GPT-5.5: Benchmarks, Safety Classification, and Availability

OpenAI's latest model, GPT-5.5, matches GPT-5.4 in per-token latency but performs at a higher level. It also uses fewer tokens to complete the same Codex tasks. 

Improvements in efficiency

There are several ways to talk about the new efficiency gains:

• Per-token latency: This first metric refers to how fast each token is generated. It is measured in something like milliseconds per token. The takeaway from this release: GPT-5.5 produces each individual token at roughly the same speed as GPT-5.4.
• Token efficiency: This second metric refers to how many tokens the model needs to finish a task. A more efficient model reaches the same answer in fewer tokens, which could be the result of fewer reasoning steps, fewer retries, and/or less backtracking. 

So the combined claim is: Each token comes out just as fast as before, and the model needs fewer of them to finish the job. Taken together, tasks now complete faster overall and cost less.

We should also say that the per-token price went up. We'll get to this in the pricing section later on. 

Stronger long-context reasoning

A long context window is only useful if the model can actually use all of it. GPT-5.4 technically supported long contexts, but its performance fell apart past ~128K tokens. Ask it to reason over something truly long — a full codebase, a long contract, hours of transcripts — and performance sinks at the far end of the window.

GPT-5.5 holds up past 128K, past 256K, and all the way out to 1M. We'll get into the specific numbers in the benchmark section below, but the headline is that this is the first OpenAI model where the whole context window is genuinely usable.

GPT-5.5 Benchmark Results

Here is what I think is the most interesting model eval from the release. It is a measure of long-context performance. These benchmarks evaluate an underlying question: If I hand you a massive amount of text, can you still reason over it?

The MRCR needle tests are essentially a "needle in a haystack" test, hence the name. The model is given a long document with specific pieces of info - "needles" - hidden inside it, then asked to retrieve them - the needles. We see the test done multiple times with different context sizes. The higher the score, the more the model actually used the full context of what it was given.

I don't want to forget the Graphwalks tests. They are actually harder than MRCR. In those tests, the model has to traverse a graph structure embedded in the context (BFS means here breadth-first search). It's a test of reasoning over long context. (

A quick scan of the results tells us that GPT-5.5 is better at searching and reasoning over long-context info. A second glance at the table tells us something more interesting: GPT-5.4 falls apart past ~128K, and GPT-5.5 doesn't. That's the real story.

GPT-5.5 Availability

GPT-5.5 is rolling out in all the usual places starting today. Plus, Pro, Business, and Enterprise users get GPT-5.5 in both ChatGPT and Codex, and Pro, Business, and Enterprise users also get GPT-5.5 Pro in ChatGPT 

OpenAI says GPT-5.5 and GPT-5.5 Pro are coming very soon through the Responses and Chat Completions APIs with a 1M-token context window. This is not par for the course. In other releases, OpenAI gave access to the API at the same time. 

GPT-5.5 Safety and Cyber Access

OpenAI classified GPT-5.5 as High on both biological/chemical and cybersecurity capabilities under its Preparedness Framework. That's the reason the API is delayed. OpenAI says serving a High-classified model at API scale requires additional safeguards it's working through.

Two practical consequences for users:

• First, standard users will hit stricter classifiers on cyber-adjacent requests. OpenAI acknowledges that some of these refusals "may be annoying."
• Second, OpenAI introduced Trusted Access for Cyber, a program where verified defenders can apply at chatgpt.com/cyber to get fewer restrictions on legitimate security work. 

This is the first time OpenAI has formally tiered cybersecurity access based on who's asking. Cybersecurity has been in the news a lot recently. We wrote about cybersecurity issues in our article on Claude Opus 4.7, which is a scaled-down sibling of Anthropic's internal-only Mythos Preview.

GPT-5.5 Pricing

I said earlier that there were gains in efficiency. We need to also say the price per token has gone up. 

GPT-5.5 (API, when available)

GPT-5.5 in the API will be priced at $5 per 1M input tokens and $30 per 1M output tokens.

GPT-5.5 Pro (API, when available)

GPT-5.5 Pro in the API will be priced at $30 per 1M input tokens and $180 per 1M output tokens.

Conclusion

GPT-5.5 is an incremental capability update paired with a non-incremental policy update. The model is faster, cheaper per task, and meaningfully better at long-context work. We can see these claims clearly in the benchmark results. The release is also the first time OpenAI has formally tiered cybersecurity access based on who's asking, which is interesting development for the industry as a whole.

If you're in ChatGPT or Codex, you have access to the new model today. If you're an API user, you're waiting around, which, as we noted, is not how OpenAI usually does things, but is probably coming soon.