Source: GPT-5 improvements won't be comparable to the leaps in performance of earlier models, such as between GPT-3 in 2020 and GPT-4 in 2023
The Information :
The Information
Context & Ripple Effects
The report extends an emerging pattern: sources had already said the GPT-4-to-Orion gain was markedly smaller than the GPT-3-to-GPT-4 jump in the earlier Orion comparison. That makes GPT-5’s expected trajectory less a single-product surprise than a test of whether frontier-model progress is becoming more incremental.
The coverage is not uniformly negative on capability: GPT-5 was also reported to improve practical software-engineering performance against earlier OpenAI models and Claude Sonnet 4 in a recent coding-focused assessment. The key distinction is between useful task-level gains and a broad generational leap.
First-order effects
Expectations for GPT-5 shift from a GPT-4-scale discontinuity toward narrower, workload-specific improvements; OpenAI must demonstrate value through concrete capabilities rather than model-name novelty.
Enterprise evaluators and developers get a stronger reason to benchmark GPT-5 against existing models on their own coding and production tasks, rather than assume a wholesale replacement cycle.
Second-order effects
Rival model providers can compete more effectively on targeted performance and deployment fit if the leading model’s aggregate advance is modest; practical coding results become a more important comparison point.
Buyers may place greater weight on integration, reliability, and the cost of serving models—areas captured by reported practical engineering gains—when raw frontier progress is less decisive.
Third-order effects
If successive frontier releases continue to deliver diminishing visible gains, AI competition could shift from periodic breakthrough models toward sustained execution in inference, product distribution, and application-specific optimization.
That would make the smaller GPT-4-to-Orion improvement part of a broader transition: scaling remains important, but its commercial payoff increasingly depends on converting incremental capability into dependable workflows.
The trend: Frontier AI is moving from headline-generating generational jumps toward a contest over measurable task performance and the infrastructure and distribution needed to monetize it.
Ok, just so I'm tracking, we're shoveling hundreds of billions of dollars into hardware buildout, while violating countless copyrights, to fuel decelerating models that are also going to achieve “AGI” in the next 2-3 years? [embedded post]
@michae.lv
Michael Veale on bluesky
looks like we're heading to superintelligence through RAGs then, thankfully the web is still working well [embedded post]
@carljanderson
@carljanderson on bluesky
Can we start discussing this concept of “diminishing returns” yet? [embedded post]
@priorsconfirmed
@priorsconfirmed on bluesky
Exponential llm improvements are over but the spending on gpus is still groeing exponentially [embedded post]
@semianalysis_
@semianalysis_ on x
The Information came out with some reporting on GPT-5 this morning. Let's talk about it and discuss the technical details of the model. thread by analyst @aj_kourabi
@semianalysis_
@semianalysis_ on x
We think that GPT-5 is a hybrid model, able to switch between thinking and non-thinking modes. TI reports that the model is able to produce high quality answers without a lot of tokens. This will make it similar to Claude 4 in both regards. Being very token efficient for a giv…
@semianalysis_
@semianalysis_ on x
We remain very optimistic about the capabilities of reasoning models. Scaling Reinforcement Learning works and drives strong performance gains. OpenAI and Google both recently scored Gold on the IMO, and progress here was mostly driven by scaling up RL and test time compute. W…
@semianalysis_
@semianalysis_ on x
TI also reports that the o3 model revealed in December is distinct from the model available in ChatGPT. While we do not think the performance losses are drastic, this does explain some regressions in areas like hallucinations when compared to o1. (Source: OpenAI System Card) [ima…
@semianalysis_
@semianalysis_ on x
Currently, most of the rubrics used for LLMs to judge responses are written by human experts, though we think this too will be automated soon. We are excited for the GPT-5 release and look forward to a successful launch by the OpenAI team.
@shakeelhashim
Shakeel on x
“The improvements won't be comparable to the leaps in performance of earlier GPT-branded models, such as the improvements between GPT-3 in 2020 and GPT-4 in 2023, one of the people said.”
@chatgpt21
Chris on x
Sam Altman calls GPT 5 a 'system that integrates a lot of our technology.'" The article says the new model will • merge the o-series reasoning core with the regular chat stack so users no longer pick models • rely on reinforcement learning that includes a universal [image]
@amir
Amir Efrati on x
I remember when I was when OpenAI released the ARC benchmark results for o3. It was insane. But internally, researchers saw those gains dissipate when they distilled the model into chat form. [image]
@amir
Amir Efrati on x
GPT-5 is good. But model performance gains are still slower than in past years and this year has been a technically challenging one for OpenAI researchers. The inside story here... [image]
r/OpenAI
r on reddit
Inside OpenAI's Rocky Path to GPT-5
r/singularity
r on reddit
One of the takeaways from The Information's article “Inside OpenAI's Rocky Path to GPT-5”: “GPT-5 will show real improvements over its predecessors …