Meta talk about the use of Google, OpenAI models on apps

Meta talk about the use of Google, OpenAI models on apps, the company will keep developing its own AI models, but also consider strategic, temporary collaborations with Google and OpenAI.

Meta Considers Using Google & OpenAI Models in Its Apps

• The new artificial intelligence division of Meta Platforms called Meta Superintelligence Labs is reportedly considering short-term collaboration with the technological competitors: Google and OpenAI in order to enhance AI capabilities in its apps.
• In particular, in regard to the integration of Google as a means of improving the conversational features of the Meta AI, which is a chatbot belonging to the company, Meta has already covered the topic of incorporating the Google Gemini model.
• Also, it is testing the models of OpenAI to fuel Meta AI and other AI-based capabilities in its social media platforms.

Purpose Behind These Partnerships

• These partnerships are perceived as short-term band-aid solutions, which aim at improving AI capabilities as Meta develops its own AI Llama 5 to a competitive standard.

Internal Use of Third-Party Models

• Meta is already apparently internally using external models. As an example, the staff is using the Anthropic models through internal tools, like a coding assistant, to facilitate the development workflows.

A — Google Gemini (what it is good at)

• Thinking models Multimodal Multimodal systems: Gemini (2.5 line) specifically is a multimodal family, trained to be a multimodal family, enabling the chaining of internal reasoning steps, supporting text+image+audio (and large video/audio inputs in some versions) and optimized to solve complex reasoning problems.
• Very large context windows & data ingestion: Gemini variants claim extremely large context (hours of video, thousands of pages of text, large codebase) in some of their documentation and marketing, and helps with long-form context, codebases and multimodal tasks.
• Image quality and editing: Recent Gemini application updates (Nano Banana / image editor upgrades) introduce the ability to fine-tune photo edits, multi-turn edits and consistency among users – demonstrating Google striving to push generative image editors as part of Gemini.
• Product access / tiers: Gemini is available as consumer assistant features, as well as via paid tiers (Pro/Ultra) and APIs to devs – Google is incorporating it throughout Android/Chrome/Workspace and providing paid access to a higher ability.

Net: Gemini: powerful multimodal arguments + long context + deep Google product differentiations and fast developing image/audio tooling.

B — OpenAI (what it has done well, as of 2025)

• Awk multimodal family and high turnover: GPT-4o family of OpenAI (and later GPT-5 announcement) focuses on multimodal inputs (text, vision, audio), low latency and better performance on non-English/text. Text+vision and more affordable / faster deployments GPT-4o and GPT-4o-mini are directly optimized.
• GPT-5 5 strengths: The GPT-5 announcement claims that there are dramatic advances in the code generation, front-end design sensibility, and repository-level debugging – making GPT-5 a huge leap forward in practical software engineering and creative UI/code assignments.
• Ecosystem/tooling: OpenAI offers mature API tooling, model embedding, and platform documentation; it has a large third-party ecosystem (plugins, Azure/enterprise integrations) so it is often the workhorse choice in product builds.
• Net: OpenAI = very powerful developer ecosystem, rapid text+code, and robust multimodal/real time audio+vision support on GPT-4o/GPT-5.

C — Where Llama (and in particular the intended Llama 5) is

• Public indications of Meta: Meta has been iterating Llama (Llama 3 / 3.1/3.2 / 3.3 and Llama 4 ). Reuters/coverage around Aug 2025 reports that Meta is constructing Llama 5 and that leadership considered the use of third-party models even as Llama 5 is scaled. That puts Llama 5 as the next big push by Meta to correspond to Gemini / GPT lines.
• Technical directions Meta has explored in the past: Recent variants of Llama have switched to mixture-of-experts architectures and longer context windows (purportedly million+ token contexts with some Llama 4 versions), multimodality and large training corpora (public + Meta proprietary social data). It is the reasoning, larger context, multimodal strength and efficient inference that are the axes Llama 5 will probably aim at.
• Reality check: timeline and risk Multiple outlets have covered that Superintelligence Labs, the Enlightenment arm of Meta, is under strain and that schedules are unpredictable; it has also made significant investments but reported internal quality issues. Same with Expect Llama 5: It will reach parity on reasoning/multimodality, but will be launched behind Gemini/GPT in features or in real-world polish short of Meta compromising its performance/safety tradeoffs.

Net: Llama 5 is what Meta wants to counterpunch, perhaps in terms of scale, multimodality and integration with Meta platforms, but publicly available data is that it is unclear whether it would be feature-to-feature comparable to Gemini/GPT at launch.

 

D — trade offs and product implications (why would Meta temporary plug in Google/OpenAI models)

1. Time to market vs. control: With a third-party model, shipping new features is quicker; making Llama 5 provides control over costs, data/privacy and monetization but is both more time and engineering resource intensive. Integrations were framed by Reuters reporting to be stopgaps.
2. Cost and latency: API call charges result in the cost and external latency per request; the optimized cost of the API call to run your own model can be lower at scale but must use immense infra (Meta is investing billions).
3. Feature parity vs. differentiation: Gemini/OpenAI are already ahead of the pack in certain multimodal and developer-tool functionality; Meta can borrow it as they develop Llama functionality that is distinctive to the meta-ecosystem (e.g. tight Instagram/WhatsApp context, proprietary engagement signals).
4. Safety policy: Public safety policy Meta has a public stance on safety (and reported internal policy not to launch advanced models) which influences whether Llama 5 will be open, restricted, or enterprise-only – which matters to partners and researchers.

E — Short checklist: what to watch next (concrete signals that indicate who is ahead)

• Benchmarks / independent assessments (MMLU, coding benchmarks, multimodal tasks) following the official release of every vendor. (Gemini 2.5/Pro and GPT-5 purported leads on various tasks — check using external tests)
• Context window & pricing (cost per token for long contexts — matters for large-document apps).
• Real product integrations – what are the models that power real user functionality (e.g., Google making Gemini default on Android; Meta showing Llama making Meta.AI).
• Safety/release modes – general availability or enterprise-only or locked models; impacts adoption and third party innovation.

Quick summary (TL;DR)

• Gemini = multimodal + giant context + heavy image/audio editing push (DeepMind upgrades) and extensive Google product integration.
• OpenAI = mature developer platform, excellent code/design capabilities (GPT-5), broad multimodal APIs and strong tooling (GPT-4o family).
• Llama 5 (Meta): the upcoming large Meta bet to compete on reasoning, multimodality and platform integration – but public announcement reveals doubtfulness over when and whether this will instantly surpass Gemini/GPT in polish or capabilities. Meta can consequently apply Gemini/OpenAI models as bridging technologies in the short term.

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