GPT-5.5 vs GPT-5.4: What improves, what it costs, and when to migrate

GPT-5.5 looks like a meaningful improvement over GPT-5.4, but not the kind of leap that automatically justifies changing every production workflow. The clearest like-for-like signal from OpenAI is GDPval: GPT-5.5 is reported at 84.9%, while GPT-5.4 was published at 83.0%. At the same time, an external comparison reports the same 1M-token context window, similar per-token latency, and roughly double the per-token price for GPT-5.5.

Quick comparison

Area	Available evidence	Practical takeaway
Newer model	OpenAI’s API documentation lists GPT-5.5 as latest, and OpenAI’s research page places it after GPT-5.4.	GPT-5.5 is the newer documented line.
GDPval quality	GPT-5.5 scores 84.9%; GPT-5.4 was published at 83.0%.	A 1.9 percentage-point gain on this evaluation.
Shared benchmarks	LLM Stats reports GPT-5.5 ahead on 9 of 10 shared benchmarks.	A positive signal, but from an external source.
Context window	LLM Stats reports 1M tokens for both models.	Context size does not appear to be the main reason to switch.
Per-token latency	LLM Stats reports similar per-token latency.	Do not assume a clear speed win.
Price	LLM Stats lists GPT-5.5 at $5/$30 per 1M tokens versus $2.50/$15 for GPT-5.4.	Variable token cost is roughly twice as high.

The strongest improvement signal is quality

OpenAI describes GDPval as an evaluation of agents’ ability to produce well-specified knowledge work across 44 occupations. On that measure, OpenAI published 83.0% for GPT-5.4 and 84.9% for GPT-5.5.

That 1.9 percentage-point difference is the cleanest quantitative comparison available from the supplied sources. It is still worth reading carefully: it shows an improvement on one professional-work evaluation, not a guarantee that GPT-5.5 will outperform GPT-5.4 on every prompt, language, tool integration, or production task.

External benchmarks are encouraging, not decisive

The broadest direct comparison here comes from LLM Stats, which reports that GPT-5.5 improves on GPT-5.4 in 9 of 10 shared benchmarks. That supports the view that GPT-5.5 is generally more capable.

The caveat matters. The benchmark, pricing, context, and latency matrix is not an official OpenAI comparison; it is an external analysis. For teams paying by volume, the sensible conclusion is not “migrate immediately.” It is: run a controlled test on the work that actually matters to your product.

Context and latency do not appear to change the decision much

For many API users, quality is only one part of the story. Context length and response speed can matter just as much. According to LLM Stats, GPT-5.5 and GPT-5.4 both have a 1M-token context window and similar per-token latency.

That does not mean the two models will produce the same answers. It does mean the best available comparison does not point to a larger context window or a clear per-token speed advantage as the main reason to try GPT-5.5. The stronger argument is better output quality on harder tasks.

Price is the main reason to be cautious

The biggest trade-off is cost. LLM Stats lists GPT-5.5 at $5/$30 per 1M tokens, compared with $2.50/$15 for GPT-5.4. In that comparison, GPT-5.5 costs about twice as much per token.

That makes “cost per token” the wrong metric on its own. The more useful measure is cost per acceptable result. GPT-5.5 can be worth it if it reduces failed outputs, human review time, or costly retries. If GPT-5.4 already clears your quality bar, the higher price may not pay for itself.

Do not assume every capability improves equally

OpenAI introduced GPT-5.4 as a model with strong coding capabilities and improvements across tools, software environments, and professional tasks involving spreadsheets, presentations, and documents. That matters because many migrations are not decided by average benchmark scores. They are decided by one specific use case: coding, agents, document analysis, tool use, or production-ready deliverables.

The supplied sources do not provide an official breakdown showing exactly how much GPT-5.5 improves in each of those subareas. If your product depends on one of them, compare both models with your own prompts, files, tools, and acceptance criteria before changing the default.

When GPT-5.5 is worth testing first

Start with GPT-5.5 if your tasks resemble well-specified professional knowledge work, if mistakes are expensive, or if a small quality gain could reduce human review. It is also reasonable to evaluate GPT-5.5 if you want to build on the latest model line documented in OpenAI’s API materials.

Staying with GPT-5.4 makes sense if your application is highly cost-sensitive, if your current quality metrics are already being met, or if you were hoping for a clear advantage in context length or per-token latency that the external comparison does not show.

How to evaluate before switching

For a serious migration decision, test GPT-5.5 and GPT-5.4 on the same prompts, documents, tool calls, and scoring rules. Track at least five metrics:

• accepted-answer rate;
• critical error rate;
• human review time;
• end-to-end latency;
• cost per completed task.

The result may be a partial migration rather than a full one. You might route high-value, quality-sensitive tasks to GPT-5.5 while keeping GPT-5.4 for high-volume workflows where the quality difference does not justify the cost.

Verdict

GPT-5.5 appears to improve on GPT-5.4, but the upgrade looks incremental rather than automatic. The strongest evidence is the GDPval increase from 83.0% to 84.9%, reinforced by the external report that GPT-5.5 performs better on 9 of 10 shared benchmarks.

The migration case is not universal because LLM Stats also reports the same context window, similar per-token latency, and about twice the per-token price for GPT-5.5. The practical answer: test GPT-5.5 where quality directly affects outcomes, and keep GPT-5.4 where cost, context, or speed is the deciding factor.