We’re working on a chatbot platform with a multi-agent setup (LangGraph-based)
involving a primary assistant, a secondary assistant, and a routing layer. Our system is optimized for low latency, predictable outputs, and strong
instruction following, rather than deep reasoning. GPT‑4o has
been working really well for us so far in terms of speed, routing accuracy, and overall stability.
With GPT‑4o nearing end of life, we’re now forced to evaluate migration options.
Gpt-5 series models have serious latency issues since they are reasoning models, and we do not need so much of reasoning. if we switch to these models than the users of our application will be pissed off.
Would love to hear how others are approaching this — especially if you
have similar latency-sensitive, non-reasoning workloads.
While gpt-4o might seem long-in-the-tooth, it is not deprecated on the API. gpt-4o-2024-08-06 and its fine-tunings are still working with no timeline trigger pulled on them, as well as gpt-4o-2024-11-20.
OpenAI recently added something to their deprecations page that only used to be a guessable pattern:
Generally available models: At least 6 months (notice)
If you do want to try a different model, the first go-to would be gpt-4.1. It is a non-reasoning model that has the same parameter controls, and has better developer instruction-following (and a bit less expensive, also)
OpenAI might want to recommend gpt-5.5 as a replacement for everything deprecated, but a tripling of the price (or more with reasoning) and a willingness to write at long length on user command is not a direct substitute.
You can send the reasoning.effort API parameter with value “none” to gpt-5.2, gpt-5.4, gpt-5.5 - and not get an optimum quality output for what you pay. However, you will receive streaming output tokens with short initial latency.
Instead, start with gpt-5.2, and “low” effort, and I think you will find that the thinking dialog that you can throw up in your user interface will give the impression that someone’s request is actually being backed by work and reasoning that is not too frustrating a wait, usually faster than you’d receive any displayable periodic reasoning summary.
Non-reasoning workloads? That’s usually when you don’t want the AI examining itself and inputs too deeply.
Thanks for the context, this is helpful. One clarification from our side is that we are operating on Azure OpenAI, not the public OpenAI API. In the Azure portal, GPT‑4o models (including gpt‑4o‑2024‑08‑06 and gpt‑4o‑2024‑11‑20) are explicitly marked with a retirement timeline around end of September / early October 2026, which means we need to plan migration ahead of time rather than relying on “still works” status. Azure publishes and enforces these lifecycle dates.
I also want to clarify what we mean by non‑reasoning workloads, since that may not have come through clearly. In our system, the model is primarily used for fast routing and intent/entity classification in a chatbot. We explicitly do not want the model to deeply analyze inputs, reflect on its own reasoning, or generate long-form explanations. Any additional reasoning overhead directly translates into higher latency and less predictable outputs, which hurts our use case.
Because of that, while we understand that GPT‑5.x models can be tuned with reasoning.effort, they still behave fundamentally differently from GPT‑4o as fast executors, even at “none” or “low”. GPT‑4.1 is closer behaviorally, but given its similar retirement window, it doesn’t materially reduce long-term migration risk for us.
So the core issue we’re trying to solve is not whether GPT‑5.x can reason well, but whether there is a stable, low-latency successor to GPT‑4o for execution- and routing-heavy workloads within the Azure lifecycle constraints.
Here’s a low-latency API request, a “router” type of categorizer, taking potentially long input but only giving a JSON, with the response message I obtained.
One wrinkle since you’re on Azure: the public API only retires the 2024-05-13 snapshot (and the gpt-4o alias) on 2026-10-23, the 08-06 and 11-20 snapshots have no timeline pulled yet. but Azure runs its own retirement schedule, and if the portal says Sept/Oct, that’s the date to plan against, not the public page. two different clocks, and Azure’s is the one ticking over your deployment.
On the latency side: the successors in the mini/nano classes (5.6-terra, 5.6-luna) are the ones to eval for a routing layer, but eval them on your router prompts specifically. Vendor numbers won’t tell you how a model behaves when the job is “long input, tiny JSON out.” the failure mode to watch is reasoning models deciding your router, deserves deep thought.