OpenAI GPT-5.4: Pricing, Benchmarks & What Developers Actually Need to Know
OpenAI just shipped GPT-5.4, and it's a big deal. A million-token context window, native computer use, and benchmark numbers that put it ahead of most things out there. But is it worth the price bump? Let's break it down.
TL;DR
- -Price: $2.50 input / $15.00 output per 1M tokens (standard). There's also a Pro tier at $30/$180.
- -Context: 1.05M tokens in, 128K out. Biggest context window in the GPT lineup.
- -Standout: Native computer use (beats human performance on desktop nav), 57.7% on SWE-Bench Pro.
- -Catch: 2x input pricing above 272K tokens. Longer responses on average. Health benchmarks slightly worse than 5.2.
What's Actually New in GPT-5.4?
If you've been using GPT-5.2, here's what changed: OpenAI basically merged their Codex and GPT lines into one model. GPT-5.4 is the "frontier model for complex professional work" in their words. In practice, that means better coding, computer use built right in, and a context window that finally crossed the million-token mark.
The model snapshot is gpt-5.4-2026-03-05 with a knowledge cutoff of August 2025. It supports text and image inputs, streaming, function calling, structured outputs, and adjustable reasoning effort (from none to "xhigh").
One thing worth noting — GPT-5.4 tends to write longer responses. Average response length is 3,311 characters vs 2,676 for 5.2. That's roughly 24% more output, which matters when you're paying per token.
GPT-5.4 Pricing Breakdown
Here's the deal with pricing. There are two tiers, and a sneaky context-length multiplier you should know about:
| Model | Input / 1M | Cached / 1M | Output / 1M |
|---|---|---|---|
| GPT-5.4 (<272K) | $2.50 | $0.25 | $15.00 |
| GPT-5.4 (>272K) | $5.00 | $0.50 | $22.50 |
| GPT-5.4 Pro (<272K) | $30.00 | — | $180.00 |
| GPT-5.4 Pro (>272K) | $60.00 | — | $270.00 |
| GPT-5.2 (previous) | $1.75 | $0.175 | $14.00 |
The jump from GPT-5.2 to 5.4 is about 43% more on input ($1.75 to $2.50) and 7% more on output ($14 to $15). Not terrible for what you get — the context window alone went from 400K to over 1M tokens.
But here's the catch most people will miss: if your prompt exceeds 272K input tokens, pricing doubles on input and goes up 1.5x on output. So that 1M context window isn't cheap to actually use in full. A 500K-token prompt would cost $5/1M input rate instead of $2.50.
The cached input pricing is solid though — $0.25/1M is a 90% discount. If you're doing repeated calls with similar system prompts, prompt caching will save you a lot.
Benchmark Numbers That Matter
I'm going to skip the cherry-picked benchmarks and focus on the ones that actually tell you something useful:
Real-world coding tasks. This is genuinely good — means it can handle actual GitHub-style issue fixes.
Professional knowledge across fields. Shows broad capability, not just coding.
Beats human performance (72.4%) at navigating desktop applications. First OpenAI model with native computer use.
Handles complex visual reasoning tasks. Useful if you're working with diagrams, charts, or screenshots.
The coding improvements are real. They're claiming 33% fewer hallucinated facts and 18% fewer outright wrong answers compared to GPT-5.2. Plus, the new Codex /fast mode gives you 1.5x faster token throughput, and the tool search feature cuts token usage by 47% with the same accuracy.
Where it falls short: health-related benchmarks actually dropped slightly (62.6% on HealthBench vs 63.3% for 5.2), and it scores lower on some cybersecurity tasks compared to the dedicated Codex model. Not a dealbreaker, but worth knowing if those are your use cases.
GPT-5.4 vs Claude Opus 4.6 vs Gemini 3.1 Pro
The question everyone's asking — how does it compare? Here's a quick side-by-side on what matters:
| GPT-5.4 | Claude Opus 4.6 | Gemini 3.1 Pro | |
|---|---|---|---|
| Input / 1M | $2.50 | $5.00 | $2.00 |
| Output / 1M | $15.00 | $25.00 | $12.00 |
| Context | 1.05M | 200K | 1M |
| Max Output | 128K | 32K | 65K |
| Computer Use | Yes | Yes | No |
| Image Input | Yes | Yes | Yes |
Price-wise, GPT-5.4 sits between Gemini (cheapest) and Claude (most expensive). The value proposition really depends on what you need. For pure coding work, GPT-5.4's SWE-Bench Pro score is strong. For long-form reasoning and writing, Claude still has an edge in many people's experience. For cost- sensitive batch processing, Gemini's hard to beat.
The computer use capability is a real differentiator. At 75% on OSWorld (beating human 72.4%), this is the first GPT model where you can genuinely use it for desktop automation tasks. Anthropic has had this with Claude for a while, but OpenAI catching up here opens interesting possibilities.
Should You Switch from GPT-5.2?
Honestly? It depends. If you're doing coding tasks, the accuracy improvements and the merged Codex capabilities make it worth the 43% input price bump. The tool search feature alone (47% token savings) could offset some of that cost increase.
If you need the big context window — say you're processing long documents, codebases, or legal contracts — the jump from 400K to 1.05M is massive. Just watch out for the 272K pricing threshold.
If you're running a chatbot or doing simple Q&A, sticking with GPT-5.2 or even GPT-5-mini ($0.25 input) probably makes more sense. The improvements in 5.4 are mostly about professional and coding workloads.
One timeline note: OpenAI says GPT-5.2 Thinking will be discontinued on June 5, 2026. So if you're using that variant, you've got three months to migrate.
Real Cost Examples
Let's put real numbers on typical use cases:
Want to calculate costs for your specific use case? Try our free cost calculator — it has GPT-5.4 pricing baked in already.
The Bottom Line
GPT-5.4 is a solid step up from 5.2, especially for coding and professional workloads. The million-token context window and native computer use are genuine differentiators. The pricing is fair for what you get, but that 272K threshold and the Pro tier costs can add up fast.
For most developers, the standard GPT-5.4 tier is the sweet spot. Use prompt caching aggressively, stay under 272K when you can, and you'll get a meaningfully better model without blowing your API budget.