GPT-5.5 Review: Benchmarks, Pricing, Codex Impact, and Early Reactions (2026)

What exactly has changed in GPT-5.5?

To conclude, GPT-5.5 is closer to “A model that pushes the work to the end” than to “Models who are better at chatting.” OpenAI described GPT-5.5 as a model that sequentially performs code writing and debugging, online research, data analysis, document and spreadsheet creation, and software manipulation (Source: OpenAI GPT-5.5 Announcement). This is a release that focuses on planning, using tools, checking, and trying again without requiring users to constantly manage the detailed steps.

Release date and distribution scope

The official announcement date of OpenAI is April 23, 2026. In Korean time, it can be viewed as the latest public model as of April 24, 2026. In ChatGPT, GPT-5.5 Thinking is sequentially distributed to Plus, Pro, Business, and Enterprise users, and GPT-5.5 Pro is provided to Pro, Business, and Enterprise users. Codex provides Plus, Pro, Business, Enterprise, Edu, and Go plans and uses 400K contexts (Source: OpenAI GPT-5.5 Announcement).

There are also things to be careful about. The API price has been revealed, but OpenAI only said that it will soon provide GPT-5.5 and GPT-5.5 Pro for the Responses API and Chat Completions API. In other words, as of April 24, 2026, ChatGPT·Codex distribution and general API provision must be viewed separately.

How is its role different from GPT-5.4?

If GPT-5.4 was a “relatively affordable pro working model,” GPT-5.5 is a one-level expensive frontier work model. The OpenAI pricing page also lists GPT-5.5 as the new intelligence class for “coding and professional work,” leaving GPT-5.4 as the cheaper option (Source: OpenAI API Pricing).

Why is it called “agent model”?

The message of GPT-5.5 is clear. It is not a model where you ask a question once and receive an answer, but rather an agent-type business model that handles multiple stages of work. TechCrunch also reported that OpenAI described GPT-5.5 as part of a super app strategy that combines ChatGPT, Codex, browser, and work automation (Source: TechCrunch).

This flow is also in line with the IDE-centered agent flow discussed in [Completely Conquering Claude Code] (/en/ai/claude-code-review). Model comparison is now moving from “Is your answer smart?” to “Can you use the tool to make actual changes?”

Where is GPT-5.5 stronger in the benchmark?

GPT-5.5’s strengths are most evident in terminal-based agents, web browsing, and long context searches. Conversely, in SWE-Bench Pro, which looks at actual GitHub issue resolution, Opus 4.7 is still ahead. So both “GPT-5.5 is the best” and “no difference” are half-interpretations.

Coding: Terminal-Bench wins by a landslide, SWE-Bench Pro is ambiguous

In the OpenAI official table, GPT-5.5 recorded Terminal-Bench 2.0 82.7%. It is higher than GPT-5.4 75.1%, Claude Opus 4.7 69.4%, and Gemini 3.1 Pro 68.5%. This benchmark is an indicator of planning, iteration, and tool coordination in complex command line tasks, so it fits well with agent environments such as Codex (Source: OpenAI GPT-5.5 Announcement).

But SWE-Bench Pro is different. GPT-5.5 is 58.6%, GPT-5.4 is 57.7%, and Claude Opus 4.7 is 64.3%. It’s hard to say “1st place overall in coding”. Solving single PRs, fixing complex code base bugs, and real-service refactoring require a head-to-head comparison with Opus 4.7, which will be covered in the next article. The existing summary can be viewed first in [Claude Opus 4.7 summary] (/en/ai/claude-opus-4-7).

Computer use and browsing: Getting closer to being a practical agent

OSWorld-Verified looks at the model’s ability to understand the screen and perform tasks in a real operating system environment. GPT-5.5 is 78.7%, slightly ahead of GPT-5.4’s 75.0% and Opus 4.7’s 78.0% (Source: OpenAI GPT-5.5 Announcement).

BrowseComp is focused on finding and synthesizing information on the web. The GPT-5.5 basic model is 84.4%, and the GPT-5.5 Pro is 90.1%. Opus 4.7 is 79.3%. In long-term research, data verification, and document creation-type tasks, GPT-5.5 is more persuasive. This point is also connected to How to reduce Claude Code tokens with Graphify. Even if the model’s long-reading ability improves, cost and accuracy can be further stabilized by using external memory and structured context.

Long context: jumps are large around 1M

The most notable figure is OpenAI MRCR v2 8-needle 512K-1M. GPT-5.5 is 74.0%, GPT-5.4 is 36.6%, and Opus 4.7 is 32.2%. In a test that required finding multiple needle-like pieces of information in the same long document, GPT-5.5 showed a nearly double jump (Source: OpenAI GPT-5.5 Announcement).

This figure is the basis for the use case “Put entire large code bases in and ask questions.” Of course, in an actual repo, dependencies, build logs, test results, and the latest file status must be included, so benchmark scores should not be directly translated into actual success rates. Still, the level of improvement compared to GPT-5.4 in the 1M context section is the most obvious advantage of this update.

Is the price really twice as expensive as GPT-5.4?

That’s true if you look at the price per token. GPT-5.5 is twice as expensive as GPT-5.4 for both input and output. However, in agent work, “Total tokens it takes to complete one task” is more important than the price per token.

Public API Pricing List

The OpenAI price page lists GPT-5.5 as “Coming soon” and suggests an input of $5.00, a cash input of $0.50, and an output of $30.00 based on 1 million tokens. GPT-5.4 costs $2.50 input, $0.25 cache input, and $15.00 output (Source: OpenAI API Pricing).

Here, GPT-5.5 Pro is in a completely different cost range. Output of $180 is a burdensome amount to spend on mass coding automation. It is correct to view it as a model that is limited to “Need to get hit one more time” tasks such as research, legal review, financial modeling, and scientific data interpretation where the cost of incorrect answers is high.

Double the unit price and the cost per job are different.

Artificial Analysis analyzed that although the price per token of GPT-5.5 is twice that of GPT-5.4, when running its own Intelligence Index, output token usage was reduced by about 40%, so the overall execution cost increase was only about 20% (Source: Artificial Analysis).

Things to watch out for when comparing costs

This calculation does not apply automatically to all users. Tasks that do not significantly reduce the output tokens, such as short questions, simple translations, and general chatting, may feel like they cost nearly twice as much. Conversely, in an agent loop where debugging, testing, browsing, and tool calls are repeated, costs are likely to be offset by reducing retries and lengthy intermediate explanations.

Should Codex users switch to GPT-5.5 right now?

If you use Codex for business purposes, it is worth testing. However, rather than changing it to the default for all tasks, it is better to route from “Long-term tasks that use a lot of terminal”.

Codex tasks that fit well with GPT-5.5

OpenAI explains that GPT-5.5 has become stronger for engineering work from Codex to implementation, refactoring, debugging, testing, and verification (Source: OpenAI GPT-5.5 Announcement). The Terminal-Bench 2.0 scores also support this explanation.

It is especially good to experiment starting with the next task.

What CodeRabbit Early Tests Say

CodeRabbit reported improved code review signals in its initial testing of GPT-5.5. In the screening benchmark, the expected issue discovery rate increased to 79.2% vs. 58.3%, and precision increased to 40.6% vs. 27.9%, while on the larger test set, it achieved 65.0% vs. 55.0% and 13.2% vs. 11.6%, respectively (Source: CodeRabbit).

However, this is the vendor’s own workload. Rather than saying “All code reviews are 30% better,” it is correct to view this as evidence that signal quality is likely to improve in code review products. Still, the observation that GPT-5.5 is more robust against small changes, actual failure causes, and verification loops than lengthy rewrites is a pretty important signal for Codex users.

Who is High Speed ​​Mode for?

OpenAI announced that Codex also provides GPT-5.5 fast mode. Token creation is 1.5 times faster but 2.5 times more expensive (Source: OpenAI GPT-5.5 Announcement). This mode is closer to “Reducing waiting time for long agent tasks” than to “Seeing the answer quickly.”

The recommendation criteria are simple.

Why are people’s reactions so different?

Initial reactions were clearly divided. The positive side refers to “perceived intelligence,” “Short and direct task handling,” and “Agency in Codex.” The negative side says “It falls behind Opus 4.7 in SWE-Bench Pro”, “the price went up”, and “xhigh mode is slow, but the perceived improvement is not clear”. There are reasons for both.

Positive reaction: It feels different than the benchmark.

The OpenAI announcement included reactions from early testers, including Dan Shipper and Pietro Schirano. The key point is that GPT-5.5 understands the form of the system, the cause of failure, and the scope of surrounding influences better than simply writing code (Source: OpenAI GPT-5.5 Announcement).

There was also a response on Reddit r/singularity: “The benchmark improvement is smaller than expected, but the experience of conversation and explanation is different.” An early review of r/OpenAI also felt strongly about infrastructure and workflow issues, but cited the habit of predicting actions instead of actual actions and the delay of xhigh mode as disadvantages (Source: r/singularity, r/OpenAI).

Negative reaction: I see areas where Opus cannot be defeated.

The r/codex release thread immediately talked about SWE-Bench Pro. It is pointed out that GPT-5.5 is 58.6%, but Opus 4.7 is 64.3% (Source: r/codex). This criticism is valid. “Agent-type coding” that OpenAI talks about and “Ability to fix actual PR at once” that users expect are not exactly the same indicators.

The price reaction is also similar. A response was posted on r/OpenAI that the GPT-5.5 API is twice as expensive as GPT-5.4 (Source: r/OpenAI). The token efficiency claims made by OpenAI and Artificial Analysis are important, but if users focus on short chats or simple API calls, the experience may be closer to “It just got expensive.”

How to read reactions now

Community response is not a benchmark. In particular, the response on the day of launch is a mix of account rollout, plan limitations, UI status, prompt habits, and expectations compared to previous models. So, in this article, I only see the response as a hint of “Where do users find value?”

To summarize, it is like this. GPT-5.5 is not an all-purpose model that will immediately impress you, but rather a model that gradually shows differences in tasks that are left to you over a long period of time. Conversely, just looking at a short question and a single code patch can easily lead to a response like “Why has this become so expensive?”

Where do GPT-5.5 and Opus 4.7 diverge?

The subject of the next article is this comparison. Here, let’s just draw the conclusion first. GPT-5.5 is strong in terminal, browsing, long context, and cost-effectiveness, while Opus 4.7 is strong in SWE-Bench Pro, MCP Atlas, high-density code review, and self-verification.

GPT-5.5 is advantageous

Areas where GPT-5.5 is clearly ahead are Terminal-Bench 2.0, BrowseComp, CyberGym, and long context MRCR. In particular, the fact that MRCR 512K-1M recorded 74.0% is qualitatively significant compared to GPT-5.4 (Source: OpenAI GPT-5.5 Announcement).

Opus 4.7 has an advantage

Opus 4.7 has SWE-Bench Pro 64.3%, which is higher than GPT-5.5’s 58.6%. MCP Atlas is also 75.3% for GPT-5.5 and 79.1% for Opus 4.7 based on OpenAI announcement (Source: OpenAI GPT-5.5 announcement). As already discussed in [Claude Opus 4.7 summary] (/en/ai/claude-opus-4-7), Opus 4.7’s strength is the flow of verification and reporting during long coding sessions.

So, rather than picking a single winner, it is better to break it down by task. GPT-5.5 is put first in the OpenAI ecosystem’s Codex work, terminal automation, and long context research, and Opus 4.7 is put in deep codebase patching and PR unit verification.

Points to look out for in the next comparison article

The next article will deal directly with “GPT-5.5 vs Claude Opus 4.7.” There are three key questions:

Adding cheaper alternatives makes the situation more complicated. If budget is more important, you should also look at GLM 5.1 Review and Kimi K2.6 Complete Analysis.

What are the limitations and precautions?

GPT-5.5 is clearly stronger, but it also has dangerous misunderstandings. API access status, hallucinations, safe rejections, and differences between benchmarks and practice must be viewed separately.

APIs have different pricing and availability

The first thing to check is the API. OpenAI said that it will soon provide gpt-5.5 and gpt-5.5-pro to the Responses API and Chat Completions API, but it does not say “Any developer can use it right now” based on the release date (Source: OpenAI GPT-5.5 Announcement).

Among blogs and community posts, you can see posts written as if the API has already been opened. When planning commercial deployment, you should double-check the OpenAI pricing page and model page.

The hallucination rate indicator must be viewed dispassionately.

Artificial Analysis revealed that GPT-5.5 ranked first in the Intelligence Index by 3 points. At the same time, AA-Omniscience pointed out that although the accuracy is high, the hallucination rate is 86%, which is higher than 36% for Opus 4.7 Max and 50% for Gemini 3.1 Pro Preview (Source: Artificial Analysis).

These numbers are the result of specific benchmarks. This does not mean that you will hallucinate 86% of all knowledge questions. However, “The ability to say you don’t know what you don’t know” should be seen as a sign that it is still a task. When using GPT-5.5 as a research partner, it is essential to check sources, search for counterexamples, and verify links to the original text.

Safety guardrails can create friction

The OpenAI System Card summary explains that GPT-5.5 has undergone pre-deployment evaluations related to cybersecurity and biology, external red teams, and feedback from approximately 200 early access partners (Source: OpenAI GPT-5.5 System Card). The OpenAI announcement also treated cyber, biological and chemical capabilities as high based on the Preparedness Framework, and stated that a separate access path is provided to users for trusted defense purposes.

It’s not just a good thing. Teams working on defensive security may experience unnecessary rejection. The reason OpenAI has a separate access path called Trusted Access for Cyber ​​is to reduce this friction. Therefore, when introducing GPT-5.5 for security purposes, not only model performance but also account trust signals, access rights, and audit logs must be designed together.

Which team should use it first?

The first teams to test GPT-5.5 are those with the “AI actually executes and verifies something” workflow. For simple chatbots, summaries, and short customer response, GPT-5.4 mini or existing models may be more reasonable.

Team to test first

If any of the following apply to you, GPT-5.5 is likely worth your while.

A team that can still wait

Conversely, the team below can move slowly.

Order of introduction into practice

FAQ: Frequently asked questions about GPT-5.5?

Conclusion: Is GPT-5.5 worth using now?

GPT-5.5 is worth a try. It’s worth testing out right away, especially for codex, terminal automation, long context research, and working documentation. However, it should not be taken as “Beats Opus 4.7 in all coding” or “Even if the price is double, the actual cost is always the same.”

In the next article, we will compare GPT-5.5 and Claude Opus 4.7 head-to-head. The key ones are SWE-Bench Pro, Terminal-Bench, MCP Atlas, BrowseComp, Long Context, and Real Cost. The tentative conclusion at this stage is simple. GPT-5.5 is the strongest working model in the OpenAI ecosystem, and Opus 4.7 still has not easily given up the “Throne of Codebase Patches”.