Choosing a language model today is not a matter of faith. It is an engineering decision that affects costs, latency, privacy, and output quality. And to decide well, it helps to understand what lies beneath that black box we call an LLM. This comparison is not about «which one is best,» but about why each family of models behaves differently depending on the task, and what you should look at before integrating one into your workflow.
The common architecture: they all start from the same trick
Current models (GPT, Claude, Gemini, Llama, Mistral) are direct descendants of the transformer, introduced in the paper Attention Is All You Need (2017). The central idea: process text in parallel through attention mechanisms that weigh which words in the context matter most for predicting the next one. Training is done with gradient descent over billions of tokens, and the output is a probability distribution over the next token.
That detail matters: the model doesn’t «know» anything in the strict sense. It calculates probabilities. That’s why it can sound confident while inventing a bibliographic citation. The difference between models is not in the concept, but in how they are fine-tuned after pre-training (RLHF, DPO, constitutional AI) and in the quantity and quality of data they see.
Comparison of the four most widely used families
GPT (OpenAI): the broadest ecosystem
The GPT-4o family and its mini variants offer native multimodality (text, image, audio) and a very well-documented API. Its strength is integration: plugins, assistants, mature function calling. Its practical weakness is opacity about training and dependence on a single provider. For quick prototypes and third-party tools, it is the path with the least friction.
Claude (Anthropic): long context and nuanced responses
Claude stands out for its extensive context windows (hundreds of thousands of tokens in its recent versions) and for conservative behavior when faced with ambiguous instructions: it tends to ask for clarification instead of improvising. In tasks involving analysis of long documents or code review, that prudence is appreciated. In pure creative generation, it sometimes turns out less risky than desired.
Gemini (Google): ecosystem integration and multimodality
The Flash and Pro versions are designed to coexist with Google services (Workspace, Vertex AI, search). Their long context and multimodal capability are competitive. The trade-off: documentation and quota limits change frequently, and the differences between Flash and Pro versions greatly affect reasoning quality.
Llama and Mistral: open weights and full control
Here the logic is different. The weights are available (with licenses worth reading—not all are equally permissive). You can run them locally or in your own cloud, fine-tune them, and audit their behavior. The cost is infrastructure: a 70B-parameter model in FP16 needs approximately 140 GB of VRAM just for the weights, plus memory for the context. Quantization to 4 bits reduces that figure to a manageable range on high-end consumer GPUs, but introduces measurable degradation in reasoning tasks.
What to actually compare before choosing
- Real versus effective context window: a model announcing 200,000 tokens doesn’t mean it maintains coherence across that entire range. Degraded attention at intermediate positions (the lost in the middle phenomenon) is documented in industry studies. Test with your own long document before trusting it.
- Latency and cost per token: a large model can take several seconds to generate a complete response. For interactive chat, small fine-tuned models often win on perceived speed.
- Behavior when refusing: they all have filters. The difference lies in how many false positives they generate. A model that refuses a legitimate medical query out of excessive caution is an operational problem.
- Determinism and temperature: at temperature 0, output is not completely reproducible between calls in most APIs. If you need identical results, you will have to cache or validate the output.
A concrete case: the mistake of measuring context by the advertised figure
A few months ago, while working on extracting clauses from rental contracts, we found that a model with a 128,000-token window began omitting clauses located in the middle of the document when it exceeded 60 pages. The solution was not to switch models, but to split the document into sections and chain partial summaries. It is a recurring pattern: the advertised limit is not the useful limit. Anyone integrating LLMs into production learns early on that context engineering (what you put in and in what order) matters more than the choice of model brand.
Another common mistake: assuming that a model with better general benchmarks will be better in your domain. Evaluations like MMLU or HumanEval measure broad capabilities, not your use case. The only valid test is your own evaluation set, with real examples from your business and defined acceptance criteria.
Limitations that no model solves yet
LLMs have no persistent memory between sessions unless you build it yourself (RAG, vector databases, history). They do not verify facts on their own: if you ask them for a figure, they will generate it with the same confidence as a correct one. They do not reason symbolically in a reliable way on formal logic problems with many variables. And they are not suitable, on their own, for medical, legal, or financial decisions without human oversight: the providers’ own official documentation warns of this.
Nor should they be used as oracles of current events. Their knowledge has a cutoff date, and although some integrate web search, that integration adds latency and an additional layer of error (which source they choose, how they cite it).
How to decide in practice
For high-volume classification and extraction tasks, a small fine-tuned model is usually cheaper and more predictable than a large one. For complex reasoning and long documents, extended-context models save you architecture. For sensitive data that cannot leave your infrastructure, open weights are the only real option, assuming the maintenance cost. And for prototypes, the API with the best documentation and community will save you days.
No choice is definitive: models change every few months, and what is a reference today will be outdated tomorrow. Design your integration with an abstraction layer over the provider and your own evaluation set. That will allow you to switch models without rewriting the product, which is the most cost-effective decision in the long run.
And a final transparency warning: there is no model that guarantees correct answers. Quality depends on the context you give it, on how you phrase the instruction, and on the validation you put in place afterward. Any promise of infallible results should be viewed with skepticism.