GPT-5.4 Review (2026): Is It Still Worth It vs GPT-5.6?

How we assess: every model here is judged on the same rubric — real-world reliability, price per token, benchmark evidence, and how it holds up against current rivals. See our Editorial Policy for the full method. Last updated: September 2026.

What GPT-5.4 actually is

Let me be direct, because the version numbers move fast and it is easy to lose the plot. GPT-5.4 is the model OpenAI shipped on March 5, 2026, across ChatGPT (as “GPT-5.4 Thinking”), the API, and Codex, with smaller mini and nano versions arriving on March 17. Its headline upgrades over the 5.2 generation were native computer use, a much bigger context window, and a real drop in factual errors. It was, for about two months, the best general model OpenAI offered.

The reason I keep flagging the date is that in AI, six months is a long time. Since GPT-5.4, OpenAI has released GPT-5.5 and then, on July 9, 2026, GPT-5.6 with its Sol, Terra, and Luna tiers. So reviewing GPT-5.4 in September 2026 is less “is this good” and more “is there any reason to still pick it,” which is the question I will actually answer. If you want the current pricing landscape across the whole field, our explainer on how AI pricing and credits work is the companion piece to this one.

Who this is for

GPT-5.4 is worth your attention if you are a developer or team that already built on it earlier this year and now you are wondering whether to keep it or move. It is also relevant if you specifically need a pinned, unchanging model — regulated workflows, reproducible research, or a product where “the model silently changed” is a bug, not a feature.

It is not the pick if you are starting something new today. In that case you are choosing between paying GPT-5.4 prices for older capability or paying the same for GPT-5.6 Terra and getting more. I will show you exactly where that trade-off sits below.

GPT-5.4 at a glance

An honest point-by-point walkthrough

Reasoning and reliability

This is GPT-5.4’s best event. The roughly 33% cut in factual errors versus GPT-5.2 is not a rounding-error improvement — it is the kind of change you feel in daily use, with fewer confidently wrong answers. Independent reviewers echoed it, praising the low hallucination rate specifically. Scores like 83.0% on GDPval (professional-grade tasks) and 73.3% on ARC-AGI-2 back that up. Verdict: genuinely trustworthy for research and knowledge work, and this is the reason people who adopted it tend to stay.

Coding

Good, with an asterisk. On SWE-Bench Pro it lands at 57.7%, which is respectable, but on Terminal-Bench 2.0 it actually falls behind the specialized GPT-5.3-Codex (75.1% vs 77.3%). Translation: for chatty code help it is fine, but for hardcore agentic coding, a Codex-tuned model or the newer 5.6 will serve you better. Verdict: a capable generalist coder, not the specialist. [ADD YOUR EXPERIENCE: drop in how GPT-5.4 handled a real repo task you ran — language, size, and whether it one-shotted it.]

Computer use

The 75.0% OSWorld-Verified score is the flashy number, and jumping from 5.2’s 47.3% is legitimately big. GPT-5.4 can drive a desktop — click, type, navigate — not just talk about it. But temper the hype: in shipping products the feature is gated, it is slower than a person at plenty of tasks, and you cannot leave it unsupervised on anything that matters. Verdict: a real capability and a preview of where things are going, but not yet a hands-off employee.

Context and long documents

Up to 1M tokens sounds limitless, and for feeding whole codebases or long document sets it is great. Just know the fine print: the standard serving limit is 272K tokens and anything above that bills at roughly double. So the 1M headline is real but expensive at the top end. Verdict: excellent for large context, as long as you watch the 272K pricing cliff.

Price

At $2.50 in and $15 out per million tokens, GPT-5.4 was fairly priced at launch. The problem is not the number, it is the comparison: GPT-5.6 Terra now charges the identical price for better output. Verdict: fine in isolation, hard to justify next to its own successor.

What no one else tells you about GPT-5.4

Here is the contrarian truth the launch-day hype cycle skips: the version number is marketing, and your output-token bill is the real story. Everyone fixates on whether 5.4 beats 5.2 on a leaderboard. Almost nobody mentions that at $15 per million output tokens, an agentic workflow that generates a lot of text will quietly cost you far more than the same task on a cheaper tier — and that GPT-5.6’s Luna tier does many jobs at $1 in / $6 out. Pick the tier that matches the job, not the biggest number in the name.

The second thing: “native computer use” is the most over-sold phrase in this whole category. A 75% benchmark makes it sound like the model runs your laptop for you. In practice it is supervised, rate-limited, and slower than you at routine clicking. It is a real advance and a bad reason to buy on its own. Judge GPT-5.4 on its reasoning reliability, which is excellent, not on the demo-reel automation.

And the third, the one that actually decides this review: staying on an older model can be the smart move, but only on purpose. If you tuned prompts and evals against GPT-5.4 and it works, chasing 5.6 for a few benchmark points is churn you may not need. Pinning a known-good model is a legitimate engineering choice. Drifting on an old model because you never looked up is not.

How GPT-5.4 compares in September 2026

The head-to-heads fill in the rest of the picture: see GPT-5.4 versus Claude Opus 4 for writing and reasoning, Gemini 3.1 versus GPT-5.4 for the Google comparison, and DeepSeek V4 versus GPT-5.6 if raw cost is your priority. Deciding between consumer plans instead? Our ChatGPT Plus versus Claude Pro breakdown is the one to read.

Who should use it, and who should skip it

Use GPT-5.4 if you already ship on it, your evals pass, and a stable pinned model is worth more to you than a modest benchmark bump. It remains reliable, low-hallucination, and perfectly capable for research, writing, and general coding.

Skip GPT-5.4 if you are starting new work. Reach for GPT-5.6 instead — Terra for the same price with better results, Luna if you are cost-sensitive, Sol if you need the frontier. There is little reason to begin a fresh project on a model two releases old at full price.

The honest limits

GPT-5.4 has been superseded twice, so you are paying current-model prices for prior-generation capability. Its output tokens are expensive for verbose or agentic use. Its 1M context carries a 2x pricing cliff above 272K tokens. Its computer use, while benchmarked well, is supervised and slower than the marketing implies, and it trails a specialized Codex model on hard terminal coding. None of that makes it bad — it makes it a model whose moment has passed unless you have a specific reason to hold it.

Frequently asked questions

Is GPT-5.4 still worth it in 2026?

It is still a genuinely capable model, but it is no longer the one to reach for by default. GPT-5.4 launched in March 2026, and by September 2026 OpenAI has shipped GPT-5.5 and GPT-5.6. The catch is that GPT-5.6 Terra costs the same as GPT-5.4 through the API — $2.50 per million input tokens and $15 per million output — while scoring higher. So GPT-5.4 is worth it mainly if you have already validated your app on it and value stability over the newest benchmarks.

When was GPT-5.4 released?

OpenAI released GPT-5.4 on March 5, 2026, launching it the same day in ChatGPT (as GPT-5.4 Thinking), the API, and Codex. The smaller GPT-5.4 mini and nano followed on March 17, 2026. That makes it roughly a six-month-old model as of September 2026, which in AI terms is two releases back.

How much does GPT-5.4 cost?

Through the API, GPT-5.4 is $2.50 per million input tokens, $0.25 for cached input, and $15 per million output tokens. The heavier GPT-5.4 Pro runs $30 input and $180 output. In ChatGPT, GPT-5.4 Thinking is included for Plus, Team, and Pro subscribers, GPT-5.4 mini reaches free-tier users, and nano is API-only.

What is the GPT-5.4 context window?

Up to 1 million tokens through the API and Codex, though the standard serving limit is 272,000 tokens and any request above that is billed at roughly double the normal rate. Maximum output is 128,000 tokens. Inside ChatGPT the usable context is smaller and unchanged from GPT-5.2.

Is GPT-5.4 good at coding?

It is solid, not class-leading. GPT-5.4 scores about 57.7% on SWE-Bench Pro, but on Terminal-Bench 2.0 it actually trails the specialized GPT-5.3-Codex (75.1% versus 77.3%). For serious agentic coding you will likely prefer a Codex-tuned model or the newer GPT-5.6, which pushes coding scores higher.

What is ‘computer use’ in GPT-5.4?

Computer use means the model can operate a desktop — move a cursor, click, type, and navigate apps — rather than just chat. GPT-5.4 scores 75.0% on the OSWorld-Verified desktop benchmark, a big jump from GPT-5.2’s 47.3%. It is impressive in tests, but in real products the feature is gated, slower than a human at many tasks, and needs supervision.

Does GPT-5.4 hallucinate less than older models?

Yes. OpenAI reports about a 33% reduction in factual errors versus GPT-5.2, and reviewers singled out its lower hallucination rate as a real strength. It is not perfect — testers still noted occasional prompt-following slips — but on factual reliability it was a clear step up from the 5.2 generation.

GPT-5.4 vs GPT-5.6: which should I use?

For most new work, GPT-5.6. Its balanced Terra tier matches GPT-5.4’s API price ($2.50/$15) while scoring higher across reasoning, coding, and computer use, and the family adds parallel-agent ‘ultra’ mode and programmatic tool calling. Choose GPT-5.4 only if you are pinned to it for stability, reproducibility, or a workflow you have already tuned and tested.

Is GPT-5.4 available on the free ChatGPT plan?

Only the smaller GPT-5.4 mini reaches free-tier ChatGPT users. The full GPT-5.4 Thinking model requires a Plus, Team, or Pro subscription, and GPT-5.4 Pro is limited to Pro and Enterprise accounts. If you are on the free plan, you are getting a lighter version, not the flagship.

What are the main downsides of GPT-5.4?

Three stand out: it has been superseded twice, so you are paying current-model prices for older capability; its output tokens at $15 per million add up fast in long or agentic workloads; and its 1M context has a pricing cliff above 272K tokens. It is a good model that time and OpenAI’s own release cadence have moved past.