OpenAI just dropped GPT-5.4 on March 13, 2026, and I’ve been testing it for almost two weeks now. Here’s the thing — this isn’t just another incremental update. It’s a full-blown triple release, and it changes how developers think about picking the right model for their work.
What Exactly Is GPT-5.4?
GPT-5.4 comes in three separate variants: Standard, Thinking, and Pro. Each one targets a different use case, which is a first for OpenAI. Before this, you’d pick one model and just hope it worked well enough across your stack. Now you’ve got options, and honestly, the difference between them is bigger than I expected.
GPT-5.4 Standard is the everyday workhorse. Fast responses, solid accuracy, good for chat apps and content generation. Think of it as the replacement for what most people were doing with GPT-5.3.
GPT-5.4 Thinking is the reasoning-first model. It takes a beat before answering, works through problems step by step, and absolutely crushes complex logic tasks. If you’re building anything that needs math, code debugging, or multi-step analysis, this is the one.
GPT-5.4 Pro is the powerhouse. Maximum capability, highest accuracy, but it’ll cost you. This one’s built for enterprise teams who need the absolute best output quality and can handle the pricing.
The 1 Million Token Context Window Is Real
I know what you’re thinking — “another company claiming massive context windows.” But I tested it. GPT-5.4 supports up to 1.05 million tokens, which is the largest commercial context window OpenAI has ever shipped. There’s a catch though. You need to explicitly enable it by configuring model_context_window and model_auto_compact_token_limit in your API calls. Without those parameters, you’re stuck at the standard 272K window.
For practical purposes, 272K is already massive. But that million-token option? It’s a big deal for anyone working with entire codebases, long legal documents, or research papers where you need the model to hold everything in memory at once.
How Does It Stack Up Against Claude Opus 4.6?
The obvious comparison. GPT-5.4 scores roughly 80% on SWE-bench Verified, which puts it right in Claude Opus 4.6 territory. In my own testing, Claude still feels slightly better at following complex instructions with lots of constraints. But GPT-5.4 Thinking mode is genuinely impressive for pure reasoning tasks — it caught errors in my code that Claude missed in a couple of cases.
The real differentiator? GPT-5.4 has five discrete reasoning effort levels: none, low, medium, high, and xhigh. You can literally dial how hard the model thinks. That kind of control is something Claude doesn’t offer yet, and it matters when you’re optimizing for speed versus accuracy in production.
The Computer Use API Changes Everything
Now here’s where it gets interesting. GPT-5.4 includes a Computer Use API — the model can see screens, move cursors, click elements, type text, and interact with desktop apps programmatically. OpenAI basically took what Anthropic pioneered with Claude’s computer use and built their own version.
I tested it with a simple workflow: opening a spreadsheet, entering data, running formulas, and saving. It worked. Not perfectly — it stumbled on a dropdown menu once — but the fact that it works at all through an API is wild. This opens up automation possibilities that were science fiction two years ago.
Pricing Breakdown
Let me break this down because it matters for anyone budgeting API costs:
GPT-5.4 Standard: $2.50 per million input tokens, $15 per million output tokens. That’s competitive with what we saw from GPT-5.3, maybe even slightly cheaper per capability point.
GPT-5.4 Pro: $30 per million input, $180 per million output. Yeah, it’s expensive. But if you’re using Pro, you probably already have the budget for it.
One thing to watch: regional processing endpoints carry a 10% surcharge. So if you’re routing through specific geographic endpoints, factor that into your costs.
Should You Switch?
If you’re on GPT-5.3, upgrading to Standard is a no-brainer. Better output, similar pricing, more features. The Thinking variant is worth testing if you’re doing anything involving code, math, or multi-step reasoning. Pro? Only if you’ve got enterprise money and you need every last drop of quality.
The real question is whether this pulls people away from Claude or Gemini. My honest take — it doesn’t replace either. It gives you another strong option. And in 2026, having choices is better than being locked into any single provider.
What I’d love to see next is OpenAI making that million-token context the default instead of gating it behind special config. That would genuinely change the API experience for everyone, not just power users who know where to find the settings.
