If you’re building with AI today, you’ll quickly run into the same question: which model should I use?
The ecosystem is crowded, and every provider markets their models as the “best.” The truth is that different large language models (LLMs) shine in different contexts. The trick is not just knowing what’s available, but understanding where each fits best.
Here’s a breakdown of the most common LLMs in use: GPT (OpenAI), Claude (Anthropic), Gemini (Google), and a couple of others worth knowing, along with how to think about choosing between them.
Strengths:
Versatile, strong performance across reasoning, code generation, and general tasks.
Largest ecosystem of apps, plugins, and integrations.
Frequent updates, including smaller and cheaper models for efficiency.
Best for:
General-purpose applications when you don’t want to over-optimize for niche cases.
Tasks where ecosystem and tooling matter (e.g. embeddings, function calling).
Developers who want the “default safe bet” when performance and reliability matter.
Claude (Anthropic)
Strengths:
Long context windows that can handle hundreds of pages in a single input.
Strong on reasoning-heavy and structured tasks.
Polished output style that often reads as clearer, more concise, and aligned to user intent.
Best for:
Workflows requiring deep reading or summarization of long documents.
Building AI assistants that need reliable reasoning and polite, user-friendly responses.
Use cases where interpretability and alignment matter.
Gemini (Google DeepMind)
Strengths:
Natively multimodal, covering text, images, video, and code.
Strong integration with Google products and search capabilities.
Good at code reasoning and structured problem-solving.
Best for:
Applications that mix text with images or other media.
Leveraging the Google ecosystem such as Workspace, YouTube, or Android integrations.
Builders who want an early edge in multimodal user experiences.
Strengths:
Open-weight models available for self-hosting and customization.
Fast-growing ecosystem of fine-tunes, optimizations, and inference tooling.
Lower costs and control over deployment.
Best for:
Teams that want more control and are willing to manage their own infra.
Privacy-sensitive use cases where data shouldn’t flow through external APIs.
Experimentation with custom fine-tunes and domain-specific applications.
Strengths:
Open-weight, highly optimized small and medium-sized models.
Excellent efficiency-to-performance ratio.
Popular in cost-sensitive and high-performance infra setups.
Best for:
Teams focused on serving LLMs at scale with tight budgets.
Use cases where latency and throughput matter as much as raw intelligence.
Other Key Factors to Consider
Beyond raw performance, a few practical dimensions often decide which LLM makes sense for your workload:
Latency: Open-weight providers like Mistral and Meta often shine here, since you can run lightweight models on optimized hardware with low response times. For hosted APIs, OpenAI and Anthropic have put a lot of work into serving efficiency at scale, though they sometimes trade speed for higher reasoning accuracy.
Tooling Features: OpenAI leads in developer-focused features like function calling and embeddings, while Google is ahead on multimodality and Anthropic has invested in structured reasoning and long-context workflows. These extras can matter as much as the model’s core intelligence.
Deployment Flexibility: Open-weight models like Llama and Mistral dominate this category, since they can be fine-tuned, quantized, or deployed on private infrastructure. Closed APIs from OpenAI, Anthropic, and Google are more convenient but limit how much you can customize or control costs.
What type of task am I solving? If it’s broad, a strong generalist model will usually suffice. If it’s reasoning with long inputs, a model designed for context handling is better. If it involves multimodality, look for one built with that in mind.
What constraints do I have? If cost, infra control, or privacy are priorities, open-weight models may fit best. If ecosystem and developer experience matter most, a hosted API could be the better choice.
No single LLM is the “winner.” Instead, think of them like tools in a toolbox, each sharpest in different situations.
At Lava, this variety is exactly why we built Lava Build: one API that lets you switch between all these models without rewriting your app. Developers don’t have to gamble on a single LLM or rebuild their stack every time a new model arrives. You can route workloads dynamically: one model for general chat, another for long document analysis, another for multimodal inputs, all through one integration.
Practical tip: start with one model, but architect your app so it’s easy to swap others in. Future-proofing matters, and the pace of model releases isn’t slowing down.