GPT-5.0 to 5.5: Which ChatGPT Model Wins?

OpenAI’s GPT-5 family grew from a 400K-token baseline to 1M-token agentic models, with GPT-5.5 now leading benchmarks.

OpenAI has shipped six GPT-5 variants in less than nine months, and the differences are large enough to matter in real projects. The latest, GPT-5.5, arrived on April 23, 2026 and posts 93.6% on GPQA Diamond, 82.7% on Terminal-Bench 2.0, and 78.7% on OSWorld-Verified.

If you only remember one thing, remember this: GPT-5.0 was the baseline, GPT-5.1 made the system faster, GPT-5.2 pushed reasoning harder, GPT-5.3 cut cost, GPT-5.4 added computer use, and GPT-5.5 is the current top model. That is a lot of movement in a short window, and it changes how developers should pick models for chat, coding, research, and agent workflows.

How the GPT-5 family changed so quickly

The speed of this rollout is the real story. OpenAI did not ship one model and let it age quietly; it kept splitting the family into specialized versions for speed, reasoning, cost, and agentic work. That means “best ChatGPT model” is no longer a single answer. It depends on whether you care about price, latency, long context, tool use, or benchmark strength.

GPT-5.0 launched on August 7, 2025 as a unified system with a fast base model and a deeper reasoning layer called GPT-5 Thinking. A router chose the mode automatically, which removed the old ritual of manually switching between chat and reasoning models. For most users, that was the first big quality-of-life improvement in the family.

• GPT-5.0: unified routing, 400K input context, 128K output context
• GPT-5.1: adaptive reasoning, same price as GPT-5.0, faster on easy prompts
• GPT-5.2: first to cross 90% on ARC-AGI-1
• GPT-5.4 and GPT-5.5: 1M-token API context and computer-use workflows

Where each model actually differs

GPT-5.1 did not aim to be smarter in a dramatic way. It aimed to be more efficient. OpenAI’s adaptive reasoning system lets the model spend less compute on easy prompts and more on hard ones, which is why simple tasks can run 2 to 3 times faster than the standard mode. That matters more than it sounds, because speed changes how often people use a model during a workday.

GPT-5.2 is the one that pushed reasoning into a new bracket. It hit 100% on AIME 2025 math, more than 90% on ARC-AGI-1 in Pro mode, and 80.0% on SWE-Bench Verified. Those numbers tell a simple story: this was the model for hard problems, especially when accuracy mattered more than cost.

“The model is a significant leap in intelligence, and our most capable model yet,” OpenAI said in its GPT-5 launch announcement.

That quote matters because it shows how OpenAI framed the family from the start: not as one monolithic model, but as a stack of trade-offs. Once that framing is in place, the rest of the releases make sense. GPT-5.3 cut hallucinations and price, GPT-5.4 added desktop control, and GPT-5.5 raised the ceiling again.

Why GPT-5.4 changed agent workflows

GPT-5.4 is where the family became useful in a more operational sense. Its native computer use lets it click through interfaces, run commands, verify output, and loop through a build-run-verify-fix cycle. On OSWorld-Verified, it scored 75.0%, above the measured human baseline of 72.4%.

That is a meaningful line to cross. It means the model is not just answering questions or writing code snippets; it can interact with software like an operator. For teams building agents, that opens up workflows that used to require a human sitting in the loop for every step.

• OSWorld-Verified: 75.0% for GPT-5.4 vs 72.4% human baseline
• SWE-Bench Pro: 57.7% for GPT-5.4, up from 55.6% for GPT-5.2
• FrontierMath: 47.6% for GPT-5.4, up from 40.3% for GPT-5.2
• Tool search cut token usage by 47% in tool-heavy workflows

There is also a practical pricing wrinkle. GPT-5.4’s 1M-token context window is API-only, and OpenAI charges extra once a request crosses 272K input tokens. So while the number sounds generous, the economics still push teams to think carefully about when they use the full window.

Why GPT-5.5 is the model most teams will notice

GPT-5.5 is the model that makes the family feel complete. It beats GPT-5.4 on the benchmarks that matter for knowledge work and coding, including 93.6% on GPQA Diamond, 82.7% on Terminal-Bench 2.0, and 78.7% on OSWorld-Verified. Its Pro version goes further, with 90.1% on BrowseComp and 39.6% on FrontierMath Tier 4.

That said, the jump comes with a price jump too. GPT-5.5 API pricing is $5 per 1M input tokens and $30 per 1M output tokens, while GPT-5.4 sits at $2.50 and $15. If you are building a product with heavy inference volume, GPT-5.5 is the model you reserve for hard queries, not the default for everything.

For comparison, GPT-5.3 Instant is still the bargain model in the family at roughly $0.30 per 1M input tokens and $1.20 per 1M output tokens. It is a much better fit for everyday writing, support, and search-backed answers than for deep reasoning. That price gap is large enough that model routing matters as much as model quality.

What developers should pick right now

If you are building with the GPT-5 family, the choice is mostly about workload shape. GPT-5.3 is the sensible default for cost-sensitive, high-volume tasks. GPT-5.2 still makes sense when reasoning quality matters more than latency. GPT-5.4 is the one to use for agentic software work, especially if your app needs to operate across desktop tools or long sessions.

GPT-5.5 is the model to test when you want the best overall performance and can tolerate the price. That is especially true for research assistants, coding copilots, and enterprise workflows where a wrong answer costs more than a few cents in tokens. If you want a compact comparison with more context on OpenAI releases, our ChatGPT pricing guide is a useful companion read.

One more detail matters: OpenAI has already said GPT-5.2 Thinking is being retired on June 3, 2026, which is a reminder that model families are now moving targets. If you are shipping product features on top of these APIs, you need fallback logic and a plan for version changes, because the model you test today may not be the one you get next quarter.

What comes after GPT-5.5

OpenAI has not shipped GPT-6 yet, but the direction is already visible. The next step is likely to focus on persistent memory, longer-running autonomous agents, and better control over multi-step work. If that happens, the big question will not be whether the model can answer more questions. It will be whether it can keep state, remember goals, and complete work without losing the thread halfway through.

For now, the practical takeaway is simple: do not choose a GPT-5 model by headline benchmark alone. Pick the cheapest model that can handle your failure mode, then move up only when the task actually needs more reasoning, more context, or computer control. That is the difference between using AI as a demo and using it as infrastructure.