In this week’s tech insight, here’s a handy guide to the major (and now plentiful) generative AI models available, including what they do best, and how to access them.
Which One Is Right For You?
The AI boom is showing no signs of slowing. Whether it’s for writing, coding, design, research, or customer support, generative AI tools have become an everyday business asset, or at the very least, something worth exploring.
However, with so many models now flooding the market, it’s becoming harder to tell which ones genuinely deliver value and which are simply overhyped. Each promises cutting-edge performance, but the real differences often only become clear once you’ve tested them in practice.
With this in mind, here’s our plain-English guide to the standout generative AI models available to UK businesses in 2025. Here you can discover (if you haven’t already) what they’re particularly good at, where to find them, the pricing, and any recent updates (and controversies).
OpenAI: GPT-4o, GPT-4.5 ‘Orion’, Sora, Operator & More
The first place that many people were introduced to generative AI (and still the best-known), OpenAI offers multiple models for different use cases. These include:
GPT-4o (ChatGPT): The default for most business users. Handles writing, analysis, basic reasoning, and now image generation too. Available on chat.openai.com.
Free Tier: GPT-3.5 only.
ChatGPT Plus: £20/month for GPT-4o and image tools.
GPT-4.5 ‘Orion’: A more advanced version with stronger ‘world knowledge’. Currently only available with OpenAI’s £160/month Pro subscription.
Sora: A new text-to-video model capable of creating entire scenes from prompts. Still experimental and only available on paid plans.
Operator: An AI ‘agent’ designed to take actions on your behalf, like ordering stock or booking meetings. Available on Pro (£160/month), but early testers report unpredictable behaviour.
Deep Research: Designed for serious research with citations. Also Pro-only. Hallucinations are still an issue.
o3-mini & 4o-mini: Cheaper, faster reasoning models optimised for maths and code. Available for free or low cost on ChatGPT.
Pros: Mature, fast, widely integrated. Huge plugin and extension ecosystem.
Cons: Some of the most powerful tools are behind expensive paywalls. Occasional hallucinations and inconsistencies.
Google: Gemini 2.5, Deep Research & AI Premium Tools
Google’s AI suite, now rebranded under the Gemini umbrella, focuses mainly on knowledge tasks, coding, and long-context reasoning.
Gemini 2.5 Pro Experimental: Excels at code generation and reasoning. Slightly underperforms Claude 3.7 on some benchmarks.
Gemini Deep Research: Summarises large volumes of search data with citations.
Both require a Google One AI Premium subscription (£19.99/month), which also grants access to Gemini in Docs, Gmail, and other Google apps. See gemini.google.com.
Pros: Integrated across Google’s ecosystem. Long 2 million-token context.
Cons: Still looks like it’s catching up to OpenAI on certain creative benchmarks. Perhaps not as strong at natural conversation.
Anthropic: Claude 3.7 and Computer Use
Anthropic’s Claude models have quietly become the insider’s favourite. Models here include:
Claude Sonnet 3.7: A hybrid reasoning model. Can produce fast responses or take longer to ‘think’, depending on the task. Available free at claude.ai or via API.
Pro Plan: $20/month (about £17) gives faster access and priority use.
Computer Use: A more experimental agent designed to operate your machine. Still in beta. Billed by token usage.
Pros: Strong at coding, clear writing, and safe outputs. More control over behaviour.
Cons: Doesn’t generate images. Agents still under development.
xAI: Grok 3 and the Acquisition of X
Elon Musk’s xAI is pitching itself as the politically neutral, open challenger to OpenAI. However, it also appears to be playing a longer game. Models include:
Grok 3: Strong on maths, science and factual knowledge. Integrated into Musk’s X platform (formerly Twitter). Requires an X Premium+ subscription at $50/month (about £39).
Aurora: xAI’s image generator, capable of photorealistic visuals.
Also, in recent news (just this month) Musk’s xAI acquired his X (Twitter) platform in an all-stock deal. Musk claims it’s about combining data, distribution and compute. However, some commentators have suggested that the real goal may be access to X’s vast post database to supercharge AI training. With X’s 600 million users, xAI now has both data and a delivery vehicle.
It seems that according to some other commentators, there may be a financial angle to the deal related to possible troubles at Tesla. For example, with Tesla facing increased scrutiny over mounting debt and loan repayments, and with sales apparently affected by a backlash over Musk’s involvement with President Trump’s administration (and DOGE), Musk’s empire may need fresh capital and investor confidence. Folding X into xAI, now valued at $80 billion, may allow for more aggressive fundraising and help shield the broader group from Tesla’s recent turbulence. It may, therefore, be as much of a strategic hedge as a technical merger.
Pros: Fast-growing, quite good at logic tasks. Trained on unique datasets.
Cons: Limited availability outside X. Some controversy around political alignment and data use.
Meta: Llama 3.3 70B
Meta’s Llama series is aimed at developers and businesses looking for open source AI models.
Llama 3.3 (70B): Free and open source. Great for running on your own servers or fine-tuning in-house. Ideal for companies concerned with privacy.
Access it via Meta’s GitHub or model hubs like Hugging Face.
Pros: Free, transparent, customisable.
Cons: Needs technical setup. No hosted version available by Meta.
Cohere: Aya Vision & Command R+
Canadian firm Cohere focuses on language models optimised for enterprise and multilingual use.
Aya Vision: Multimodal. Great for image captioning and image Q&A, especially in non-English languages. Available for free via WhatsApp.
Command R+: Excels at RAG (retrieval-augmented generation) — good for firms needing AI to cite reliable sources. More info at cohere.com.
Pros: Multilingual strength. Strong on RAG.
Cons: Not as widely used yet. Hallucination issues persist in complex queries.
Stability AI: Stable Virtual Camera
Stability AI, known for Stable Diffusion, has pushed into 3D image generation.
Stable Virtual Camera: Turns 2D images into simulated 3D scenes and angles. Available on Hugging Face.
Pros: Innovative visuals. Good for creative use cases.
Cons: May struggle with complex or moving subjects. Research-use only for now.
Other Contenders Worth Watching
DeepSeek R1 (China): Impressive code and maths abilities, but data privacy risks due to Chinese government links.
Mistral Le Chat (France): Fast response AI with journalism tie-ins. Solid but prone to errors.
Alibaba Qwen: High benchmark scores in coding, but trust and censorship remain concerns.
Again, these models can be accessed via huggingface.co or the developers’ own sites.
What Does This Mean For Your Business?
The sheer volume of AI models / generative AI platforms now available, each claiming unique strengths, can make decision-making difficult. However, the good news is that these tools are maturing fast, with clearer use cases, pricing tiers, and performance benchmarks emerging.
Not surprisingly, OpenAI remains a dominant force, especially for content generation and general-purpose reasoning. However, it’s no longer the only serious player. Google’s Gemini, Anthropic’s Claude, and xAI’s Grok all offer increasingly credible alternatives, some with more transparency, others with deeper integration or specialisation in logic-heavy tasks. Open-source options like Meta’s Llama or Cohere’s RAG-optimised models give businesses more flexibility, particularly where privacy, cost, or fine-tuning are concerns.
The broader AI arms race is also having knock-on effects across sectors. For example, Musk’s consolidation of X and xAI could indicate a push towards tighter control of data, distribution, and development. While that may bring faster innovation, it also raises questions about data ownership, platform dependency, and regulatory oversight, all of which UK stakeholders, from policymakers to investors, will need to monitor closely.
The message for businesses, therefore, seems to be don’t get distracted by the hype. Instead, focus on what you actually need AI to do. Whether that’s speeding up internal workflows, improving customer service, or enhancing research and development, the right model is out there, but it may take some experimenting to find it. With the pace of change showing no sign of slowing, those who take the time to understand the landscape now will be better positioned to benefit from it in the months ahead.
