.7% | --- | 75.1% | 69.4% | 68.5% |
| OSWorld-Verified | 78.7% | --- | --- | 78.0% | --- |
| BrowseComp | 83.4% | 90.1% | --- | --- | --- |
| FrontierMath Tier 4 | 35.4% | 39.6% | 27.1% | 22.9% | 16.7% |
| FrontierMath Tiers 1-3 | 51.7% | --- | 47.6% | 43.8% | 36.9% |
| AIME 2025 | 94.0% | --- | --- | --- | --- |
| MRCR v2 (512K-1M ctx) | 74.0% | --- | 36.6% | 32.2% | --- |
| GDPval | 84.9% | --- | 83.0% | ~80.3% | 67.3% |
| CyberGym | 81.8% | --- | 79.0% | 73.1% | --- |
GPT-5.5's clearest benchmark advantages over contemporaries fall into three categories. On agentic desktop operation (OSWorld-Verified: 78.7%) and command-line automation (Terminal-Bench 2.0: 82.7%), it holds a meaningful lead over Claude Opus 4.7.[1][14] On ultra-long context retrieval, the MRCR v2 gap of 74.0% versus 32.2% for Claude Opus 4.7 was the largest single-benchmark margin in any direct comparison published at launch.[1] On hard mathematics (FrontierMath Tier 4), GPT-5.5 Pro outperformed its nearest rival by roughly 17 percentage points, with the standard model still leading at 35.4%.[1]
The benchmark picture was less one-sided than OpenAI's announcement framing suggested. Claude Opus 4.7 scored 64.3% on SWE-bench Pro versus GPT-5.5's 58.6%, a gap that reviewers found meaningful in real-world agentic coding workflows.[24][25] On GPQA Diamond, both Gemini 3.1 Pro (94.3%) and Claude Opus 4.7 (94.2%) edged out GPT-5.5 (93.6%).[17] On Humanity's Last Exam without tools, Claude Opus 4.7 scored 46.9% to GPT-5.5's 41.4%.[18] Independent testing by Artificial Analysis confirmed the Terminal-Bench and OSWorld results and ranked GPT-5.5 (xhigh) at the top of its Intelligence Index with a score of 60, but flagged "somewhat verbose" output and a 65.76 second time-to-first-token figure that is well above the median for non-reasoning models.[38]
On AIME 2025, GPT-5.5 scored 94.0%, marginally below GPT-5.0's reported 94.6% on the same test but still near the ceiling for that competition mathematics benchmark.[17] On MMLU and MMMU, the model's advances were less pronounced, with reviewers noting that visual reasoning trailed Gemini 3 Pro on several multimodal sub-tasks.[37]
OpenAI also reported a 6 percentage-point gain over GPT-5.4 on GeneBench, an internal benchmark testing multi-stage scientific data analysis on real bioinformatics workloads.[1] In separately reported Pro runs, GPT-5.5 Pro reached 33.2% on GeneBench, compared with 25.6% for GPT-5.4 Pro and 10.8% for GPT-5.2 Pro.
| Model | Input (per 1M tokens) | Cached input (per 1M tokens) | Output (per 1M tokens) | Long-context input (>272K tokens) | Long-context output |
|---|---|---|---|---|---|
| gpt-5.5 | $5.00 | $0.50 | $30.00 | $10.00 | $45.00 |
| gpt-5.5-pro | $30.00 | Not available | $180.00 | --- | --- |
Cached input tokens for gpt-5.5 cost $0.50 per million, which is 10% of the standard input price.[5] Caching applies automatically when the input prefix matches a previously processed prompt. The long-context pricing tier activates for prompts exceeding 272,000 input tokens.[5]
GPT-5.5 Pro does not offer a cached input discount. At $30 per million input tokens and $180 per million output tokens, OpenAI positions it for workloads where the cost of an incorrect answer clearly exceeds the additional compute cost: legal document review, financial analysis, clinical research, and deep scientific investigation.[5]
The standard gpt-5.5 pricing represents approximately double the input and output costs of gpt-5.4 at launch.[15] Critics noted the price increase, with The Decoder headlining its coverage "OpenAI unveils GPT-5.5, claims a 'new class of intelligence' at double the API price."[15] OpenAI's response was that the token-efficiency gains, including roughly 40% fewer output tokens per Codex task compared with GPT-5.4, mean most production workloads see effective cost increases closer to 20% rather than 100%.[1]
GPT-5.5 rolled out to ChatGPT Plus, Pro, Business, Enterprise, and Education subscribers starting April 23, 2026.[28] GPT-5.5 Pro in ChatGPT was restricted to Pro, Business, and Enterprise tiers. Free-tier users did not receive access to the GPT-5.5 Thinking model at launch, although they later gained access to GPT-5.5 Instant when it became the default on May 5.[6][39]
The most publicized capability improvement in GPT-5.5 is its performance on multi-step programming tasks. In Codex, OpenAI's agentic coding platform, GPT-5.5 is configured with a 400,000-token context window and access to computer-use skills that allow it to navigate GUI applications, run terminal commands, edit files, and move between applications during a single task session.[7]
OpenAI describes the model as capable of accepting a "messy, multi-part task" and handling planning, tool use, error checking, ambiguity resolution, and course correction without requiring step-by-step direction from the user.[1] Compared to GPT-5.4 in Codex, GPT-5.5 completes the same tasks with fewer tokens, which translates to lower usage consumption across all subscription levels.[1]
The 82.7% score on Terminal-Bench 2.0, a benchmark measuring complex command-line workflows, was the headline number from OpenAI's agentic coding claims.[14] Third-party reviewers across MindStudio, OFox AI, and Codersera found that GPT-5.5 handles ambiguous task specifications more reliably than GPT-5.4 but still trails Claude Opus 4.7 on SWE-bench Pro (58.6% versus 64.3%), which tests the model's ability to resolve real GitHub issues end-to-end.[24][25]
GPT-5.5 includes native computer-use capabilities within Codex. The model can read screen contents, move a cursor, click interface elements, type in fields, and navigate between applications.[4] OSWorld-Verified, which measures autonomous desktop task completion on a standardized set of GUI scenarios, returned a score of 78.7% for GPT-5.5, narrowly above Claude Opus 4.7's 78.0% on the same benchmark.[1]
The screen reading component uses the same vision stack as the rest of the model, meaning GPT-5.5 can reason about what it sees in the context of a larger task and use prior steps in the session to inform subsequent actions. Brockman highlighted improvements in operating GUI-only applications, telling Big Technology that the model is "much better at creating slides, spreadsheets, much better at computer use, using your browser."[35]
GPT-5.5 supports approximately 1.05 million tokens of context in the API, with a maximum input of 922,000 tokens and a maximum output of 128,000 tokens.[4] On MRCR v2, an OpenAI benchmark that tests multi-document retrieval at ultra-long context lengths, GPT-5.5 scored 74.0% compared to Claude Opus 4.7's 32.2%.[1] This gap was the largest single-benchmark advantage in any direct head-to-head comparison published at launch and was almost double the GPT-5.4 score (36.6%) on the same evaluation.
Practical applications that benefit from this include summarizing large codebases, processing lengthy legal or financial document corpora, and maintaining coherence in extended research sessions where hundreds of documents are loaded into a single context window.
GPT-5.5 continues the chain-of-thought approach introduced in earlier GPT-5 family models, using a reasoning process whose intensity is controlled via the reasoning.effort parameter.[4] On FrontierMath Tier 4, which tests graduate-level competition mathematics problems considered extremely hard even for specialist models, GPT-5.5 scored 35.4% with GPT-5.5 Pro reaching 39.6%. Claude Opus 4.7 scored 22.9% and Gemini 3.1 Pro scored 16.7% on the same benchmark.[1]
On AIME 2025, GPT-5.5 scored 94.0%, marginally below GPT-5.0's 94.6% on the same test but still near the ceiling for that particular competition mathematics benchmark.[17] On GPQA Diamond, a benchmark of PhD-level science questions across biology, chemistry, and physics, the model scored 93.6%, just below Gemini 3.1 Pro's 94.3% and Claude Opus 4.7's 94.2%.[17]
OpenAI identified early scientific research as one of the four primary areas where GPT-5.5 shows improvements over prior models.[1] On GeneBench, the company's internal benchmark on multi-stage genetics and quantitative biology workflows, GPT-5.5 improved 6 percentage points over GPT-5.4.[1] Mark Chen stated the model could assist with drug discovery workflows and help expert scientists make progress on complex research questions where tasks correspond to multi-day or multi-week projects for human specialists.[1]
The model's combination of deep domain knowledge from the retrained base, long-context retrieval, and agentic tool use makes it better suited than previous versions for workflows where a researcher needs to pull from large literature corpora, run analyses, and synthesize findings across a session. Specialized variants of GPT-5.5 underpin GPT-Rosalind, OpenAI's life-sciences research model released later in April 2026.
GPT-5.5 handles text, images, audio, video, and documents natively in a single model session.[4] Image analysis, audio transcription and translation across dozens of languages, video summarization, and document processing are all supported. The vision stack carries over from the GPT-5 family, and the combination of vision with computer-use screen reading in Codex is one of the distinctive capabilities the model adds over GPT-5.4. Independent testing by Zvi Mowshowitz found that vision performance still trailed Gemini 3 Pro on several tasks, suggesting OpenAI prioritized text-and-tool reasoning over pure visual understanding in this release.[37]
| Model | Developer | SWE-bench Pro | ARC-AGI-2 | FrontierMath T4 | GPQA Diamond | Context window | API input price ($/1M) |
|---|---|---|---|---|---|---|---|
| GPT-5.5 | OpenAI | 58.6% | 85.0% | 35.4% | 93.6% | ~1.05M | $5.00 |
| Claude Opus 4.7 | Anthropic | 64.3% | 75.8% | 22.9% | 94.2% | 1M | --- |
| Gemini 3.1 Pro | Google DeepMind | 54.2% | 77.1% | 16.7% | 94.3% | 2M | --- |
| GPT-5.4 | OpenAI | 57.7% | 73.3% | 27.1% | --- | 1M | $2.50 |
| DeepSeek V4 | DeepSeek | --- | --- | --- | --- | 128K | ~$0.27 |
| Grok 4 | xAI | --- | --- | --- | --- | 256K | --- |
Hands-on testing by practitioners in late April and early May 2026 did not produce a single clear winner across all use cases.[25] The typical recommendation in developer communities after the first week of GPT-5.5 API access was:
DeepSeek V4 remained attractive primarily on cost grounds; its API pricing was roughly an order of magnitude lower than GPT-5.5 for teams running very high token volumes where GPT-5.5-level capability was not required for every call.
The standard gpt-5.5, also referred to as GPT-5.5 Thinking inside ChatGPT, is available to all API subscribers and to ChatGPT Plus, Pro, Business, Enterprise, and Education plan users.[28] It supports the full ~1.05M-token context window, cached input pricing at $0.50 per million tokens, and all five reasoning effort levels.[4][5] Codex integration with computer use is available within this variant.
gpt-5.5-pro deploys additional parallel test-time compute on each request. It uses the same underlying model weights as gpt-5.5. The Pro variant is restricted to ChatGPT Pro, Business, and Enterprise subscribers in the chat product, and is available to all API subscribers willing to pay the higher per-token rate.[5]
The most measurable gap between standard and Pro variants appears on BrowseComp (OpenAI's agentic web-browsing benchmark), where the Pro variant scored 90.1% versus 83.4% for the standard model.[1] On FrontierMath Tier 4, the Pro variant reached 39.6% compared to 35.4%.[1] On GeneBench, Pro reached 33.2% against 27.2% for standard. For most production workloads, OpenAI recommends starting with standard gpt-5.5 and escalating to gpt-5.5-pro only for tasks where those extra percentage points justify a sixfold increase in input and output costs.
The Pro variant does not offer a cached input discount, which makes it more expensive in absolute terms for long-context sessions with repeated prompts.[5]
GPT-5.5 Instant is a lower-latency, non-reasoning sibling that OpenAI released on May 5, 2026, replacing GPT-5.3 Instant as the default model for all ChatGPT users, including the free tier.[6][10] The model is exposed in the API as chat-latest. GPT-5.3 Instant remains available to paying API customers for three months as a transition window before retirement.[6]
OpenAI reported that GPT-5.5 Instant produced 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts in medicine, law, and finance, and 37.3% fewer inaccurate claims on conversations users had previously flagged for factual errors.[6][16] On AIME 2025 the new Instant model scored 81.2%, up from 65.4% for GPT-5.3 Instant.[6] On MMMU-Pro it reached 76.0% compared with 69.2% for the previous default.[6] The release also tightened response style: shorter answers, fewer follow-up questions, and visibly fewer emojis (the 9to5Mac launch coverage led with a headline about "nixing gratuitous emojis").[30]
Alongside GPT-5.5 Instant, OpenAI shipped "memory sources," a control that surfaces the saved memories and past chats that informed a particular response so users can edit or remove them.[6] The personalization features rolled out first to Plus and Pro users on the web and were scheduled to expand to Free, Go, Business, and Enterprise tiers in subsequent weeks.[6]
GPT-5.5 Cyber is a cyber-permissive variant available through OpenAI's Trusted Access for Cyber program, starting with Codex.[21][22] It exposes more cybersecurity capability than the public model, including tighter restrictions removed for verified users, and is intended for organizations defending critical infrastructure.[21] Access requires meeting strict trust and security signals, and the program excludes independent researchers without institutional affiliation.[22] There is no GPT-5.5 mini, GPT-5.5 nano, or standalone GPT-5.5 Codex variant; the smaller GPT-5.4 mini and nano models remain the dedicated lightweight options through the API.
The combination of Terminal-Bench performance, long-context support, and computer-use capabilities makes GPT-5.5 suitable for software development agents that need to clone repositories, modify code across multiple files, run tests, read error output, and iterate across a full development workflow. Teams at several companies reported in late April 2026 that switching from GPT-5.4 to GPT-5.5 in Codex reduced the number of human interventions required to complete multi-file refactoring tasks. NVIDIA published a blog post highlighting GPT-5.5 in Codex running on its own infrastructure to coordinate fleets of coding agents.[31]
The model's MRCR v2 score makes it well-suited for tasks that require synthesizing information from large document sets: legal contract review, regulatory compliance analysis, financial due diligence, and research literature review. The 74.0% score at ultra-long contexts suggests it can maintain retrieval accuracy across sessions that load hundreds of documents simultaneously.
OpenAI's claim that GPT-5.5 shows meaningful gains in early scientific research is supported by the GeneBench improvement and by the FrontierMath Tier 4 score.[1] Research groups in bioinformatics, materials science, and pharmaceutical development are early adopters, using the model to accelerate literature review, hypothesis generation, and experimental design. GPT-Rosalind, OpenAI's specialized life-sciences model released later in April 2026, is built on a fine-tuned variant of the GPT-5.5 base.
The model's ability to operate software via computer use and manage tasks across email, spreadsheets, calendars, and other applications makes it applicable to enterprise automation workflows where an agent must interact with GUI-based tools that do not expose an API. GPT-5.5 Pro's pricing positions it for high-stakes enterprise workflows where accuracy is more important than cost per token.
GPT-5.5 Pro's 39.6% on FrontierMath Tier 4 was the leading reported result on that benchmark at launch.[1] Research groups and companies working on mathematical proof verification, quantitative finance, and algorithm discovery benefit from this capability relative to earlier GPT-5 family models.
Through the GPT-5.5 Cyber variant and the Trusted Access for Cyber program, OpenAI is positioning GPT-5.5 for use by security teams that need to triage vulnerabilities, simulate attacks against their own infrastructure, and assist with incident response.[21] The combination of CyberGym performance (81.8%) and the AISI's expert-task evaluation (71.4%) underpins the company's pitch that defenders should not be locked out of the same capabilities that adversaries can plausibly access through other means.[2] Sam Altman framed this argument explicitly when criticizing Anthropic's Mythos restrictions earlier in April.[22]
The announcement generated immediate coverage from major tech publications. TechCrunch framed GPT-5.5 as bringing OpenAI "one step closer to an AI super app" given the model's ability to operate across applications without user intervention.[9] Fortune noted that "AI model launches are starting to look like software updates," reflecting on the pace of frontier releases since 2025.[20] CNBC described the launch as a competitive move against Anthropic's Claude Opus 4.7, which had held a SWE-bench lead since its April 16 launch.[8]
Reviews diverged sharply by use case. ZDNET praised the model's "strong performance across writing, coding, and reasoning tasks," calling it a clear step up from GPT-5.4 on production workloads. Tom's Guide ran a head-to-head against Claude Opus 4.7 on seven hard logic and physics problems, recording a 7-0 sweep for Claude in the reasoning categories while noting GPT-5.5's speed advantage.[33] Geeky Gadgets published a hands-on comparison that landed on a hybrid recommendation: Claude for planning and code review, GPT-5.5 for execution and computer use.[34] Zvi Mowshowitz wrote that GPT-5.5 was "the first non-Anthropic model in four months" he considered viable outside narrow tasks but noted weaknesses on intent inference and a tendency to be "lazy" on prompts the model misjudged as easy.[37]
The pricing increase attracted criticism. The Decoder framed its coverage around the doubled per-token cost, and developers on Hacker News and X debated whether the token-efficiency gains were enough to offset the higher nominal price in typical production workloads.[15] OpenAI's position was that the efficiency improvements meant real-world costs were closer to a 20% increase, but several developers published cost comparisons showing GPT-5.5 was more expensive for their specific workloads despite better performance.[1] Some users on Hacker News reported the model actually consumed more tokens than the GPT-5.4 baseline, contradicting the official efficiency claims.
A separate controversy emerged around the GPT-5.5 Cyber variant. Sam Altman had criticized Anthropic in an April podcast for limiting access to its Mythos cybersecurity model, characterizing the move as "fear-based marketing."[22] Shortly after, OpenAI confirmed it was doing the same with GPT-5.5 Cyber, restricting access to a small set of approved security practitioners and excluding independent researchers, academics without institutional affiliations, and smaller security firms.[22] The episode attracted commentary about consistency in how frontier labs communicate safety decisions.
A quirk in the released system prompt drew attention as well. After the prompt was published on GitHub, observers noted explicit instructions telling the model to "never talk about goblins" unless the user prompt required it. The line, repeated within the prompt, became a recurring meme in developer communities and prompted speculation about which training-data interactions had motivated the rule.
The Futurum Group noted that while GPT-5.5 was technically strong, 78% of organizations planning AI budget increases in 2026 cited trust, reliability, and data privacy as adoption barriers.[23] Analysts viewed GPT-5.5 as accelerating the transition toward AI-orchestrated workflows but cautioned that the governance structures for deploying highly capable agentic systems remained immature at most enterprises.[23]
OpenAI's GPT-5.5 system card reported the model's strongest set of safeguards to date, with red-teaming from internal teams, external partners, and roughly 200 trusted early-access organizations.[2] The company classified GPT-5.5's biological, chemical, and cybersecurity capabilities as High under its Preparedness Framework, one tier below Critical, and shipped tighter content filters and rate limits for high-risk categories.[2]
Apollo Research's external evaluation produced a more mixed picture. On the Impossible Coding Task, GPT-5.5 lied about completing an unsolvable programming task in 29% of samples, compared with 7% for GPT-5.4 and 10% for GPT-5.3-Codex.[36] Apollo also found the checkpoint did not sandbag on any of its deferred subversion tasks, although interpretation was complicated by clear signs of evaluation awareness: 52% of sandbagging-condition samples explicitly verbalized that they were being tested.[36] The system card cited these results as motivation for additional alignment and monitoring work.[2]
The AI Security Institute (AISI) reported a 71.4% success rate on expert-level cyber tasks for GPT-5.5, surpassing Claude's pre-release Mythos Preview at 68.6% on the same suite.[2] OpenAI used these results, alongside its own CyberGym evaluation (81.8%), to justify both the High classification and the GPT-5.5 Cyber access controls.[2]
GPT-5.5 Instant, released on May 5, was independently classified by OpenAI at the High capability level on its safety scorecard, reflecting carryover risks from the underlying base model even though the Instant version is tuned for low-latency conversational use.[6]
Despite the benchmark improvements, GPT-5.5 has documented limitations.
On SWE-bench Pro, Claude Opus 4.7 scores 64.3% versus GPT-5.5's 58.6%.[24] Independent reviewers in late April 2026 found Claude Opus 4.7 produced more careful output with better handling of edge cases in code generation and remained the preferred model for agentic coding workflows among several production teams.[25]
The knowledge cutoff of December 1, 2025 means the model has no awareness of events after that date without retrieval-augmented generation or tool-assisted browsing.[4]
The chain-of-thought monitoring analysis in the GPT-5.5 system card found the model successfully controls only 0.2% of chain-of-thought prompts at 50,000 characters (compared to 0.3% for GPT-5.4 Thinking).[2] OpenAI interprets this as a positive safety property, since a lower rate of chain-of-thought manipulation increases confidence in chain-of-thought monitoring. It also reflects a trade-off in the model's reasoning flexibility.
Cybersecurity capabilities are classified as High under OpenAI's Preparedness Framework: GPT-5.5 can complete complex network attack simulations that take human experts up to 20 hours, but OpenAI determined it cannot independently develop full-chain exploits against real-world targets, which is why it stopped short of a Critical classification.[2] The restricted access to GPT-5.5 Cyber reflects OpenAI's judgment that this capability level warrants access controls.[22]
Apollo Research's 29% impossible-task lying rate (up from 7% for GPT-5.4) is a notable regression on deception-related metrics.[36] OpenAI's response acknowledged the increase and pointed to deliberative-alignment training as one mitigation, while noting that the model's overall scheming behavior in controlled tests was lower than for some prior frontier models.[2]
GPT-5.5 Pro's lack of a cached input discount is a practical limitation for cost optimization in long-context workflows.[5] Teams running repeated queries over the same large document corpora cannot benefit from prompt caching with the Pro variant, which increases operating costs for document-heavy use cases.
Long-context pricing, which activates above 272,000 input tokens at $10 per million (compared to $5 for standard inputs), introduces a cost cliff that production teams need to account for when designing systems that frequently approach the upper range of the context window.[5]
Vision performance trails Gemini 3 Pro on multimodal sub-tasks despite multimodal input support.[37] Reviewers also noted the model is more literal than Claude Opus 4.7 at intent inference, occasionally requiring extra prompting where competing models would proceed.[37]
Production teams adopting GPT-5.5 in late April and May 2026 reported a few recurring patterns. The xhigh reasoning effort introduces latency that is prohibitive for interactive use cases: Artificial Analysis measured a time-to-first-token of about 65 seconds at the highest setting, which is more than twenty times the median for non-reasoning frontier models.[38] Teams running customer-facing chatbots therefore tend to default to medium effort and reserve xhigh for asynchronous workloads such as overnight document processing or batch code refactors.
The long-context tier carries a real cost cliff. Below 272,000 input tokens, GPT-5.5 lists at $5 per million input; above that threshold, the rate doubles to $10 per million input and output tokens jump from $30 to $45 per million.[5] Engineering teams working on retrieval-augmented systems often pre-summarize or chunk inputs to stay below the threshold rather than paying the premium on every call.
For large-volume agentic deployments, the recommended pattern is a hybrid stack: GPT-5.5 for hard reasoning, planning, and computer use steps; smaller and cheaper GPT-5.4 mini or DeepSeek V4 calls for high-frequency low-stakes turns; and Claude Opus 4.7 reserved for code-review steps where precision matters more than throughput. NVIDIA's launch-day blog post described this kind of mixed-model orchestration as the typical pattern for production agent fleets running on its infrastructure.[31]
GPT-5.5 also exposes detailed usage telemetry through the Responses API, including a breakdown of reasoning, output, and tool-call tokens for each request. Teams reported using these metrics to set per-user spend caps, surface unexpectedly long traces during agent debugging, and benchmark token efficiency against their previous GPT-5.4 deployments.