Understanding LLM Models: What Makes One Model More Powerful Than Another?
An engineer's guide to the characteristics that actually separate one model from another — architecture, parameters, training, context, and compute.
By Santosh Joshi · Aug 10, 2026
Why can two LLMs, handed exactly the same prompt, produce dramatically different answers? And why does a smaller model sometimes beat a much larger one? The answers live in a handful of properties of the model itself: its architecture, parameters, training, context, and the compute that runs it. This is an engineer's tour of those properties, and the trade-offs they force.
1. What's actually inside an LLM?
An LLM is a large neural network trained to predict the next token. A few terms do most of the explaining:
| Term | What it means | Why it matters |
|---|---|---|
| Token | The sub-word unit a model reads and writes | Every limit, speed, and price is counted per token |
| Parameters (weights) | The numeric values learned during training | They hold what the model knows and set its raw capacity |
| Model size | The parameter count, e.g. 7B or 70B | A rough capacity signal, not a guarantee of quality |
| Transformer | The network architecture behind modern LLMs | Processes a whole sequence in parallel, which makes training practical |
| Attention | The mechanism weighing which earlier tokens matter to the current one — a spotlight over the sequence | Drives coherence and long-range understanding |
| Embeddings | Vector representations of tokens and meaning | Similar meanings land near each other; the model's internal "understanding" |
| Context window | The tokens a model can consider at once — its working desk | Bounds how much input plus output fits in one request |
| Inference | Running the trained model to generate output — performing, not practicing | Where latency, throughput, and serving cost are decided |
Start with the token, because everything else is measured in it. A token is a chunk of text — often a whole common word, sometimes a fragment. A tokenizer might split tokenization into token + ization, while the stays a single token. A rough rule: one token is about ¾ of an English word, or roughly four characters, so 1,000 tokens ≈ 750 words. The model never sees letters or words directly. It reads and writes these units, and every context limit, latency figure, and price tag is counted in them.
2. How does it learn?
flowchart LR
A[Training Data] --> B[Tokenization]
B --> C[Pretraining]
C --> D[Base Model]
D --> E[Post-training]
E --> F[Deployed Model]
Training is next-token prediction. Show the model some text, have it predict the next token, and score the miss with a loss function. Backpropagation assigns blame to each weight, and gradient descent nudges all of them a little to reduce the error. Repeat across trillions of tokens and the weights settle into a compressed model of language.
[!TRIVIA] The scale is staggering. Meta's Llama 3.1 405B was pretrained on more than 15 trillion tokens, with training scaled across over 16,000 NVIDIA H100 GPUs. Meta reports roughly 30.8 million GPU-hours for the 405B model alone — the equivalent of one GPU running continuously for about 3,500 years. Its model card estimates roughly 11,390 tonnes of location-based CO₂e emissions, while reporting zero market-based emissions because Meta's operations are supported by matched renewable energy.
That produces a base model: fluent, but not yet helpful or safe. Post-training shapes it. Supervised fine-tuning (SFT) on curated examples teaches instruction-following. Preference methods such as RLHF (reinforcement learning from human feedback) tune tone, helpfulness, and refusals. Reasoning-focused training rewards the model for working through a problem before answering.
One thing is worth internalizing: weights are not a database. The model doesn't store and look up facts; it reconstructs likely continuations from patterns. That is why it generalizes to prompts it never saw, and why it can be fluently, confidently wrong.
3. Why are some models more capable than others?
Capability is not a single number. It emerges from architecture, parameter count, the quality and quantity of training data, training compute, and post-training. Raw size is only a signal — a well-trained 70B model can beat a poorly-trained 400B one.
Parameter counts also mislead across architectures. In a dense model, essentially all parameters run for every token. In a Mixture-of-Experts (MoE) model, a router activates only a few "experts" per token, so a model with a huge total parameter count may use just a fraction of it on any given token. Total parameters and active parameters are different numbers, and comparing a dense model to an MoE one by total size alone tells you little.
Reasoning deserves the same caution. It isn't a spec like context size or parameter count; there's no "reasoning" field on a model card. It emerges from the combination of architecture, training data, post-training, and how much compute the model is allowed to spend thinking at inference time. Bigger does not automatically mean better reasoning.
A single family makes the trade-off concrete. Anthropic's current Claude lineup runs from Haiku (fastest and cheapest, with near-frontier quality), through Sonnet (the best balance of speed and intelligence), to Opus (complex agentic coding and enterprise work), up to Fable (its most capable model, built for long-horizon agentic work, and also the slowest and most expensive of the four). None of that crowns a universal winner. It shows that capability, speed, and cost pull against each other, and you pick the point on that curve your task needs. Within one family or generation, the higher-capability variant usually wins on hard reasoning, typically at higher cost and latency. Across vendors — Google's Gemini Pro versus Flash, or a flagship versus a smaller OpenAI model — the same pattern holds.
4. What happens when you send a prompt?
Your prompt doesn't reach the weights directly. It lands in the context window: the model's temporary workspace for one request. Everything competes for that budget — system instructions, your input, prior conversation, any retrieved text, and the tokens the model generates back.
Three things people conflate, and shouldn't:
Weights ≠ Context window ≠ Persistent memory.
Weights are what training baked in permanently. The context window is scratch space that exists only for this call and disappears at the next one unless you resend it. Persistent memory — a system that "remembers" a user across sessions — is an application feature built on top, not an intrinsic property of the model.
Context windows have grown quickly; some frontier models now hold anywhere from hundreds of thousands to around a million or more tokens. But a larger window doesn't make a model smarter. It means more information can be supplied, not that the model reasons better over it. Models often attend unevenly across very long inputs, so what you put in context, and where, matters more than sheer size.
5. Why does inference cost money?
Generating one token means running a full forward pass over the model's active weights — billions of multiply-adds. Generation is also autoregressive: to write a 500-token answer, the model runs that pass 500 times, once per token, re-reading its weights each step. The bottleneck is usually memory bandwidth, the rate at which those weights can be fed to the compute units, which is why LLMs run on GPUs and other accelerators with fast memory rather than ordinary CPUs.
That is the real cost driver. In effect, you're renting a supercomputer by the syllable. It's also why serving teams lean on inference optimizations — KV caching to avoid recomputing past tokens, batching requests together, quantization to shrink the weights, and optimized kernels — all to push more tokens through the same hardware.
6. How do you read a model spec?
A model card is a spec sheet. Read it for what it does and doesn't tell you.
| Field | Example Value |
|---|---|
| Architecture | Transformer, MoE |
| Parameters | 70B total |
| Context window | ~128K–1M tokens |
| Max output | 16K–128K tokens |
| Modalities | Text, image |
| Precision | BF16 / FP8 |
| Typical strength | Complex reasoning |
Parameters hint at capacity but say nothing about training quality. Context window caps input size, not reasoning depth. Precision, and quantization to formats like FP8 or INT4, trades a little quality for speed and lower memory. Modalities tell you the model accepts images, not how well it reads them. And most commercial providers — Anthropic, OpenAI, Google — don't publish parameter counts or full architectures at all. Open-weight families do: Llama 3.1, for example, shipped at 8B, 70B, and 405B (an illustrative, now-dated example, not Meta's latest). For everything else, benchmarks and your own task-specific evaluations tell you far more than any single number.
7. What this means when choosing a model on Databricks
Once these ideas are clear, model selection on Databricks becomes much less mysterious. Mosaic AI Model Serving puts several kinds of model behind one interface: Databricks-hosted foundation models through Foundation Model APIs, external models from providers such as Anthropic and OpenAI, and your own custom or fine-tuned models registered in Unity Catalog. Not everything is hosted by Databricks; it's a unified serving layer over all three.
Selection then weighs quality and task performance, context requirements, latency, throughput, cost, and modality:
- Complex coding or reasoning → a higher-capability model.
- Classification or extraction at scale → a smaller, faster one.
- Image plus text → a multimodal model.
The rule that ties it together: choose the smallest, cheapest model that reliably clears your quality bar. Confirm that with a task-specific evaluation, not a parameter-count comparison.
The model mental map
LLM capability ≈ Architecture + Parameters + Training Data + Training Compute + Post-training + Inference Techniques
- Parameters — model capacity
- Weights — the learned numerical values
- Tokens — the units the model processes
- Context window — temporary working space
- Training — creates and updates the weights
- Post-training — shapes capabilities and behavior
- Inference — runs the trained model
- Model size — a useful signal, not a measure of intelligence
The model with the biggest number on the spec sheet isn't necessarily the best one for your workload. Learn what the numbers mean, test candidates on your actual task, and optimize for the quality you need — not the largest model you can afford.
