Think this captures one of the bigger differences between what Open AI offers and what others offer using the same name. Funnily enough, Google's Gemini 2.0 Flash also has a native integration to google search[1]. They have not done it with their Thinking model. When they do we will have a good comparison.
One of the implications of OpenAI's DR is that frontier labs are more likely to train specific models for a bunch of tasks, resulting in the kind of quality wrappers will find hard to replicate. This is leading towards model + post training RL as a product, instead of keeping them separate from the final wrapper as product. Might be interesting times if the trajectory continues.
PS: There is also genspark MOA[2] which creates an indepth report on a given prompt using mixtures of agents. From what i have seen in 5-6 generations, this is very effective.
One of the implications of OpenAI's DR is that frontier labs are more likely to train specific models for a bunch of tasks, resulting in the kind of quality wrappers will find hard to replicate. This is leading towards model + post training RL as a product, instead of keeping them separate from the final wrapper as product. Might be interesting times if the trajectory continues.
PS: There is also genspark MOA[2] which creates an indepth report on a given prompt using mixtures of agents. From what i have seen in 5-6 generations, this is very effective.
[1]: https://x.com/_philschmid/status/1896569401979081073 (i might be misunderstanding this, but this seems a native call instead of explicit)
[2]: https://www.genspark.ai/agents?type=moa_deep_research