Topic · 17 resources
Model Comparison
Honest comparisons across the major models: benchmarks, real-world differences, and the far more useful question of which one to reach for on a given task.
Articles2
GPT vs Claude vs Gemini: An Honest Comparison
A practical comparison of the three leading AI models across writing, coding, reasoning, long documents and cost — including which one to reach for on which task, and where each genuinely falls short.
Which AI API Is Cheapest? How to Actually Compare
Headline price per million tokens is a misleading way to compare AI APIs. Here is how to calculate real cost for your workload, and the four changes that cut bills the most.
Glossarys14
Chain-of-thought prompting
Chain-of-thought prompting asks a model to work through its reasoning step by step before answering. It measurably improves accuracy on maths, logic, and multi-step problems by forcing intermediate steps instead of an immediate guess.
Large language model (LLM)
A large language model is a neural network trained on vast amounts of text to predict the next token in a sequence. That single objective, at sufficient scale, produces the ability to write, summarise, translate, reason and code.
Transformer
The transformer is the neural network architecture behind virtually all modern language models. Its key innovation is self-attention, which lets every token directly consider every other token in the input.
Attention
Attention is the mechanism that lets a model weigh how much each token should influence the interpretation of every other token. It is how a model resolves what a pronoun refers to, or which clause modifies which noun.
Parameters
Parameters are the learned numerical weights inside a model, adjusted during training. Parameter count — 7B, 70B, 405B — is a rough proxy for capacity, but not a reliable predictor of quality.
Reasoning model
A reasoning model is trained to generate extended internal deliberation before answering. It trades latency and cost for substantially better performance on maths, logic, coding and multi-step problems.
Multimodal model
A multimodal model accepts or produces more than one type of data — commonly text plus images, and increasingly audio and video. It processes them in a shared representation rather than through separate pipelines.
Open-weights model
An open-weights model has publicly downloadable parameters, letting anyone run it on their own hardware. Llama, Mistral, Qwen and DeepSeek are prominent examples.
Fine-tuning
Fine-tuning continues training a pre-trained model on your own examples to specialise its behaviour. It is the right tool for teaching consistent style, format or classification behaviour — not for adding knowledge.
LoRA
LoRA (Low-Rank Adaptation) is a fine-tuning method that trains small adapter matrices instead of updating all model weights. It cuts the compute and storage cost of fine-tuning by orders of magnitude.
Quantization
Quantization reduces the numerical precision of model weights — from 16-bit to 8-bit or 4-bit — shrinking memory use and speeding inference at a modest cost in quality.
Distillation
Distillation trains a smaller model to imitate a larger one, transferring much of the capability at a fraction of the inference cost. Most small production models are distilled from bigger teachers.
LLM as judge
LLM as judge uses one model to grade another's output against criteria you define. It makes evaluating subjective quality practical at scale, with known biases to control for.
Mixture of experts (MoE)
Mixture of experts is an architecture where only a subset of the model activates per token. A router selects a few specialised sub-networks, so a very large model runs at the cost of a much smaller one.