from Openai’s failed marketing to performance, the essential is elsewhere

GPT-5 is a family of models with several arbitrating variants between efficiency and reasoning for complex tasks. Furthermore, An upstream router aims to orient each request towards the most suitable variant -. Consequently, even links several models – by optimizing cost, latency and quality.

• GPT-5 innovation is less a technological rupture than recasting of already existing systems. Meanwhile,
• In this regard. Nevertheless, the Marketing choice – notably the transition to version 5 – does not reflect the level of innovation which historically accompanied these launches. Nevertheless, GPT-3.5 launched Chatgpt and allowed large-scale use of transformers. For example, GPT-1 introduced reasoning models and has established a new dimension of calculation scale to inference. In openai’s failed marketing performance, essential addition, GPT-4O has moved the innovation border to unified real-time multimodality (text, vision and audio).
• Thus. For example, it is necessary to read the launch of GPT-5, not as the establishment of a new border in the transition to the scale of the calculation, but as a strategic overhaul aimed at increasing the usefulness, efficiency and adoption of mass in real uses. Meanwhile,
• The exit tokens are 15 times cheaper and generated about 2 to 4 times faster than those of GPT-4.5 when it was launched . Meanwhile,

The progress of GPT-5 is played elsewhere than in the transition to the quantity of calculation.

• Unlike the GPT-2 → GPT-3 → GPT-4 transitions. each marked by a factor of 100 in terms of pre-training calculation, GPT-5 does not operate such a jump. GPT-5 improves efficiency by Tokens (that is to say that it is openai’s failed marketing performance, essential able to converge more quickly towards an. equivalent performance level solution) compared to the previous generation of model. On Swe-Bench Verified. a benchmark measuring the ability of an agent to solve code problems, GPT-5 is about three times more efficient than O3 .

The launch of GPT-5 thus highlights the future trajectory of AI: the race for general artificial intelligence. cannot ignore economic realities.

• GPT-5 indeed illustrates how AI follows a technological progression curve in accordance with the economic logic. of the deployment of large-scale artificial intelligence. The only gross performance is no longer the immediate objective of the laboratories.
• Beyond the computing power. the progress of AI is guided by a multi-factory approach, while laboratories also attach importance to other levers, such as usability (cost/performance ratio), tool integration (such as Claude Code, Cursor or Deep Research) or the effectiveness of model inference.
• The openai’s failed marketing performance, essential launch of GPT-5 aims to accentuate this tendency to deflation. to strengthen the competitiveness of OpenAi thanks to a double strategy: to offer an open source version of its models and offer an API approximately 90 % cheaper than that of its direct competitors for proprietary models.

The accelerated pace of models of models Rebalances the landscape between innovations of ruptures. incremental iterations while the software and material infrastructure necessary for the next very large -scale experiments obeys a different temporality conditioned by heavier technical and industrial constraints.

GPT-5 also shows that the industry does not go to a “Winner Takes All” scenario in which a unique. omniscient and sufficiently efficient model would dominate all uses. Sam Altman himself recognized that “users diverge on the strengths and weaknesses of GPT-4O and GPT-5”. The industry therefore tends to a competitive market where differentiation is possible according to openai’s failed marketing performance, essential an increasing number of dimensions:

• High -risk / high cost uses. which require extreme reliability, advanced reasoning and alignment capacities and sectoral certification;
• consumer uses or different users value different aspects of a model: the personality of the model (writing style ), its alignment with a policy of moderation of content or its cultural imprint, the usefulness of the agent he feeds, the orchestration of the different tools around the model, count as much as pure performance.

openai’s failed marketing performance, essential

Being the best in its category-that is to say to offer the best performance for a given cost-is a key objective to meet the diversity of uses: controlled costs for laboratories deploying their Large scale, rapid inference for users, optimized execution on a calculation node, a chip or even a mobile phone.

This launch also illustrates how the AI evaluation crisis continues and increases.

• Traditional benchmarks have always measured only part of the performance and real usefulness of models on the technological border.
• GPT-5 shows that model assessments are no longer good predictors of the failure. success of a launch of a new model and its product-market fit : Even with excellent results on conventional quantitative benchmarks, a model can be ranked much lower on more complex metrics of sustainability and preferably.
• For example. Grok 4, which outperforms on several standard tests, only arrives at openai’s failed marketing performance, essential the middle of the table on Lmarena or Yupp and displays an 80 %-time-horizon lower than that of O3 and Claude Opus 4 on METR (The latter assesses the time that a model can maintain a reliable performance, here defined at 80 % precision, on long and complex tasks, in agents’ environments).
• On the contrary, apparently marginal performance gains from Claude 4 compared to its predecessor Claude 3.7 on conventional benchmarks did not faithfully represent the observed qualitative jump of the model which has supplied Claude Code: since its launch in May. the active user base of the platform has tripled and its annualized turnover has been multiplied by more than five .