The short answer
There is no reliable, all-purpose percentage for how much GPT-5 is better. The answer depends on the task and the comparison model: a coding benchmark, for example, cannot tell you how much better a model is at writing, research, or your production workload.
At launch, OpenAI reported scores of 74.9% on SWE-bench Verified and 96.7% on τ2-bench Telecom. These are scores on specific coding and tool-use benchmarks—not general improvement percentages. On SWE-bench Verified, OpenAI reported 74.9% for GPT-5 versus 69.1% for o3, a 5.8-percentage-point difference on that evaluation. It is not a GPT-4o comparison or an overall improvement rate. (OpenAI’s GPT-5 developer announcement)
Bottom line: GPT-5 showed strong results on particular coding and tool-use evaluations, but those scores do not establish how much better it is for every task or user.
What a small GPT-4o comparison found
A G2 author reported five hands-on comparisons using the same prompts: GPT-5 was preferred for creative writing and code explanation; GPT-4o was judged better on one factual-accuracy prompt; and the models tied on reasoning and context retention. These are the author’s results from a small set of tests, not a representative benchmark or an overall performance calculation. (G2’s comparison)
The details matter:
- Creative writing: The author preferred GPT-5’s response to a smartwatch-story prompt and reported it was closer to the requested length: 108 words versus 95 for GPT-4o, against a 120-word request. That is a subjective preference, and neither response met the requested length.
- Code explanation: Both models were reported to answer the coding prompt accurately. The author preferred GPT-5’s more detailed explanation—not a demonstrated correctness advantage.
- Factual accuracy: The author favored GPT-4o on one prompt, saying it used fresher data for one answer. The same account says GPT-4o also cited blogs despite an instruction not to. The article does not provide enough underlying responses and source links to independently validate this comparison.
- Reasoning: The author called the results a tie based on two prompts. This does not establish that the models are equivalent across reasoning tasks.
- Context retention: The author called this a tie, but the account names GPT-5.1, so the exact version compared is unclear.
Taken together, these examples show why “better” depends on the task. They do not show that GPT-5 wins every category—or how large any general advantage is.
Should you switch or use GPT-5 in production?
Use a test, then decide approach. Keep the model that already meets your quality bar unless a matched evaluation shows a worthwhile improvement on work that matters to you. For an API workload, compare the exact model IDs and versions you would deploy—not a ChatGPT label or a result from a different benchmark.
A practical comparison:
- Choose representative inputs, including difficult cases and common failure cases.
- Run candidate models with the same instructions, tools, and settings.
- Score outputs against task-specific checks, such as correctness, required format, instruction-following, and successful tool use.
- Compare operational measures that matter to your workload, such as latency and cost per successful task.
Illustration, not a GPT-5 result: If one model passes 80 of 100 test cases and another passes 75, the observed difference is five percentage points on that test set. It does not mean the first model is five percent better at every task.
Current ChatGPT and API status are different
OpenAI’s Help Center notice says ChatGPT Enterprise workspaces retained access to GPT-5 models through February 19, 2026. That notice does not, by itself, establish the exact ChatGPT retirement date or confirm current API availability. Separately, OpenAI’s API model listing labels gpt-5 and the gpt-5-2025-08-07 snapshot Deprecated. That label alone does not establish whether either is currently callable or give a shutdown date, so original GPT-5 is not a confirmed deployment candidate in this guide. Before considering it, check the live model and deprecation documentation; otherwise, evaluate a currently documented model for your workload. (OpenAI Help Center; GPT-5 API model page; API deprecations)
OpenAI’s model-selection guidance lists GPT-6 Astra for complex reasoning and coding, GPT-6.1 Sol to balance capability and cost, and GPT-6 Luna for cost-sensitive, high-volume workloads. Treat these as starting points, not a substitute for testing your own workload. (OpenAI API model guidance)
Takeaway: GPT-5’s benchmark scores and the small GPT-4o comparison show task-specific strengths and counterexamples, not a universal “X% better” result. For an API decision, compare exact candidate models on representative inputs and against your own quality and operational requirements.
Sources
Introducing GPT-5 for developers — OpenAI
ChatGPT 4 vs. 5: Here’s What I Learned After Testing Both
Retiring GPT-4o and other ChatGPT models — OpenAI Help Center