Generative AI Models: How to Compare and Choose One

What a model actually is

A model is a set of learned parameters plus the architecture that uses them. It is trained once, at great cost, and then used many times for inference, which is the step that produces an output for your request. When you use a chat product, you are using an application built around one or more models, not the model alone.

Language models usually come in two forms. A base model has been trained to continue text and is useful mainly to researchers and for further training. An instruction-tuned or chat model has been trained further to follow requests, hold a conversation and decline harmful tasks. Applications almost always use the instruction-tuned form.

Each model has a name and a version, and the version matters. A provider’s model card or documentation describes what that version can do, its limits, its context length and how it was evaluated. Read it before you build on the model.

The model landscape in brief

Widely used text and multimodal model families include OpenAI's GPT models, Anthropic's Claude, Google's Gemini, Meta's Llama and models from Mistral AI. Image generation includes diffusion-based families such as Stable Diffusion. Speech recognition and synthesis use their own specialised models.

Vendors release new versions frequently, and capabilities, prices and limits change with them. Read the provider's current documentation and model card for the exact version you plan to use rather than relying on a summary article, including this one.

Match the model type to the task

Not every job needs a large chat model. Many applications combine several specialised models, each doing the part it is best at.

Hosted API models and open-weight models

Open-weight does not always mean unrestricted. Read the licence for commercial use, attribution and acceptable-use conditions before building on a model.

Running an open-weight model yourself has become easier. Tools such as Ollama and llama.cpp run smaller models on a laptop, often using quantisation, which stores parameters at lower precision to save memory at some cost in quality. Larger models still need GPUs and serving infrastructure.

Criteria that matter for an application

• Task quality on examples drawn from your real inputs, including difficult cases.
• Modalities: text only, or also images, audio, documents or tool calls.
• Context length relative to the documents and conversation you need to supply.
• Latency and throughput at the volume you expect.
• Cost per completed task, not only price per token.
• Data, privacy and regional requirements for your users.
• Safety behaviour and how the model responds to unsafe or out-of-scope requests.

Read the model card and pricing page

• Context window and maximum output length, in tokens.
• Input and output token prices, which often differ, plus any discounts for caching or batch processing.
• Rate limits for your account tier.
• Knowledge cutoff date, after which the model has no training information.
• Supported languages and modalities, including file and image inputs.
• Data retention and whether your inputs may be used for training.
• Deprecation schedule, so you know when a version will be retired.

Build a small evaluation before you choose

Collect twenty to fifty representative inputs with written expectations. Include routine cases, edge cases and inputs the system should refuse. Run each candidate model with the same prompt and settings, then score the outputs with clear criteria such as correct, supported by the supplied context, and in the required format.

Public benchmarks are a starting signal, but they rarely match your documents, users or output format. Keep your test set and rerun it when you change model versions or prompts, so a regression is visible before users see it.

The sketch below runs the same test cases through several models and reports an average score and median latency. Replace call_model with your provider’s SDK and score with a function that applies your written criteria.

import statistics
import time

def evaluate(model_name, call_model, test_cases, score):
    results = []
    for case in test_cases:
        start = time.perf_counter()
        output = call_model(model_name, case['input'])
        latency = time.perf_counter() - start
        results.append((score(output, case), latency))
    return {
        'model': model_name,
        'mean_score': statistics.mean(s for s, _ in results),
        'median_latency_s': statistics.median(t for _, t in results),
    }

for name in ['model-a', 'model-b']:
    print(evaluate(name, call_model, test_cases, score))

Estimate cost per completed task

Token prices are quoted per million tokens, with separate prices for input and output. A request that sends 2,000 input tokens and receives 300 output tokens costs (2,000 × input price + 300 × output price) ÷ 1,000,000. Multiply by the number of requests a task needs, including retries, retrieval and any checking step.

A cheaper model that needs two attempts can cost more than a dearer one that succeeds first time. Some providers also discount repeated prompt prefixes through caching or offer cheaper batch processing for work that is not urgent. Measure tokens on your own test cases instead of estimating from word counts.

Larger is not automatically better

A larger model can follow complex instructions more reliably, but a smaller one may meet a narrow requirement at lower latency and cost. Many applications route simple requests to a smaller model and reserve a larger one for harder cases. Decide from measured results on your task, not from parameter counts.

Smaller models are often produced by distillation, in which a small model is trained to imitate a larger one on a particular kind of task. Combined with quantisation, this can make a model fast enough to run on a laptop or phone, at some loss of general capability.

Prompting, retrieval or fine-tuning?

Sources: Lewis et al.: Retrieval-Augmented Generation. Checked 23 October 2026.

Plan for model changes

• Call models through a thin internal wrapper so that switching providers changes one module, not the whole application.
• Pin exact model versions in configuration and record which version produced each result.
• Rerun your test set before moving to a new version, because prompts tuned for one model can behave differently on another.
• Watch deprecation notices and schedule migrations before retirement dates.
• Keep a fallback model for outages if the application is critical.

What to practise at each level

Common questions

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