GPT-5.5 vs. Claude and Gemini: A Simple Benchmark Comparison

GPT-5.5, ChatGPT's new flagship model, is aiming to become a market leader

OpenAI has introduced GPT-5.5 as the new flagship model for ChatGPT and the API. It’s designed not only to answer questions, but also to perform real-world tasks: communication, programming, research, and agent-based tasks.

GPT-5.5 comes in a standard version for everyday use and a more powerful GPT-5.5 Pro version. The Pro version is more expensive but is better suited for complex tasks that require deep reasoning, working with tools, and multi-step actions.

OpenAI’s announcement emphasizes three areas: working with information across various professions, independently performing tasks on a computer, and programming through long, multi-step chains.

OpenAI's baseline benchmarks for GPT-5.5

In its technical note, OpenAI reports that GPT-5.5 achieves state-of-the-art performance on several new agent-based benchmarks. On GDPval, which covers 44 professions and tests how well an agent performs clearly defined knowledge-based tasks—ranging from consulting and law to engineering—the model achieves a score of 84.9%. On OSWorld-Verified, which measures the ability to work autonomously within a real operating system with applications, windows, and files, the result is 78.7%. And on the Tau2-bench Telecom, which simulates complex multi-turn customer service scenarios without prompt tuning, GPT-5.5 achieves 98.0%.

OpenAI also provides additional professional metrics. Let’s summarize them in a table for clarity.

These figures confirm OpenAI’s main claim—that GPT‑5.5 is designed for real-world business tasks and tool management, not just for artificial tests.

Independent benchmarks, math, code, reasoning

In the MMLU-Pro academic test, Claude Opus 4.7 leads with 92.8%, followed by GPT-5.5 Pro (92.1%), Claude Sonnet 4.6 (91.5%), and Gemini 3.1 Pro (91.0%)—the gap among the top performers is minimal. On the ARC-AGI-2 abstract reasoning test, Claude Opus 4.7 is again in first place with 81.5%, ahead of GPT-5.5 Pro (78.4%) and Gemini 3.1 Pro (77.1%). However, on the AIME 2025 math test, GPT-5.5 Pro takes the lead with 96.7%—the best result on this metric—while Claude Opus 4.7 scores 95.2% and Gemini 3.1 Pro scores 93.5%. Among the lighter models, Claude Haiku 4.5 (ARC-AGI-2 33.4%, AIME 64.8%) and the outdated GPT-4.5 (ARC-AGI-2 23.5%, AIME 58.4%) lag significantly behind across the set of reasoning metrics.

Source: Local AI Master Benchmark

The benchmark results show that no single model holds absolute dominance in the high-end segment. Claude Opus 4.7 appears to be the most consistent leader in academic and abstract reasoning tasks, particularly in MMLU-Pro and ARC-AGI-2, while GPT-5.5 Pro performs best in mathematics and the AIME 2025 competitive tasks. Gemini 3.1 Pro stays close to the leaders and remains a competitive option, especially when considering large context.

The practical choice depends not only on the highest percentage in the table but also on the use case. For complex reasoning and all-around reliability, it makes more sense to consider Claude Opus 4.7 or GPT-5.5 Pro; for math problems, GPT-5.5 Pro is the best choice; and for a balance of price, quality, and long context, Claude Sonnet 4.6 and Gemini 3.1 Pro stand out significantly. Lighter and older models remain useful for simple tasks and cost savings, but they already lag noticeably behind the new flagship models in reasoning metrics.

Programming and agent-based work in the terminal

In programming and DevOps, GPT-5.5 delivers strong results, especially in tasks that require performing multi-step actions in the terminal. On Terminal-Bench 2.0, the model scores 82.7%, which is the best result among the models in the table. For comparison, GPT-5.4 scores 75.1%, Claude Opus 4.7 scored 69.4%, and Gemini 3.1 Pro scored 68.5%.

On SWE-Bench Pro, the picture is less clear-cut. GPT-5.5 scored 58.6%, slightly outperforming GPT-5.4 (57.7%) and Gemini 3.1 Pro (54.2%). However, Claude Opus 4.7 leads here with a score of 64.3%, so GPT-5.5 cannot be called the leader of this test. The internal Expert-SWE test shows a more noticeable improvement, with GPT-5.5 scoring 73.1% compared to 68.5% for GPT-5.4.

To summarize this section, GPT-5.5 appears particularly strong in terminal and multi-step DevOps tasks, where it confidently outperforms GPT-5.4, Claude Opus 4.7, and Gemini 3.1 Pro. In SWE-Bench Pro-level tasks, the model also outperforms GPT-5.4 and Gemini, but trails Claude Opus 4.7; therefore, it is more accurate to speak not of complete dominance, but of a strong position among the top group of coding models.

Source: Scores OpenAI

Head-to-Head: GPT-5.5 vs. Claude Opus 4.7 and Gemini 3.1

Several comparative studies show that GPT-5.5 has indeed strengthened OpenAI’s position, but the picture is not one of complete dominance across all categories. In agent-based tasks and tool usage, it tends to come out ahead; on Terminal-Bench 2.0, GPT-5.5 scores 82.7% compared to 69.4% for Claude Opus 4.7 and 68.5% for Gemini 3.1 Pro. On OSWorld-Verified, the gap nearly disappears, with GPT-5.5 at 78.7% versus 78.0% for Claude Opus 4.7. In BrowseComp web navigation, GPT-5.5 Pro achieves the best result at 90.1%, while Claude Opus 4.7 scores 79.3% and Gemini 3.1 Pro scores 85.9%.

In programming, the situation is more mixed. GPT-5.5 leads confidently on Terminal-Bench 2.0 and the internal Expert-SWE benchmark, scoring 73.1% compared to 68.5% for GPT-5.4. However, on SWE-Bench Pro Public, Claude Opus 4.7 is already in the lead with 64.3%, while GPT-5.5 scores 58.6% and Gemini 3.1 Pro scores 54.2%. Therefore, it is more accurate to speak not of an unconditional advantage for GPT-5.5 in coding, but of a strong lead in terminal and engineering tasks, while Claude maintains its advantage in SWE scenarios.

GPT-5.5 and GPT-5.5 Pro perform particularly well in math and academic tests. On FrontierMath Tier 1–3, GPT-5.5 Pro scores 52.4%, GPT-5.5 scores 51.7%, Claude Opus 4.7 scores 43.8%, and Gemini 3.1 Pro scores 36.9%. On the more challenging FrontierMath Tier 4, GPT-5.5 Pro’s lead is even more pronounced: 39.6% compared to 22.9% for Claude Opus 4.7 and 16.7% for Gemini 3.1 Pro. However, on the GPQA Diamond dataset, it is not GPT-5.5 but GPT-5.4 Pro that leads with 94.4%, nearly on par with Gemini 3.1 Pro (94.3%), Claude Opus 4.7 (94.2%), and GPT-5.5 (93.6%).

GPT-5.5 also performs strongly on abstract thinking tasks, though it isn’t always in first place. On ARC-AGI-2 Verified, it scores 85.0%, outperforming GPT-5.4 Pro (83.3%), Gemini 3.1 Pro (77.1%), and Claude Opus 4.7 (75.8%). However, on the ARC-AGI-1 Verified dataset, Gemini 3.1 Pro takes the lead with 98.0%, while GPT-5.5 scores 95.0%, GPT-5.4 Pro scores 94.5%, and Claude Opus 4.7 scores 93.5%. This shows that GPT-5.5’s advantage is more pronounced on the newer and more complex ARC-AGI-2, rather than across all reasoning tests.

In long-context tasks, the results are mixed. On Graphwalks, Claude Opus 4.7 performs better on some tasks, such as BFS 256k f1 (76.9% vs. 73.7% for GPT-5.5) and parents 256k f1 (93.6% vs. 90.1%). At the same time, GPT-5.5 is noticeably stronger on OpenAI MRCR v2 with large windows; for example, 87.5% on 128K–256K versus 59.2% for Claude Opus 4.7, and 74.0% on 512K–1M versus 32.2% for Claude Opus 4.7. Therefore, it is not possible to identify a single overall winner here; GPT-5.5 performs better on MRCR-like tests, while Claude excels at graph-based tasks.

In professional and applied tasks, the distribution of leadership is also uneven. On GDPval, GPT-5.5 scores 84.9%, higher than GPT-5.5 Pro (82.3%), Claude Opus 4.7 (80.3%), and Gemini 3.1 Pro (67.3%). In OfficeQA Pro, GPT-5.5 also leads with 54.1%, compared to 43.6% for Claude and 18.1% for Gemini. However, on FinanceAgent v1.1, Claude Opus 4.7 achieved the highest score at 64.4%, while GPT-5.5 scored 60.0% and Gemini 3.1 Pro scored 59.7%. In cybersecurity, GPT-5.5 outperforms Claude on CyberGym, with 81.8% versus 73.1%, and outperforms GPT-5.4 on internal CTF challenges, with 88.1% versus 83.7%.

The Artificial Analysis Intelligence Index deserves special mention. In the graph, GPT-5.5 takes first place with a score of about 60 points, ahead of Claude Opus 4.7 and Gemini 3.1 Pro Preview, which are both around 57 points. This confirms the general trend: GPT-5.5 scales better with higher computational power and more reasoning tokens, but its lead over its closest competitors remains moderate rather than overwhelming.

Overall, GPT-5.5 and GPT-5.5 Pro most often outperform other models in agent-based, terminal, and web tasks, as well as in mathematics and certain professional scenarios. Their strengths are particularly evident on Terminal-Bench 2.0, BrowseComp, FrontierMath, ARC-AGI-2, GDPval, and OfficeQA Pro. At the same time, Claude Opus 4.7 retains significant advantages on SWE-Bench Pro, certain long-context tasks, FinanceAgent, and MCP Atlas, where it achieves a score of 79.1% compared to 75.3% for GPT-5.5 and 78.2% for Gemini 3.1 Pro.

Gemini 3.1 Pro does not emerge as the clear leader in most comparisons but remains competitive in certain areas. It achieves the best result on ARC-AGI-1 Verified (98.0%), is close to the leaders on GPQA Diamond, and performs well on BrowseComp with 85.9%. Therefore, the final conclusion must be nuanced: GPT-5.5 sets a new benchmark for many practical and reasoning scenarios; Claude Opus 4.7 remains a strong competitor in coding, finance, and long-context tasks; and Gemini 3.1 Pro retains its value as a powerful general-purpose model with standout peak performance in certain areas.

Gemini 3.5 Flash turned out to be tens of times cheaper than GPT 5.5 and Claude Opus 4.7 in terms of token cost

A new comparative cost table shows that the price of working with AI models for large volumes of queries can differ not by percentages, but by tens or even hundreds of times. According to the data in the image, Gemini 3.5 Flash costs approximately 0.10–0.10–0.15 per 1 million input tokens and 0.40–0.40–0.60 per 1 million output tokens. For comparison, GPT 5.5 is estimated at 10–10–15 per input and 30–30–50 per output, while Claude Opus 4.7 is estimated at 15–15–18 and 75–75–90, respectively.

The main difference lies in the cost per output token, which often accounts for the bulk of expenses in real-world AI scenarios, such as text generation, chatbot operations, document analysis, and customer support automation. The difference in cost per output token between Gemini 3.5 Flash and Claude Opus 4.7 is approximately 100–150 times. At the same time, all three models support caching, which helps reduce costs for repetitive queries.

For companies with high-volume AI processes, choosing a model becomes not only a matter of quality but also of economics. If a workflow generates 10 million output tokens per month, Gemini 3.5 Flash will cost about 5, while Claude Opus 4.7 might cost 5, and Claude Opus 4.7 could cost 750–$900. This price gap makes the cheaper models particularly attractive for high-volume tasks where speed and cost are more important than maximum reasoning depth.

The most convenient way to use GPT-5.5 is through unitool.ai

A powerful model is half the battle; the other half is avoiding vendor lock-in and overpaying. Our service, unitool.ai, is a one-stop shop for top-tier neural networks, where GPT-5.5 sits alongside Claude Opus 4.7, Gemini 3.1 Pro, and other models mentioned in this article. You run the same query on different models, compare the responses and costs side by side, and choose the best one for your specific task—whether it’s coding, research, or a multi-step agent scenario—without having to sign up for a dozen different subscriptions.