The loudest launch
Launch-day demos and marketing budgets are built to impress, not to predict how a model behaves on your work. Being talked about the most is not evidence of being right for you.
— Choose the right model —
Compare large language models,
without the hype.
Every launch claims to be the best. The Institute of AI helps you compare large language models on the things that actually decide fit for your work: capability on your task, cost per use, speed, context, safety, hosting, and data handling. Pick the model on the evidence, not the loudest announcement.
— Start here —
'Best model' is the
wrong question.
The model that tops a chart or dominates your feed is not automatically the one that fits your task, your budget, or your data rules. Comparison is a decision about trade-offs, not a race for a single crown.
Generic benchmarks
Public leaderboards measure broad, averaged tasks. Yours is specific. A model can lead the table and still trail on your extraction, your tone of voice, or your awkward edge cases.
The headline price
A low per-token rate looks cheap until longer prompts, retries, and human corrections are added up. And the cheapest tiers are often the most permissive with the data you send them.
— The rubric —
Seven dimensions that
actually decide fit.
Run every model you are weighing through the same seven questions. Each one carries a trade-off, so there is no free win: a gain on one dimension is usually paid for on another.
01
Capability on your task
What to look for
Test the model on your actual work, not a generic chart. Build a small set of real examples with known good answers and score each candidate against them.
The trade-off
The most capable frontier models cost more and respond slower. Peak ability you will never use is latency and money you are paying for nothing.
02
Cost per use
What to look for
Price is charged per token and split between what you send and what the model generates. Estimate realistic prompt and response sizes, then multiply by your true volume.
The trade-off
A cheaper model that needs longer prompts, more retries, or human fixing can cost more overall than a pricier one that gets it right the first time.
03
Latency and speed
What to look for
Look at time to first token and tokens per second. For a live chat or a typing-speed feature, speed is a feature; for an overnight batch it barely registers.
The trade-off
Larger models are usually slower. Smaller models, shorter outputs, and streaming buy back speed, sometimes at the cost of depth on hard questions.
04
Context length
What to look for
How much text the model holds at once, counted in tokens. Match it to your longest realistic input, a contract, a codebase, a transcript, with room to spare.
The trade-off
Big context windows are convenient but cost more per call, and models often attend less reliably to the middle of a very long input. More context is not always a better answer.
05
Safety and guardrails
What to look for
How the model handles unsafe requests, sensitive topics, and adversarial prompts, and whether its refusals suit your use. A support bot and a security tool need different thresholds.
The trade-off
Stricter guardrails cut risk but can over-refuse legitimate work; looser ones are more flexible but push more of the responsibility onto you.
06
Openness and hosting
What to look for
Open-weight models you can run yourself, or closed models behind an API. This decides where your data sits, whether you can fine-tune freely, and how tied you are to one supplier.
The trade-off
Self-hosting an open model gives control and privacy but needs infrastructure and skill; a hosted API is simpler but binds you to the pricing, limits, and roadmap of one supplier.
07
Data handling
What to look for
Whether your inputs are retained, logged, or used to train the model, and where they are processed. Read the terms rather than the marketing, and find the setting that governs training.
The trade-off
Consumer tiers are cheap or free but usually the most permissive with your data; enterprise terms cost more and give firmer guarantees. Convenience and confidentiality pull apart.
— Match the model to the job —
Common jobs, and the kind of
model that usually fits.
These are starting points, not verdicts. The right model depends on your data, your budget, and your tolerance for error. Use them to narrow the field, then measure the shortlist on your own task.
High-volume classification, tagging, and routing
Usually fits: Smaller, cheaper
The task is narrow and repeatable, so pay for speed and price rather than frontier reasoning. Validate on a sample first, then let volume do the rest.
Long-document summary and question answering
Usually fits: Large context, or retrieval
Reach for a big context window when the source genuinely will not fit, but a smaller model paired with retrieval is often cheaper and just as accurate.
Complex reasoning, coding, and multi-step analysis
Usually fits: Frontier-class
This is where extra capability earns its cost, because the wrong answer is expensive to catch and to fix. Spend on ability where mistakes hurt most.
Drafting and rewriting at everyday scale
Usually fits: Mid-tier general
Fluent first drafts are cheap now. You rarely need the most expensive model to produce something serviceable that a person then edits and signs off.
Sensitive, confidential, or regulated data
Usually fits: Open or enterprise terms
A self-hostable open model, or a closed one under enterprise data terms, matters more here than raw rank. Data handling and hosting outweigh a place on a leaderboard.
On-device or offline features
Usually fits: Small, local
A small open model that runs locally trades some capability for privacy, low latency, and no per-call cost. The right choice when the network is not guaranteed.
None of these are fixed answers. Model families move fast, and a smaller model this year can match last year's frontier. The Institute of AI's LLM Leaderboard is community-scored and independent, so no vendor pays for placement, which is why it is a better place to shortlist than any launch-day chart.
— Run the comparison —
Four steps to a decision you
can stand behind.
A comparison is only useful if it ends in a choice you can defend. This is the short version of the method the rubric supports.
01
Define the job and the limits
Write down the task, the error rate you can accept, your budget per call, your latency ceiling, and any data rules. You cannot compare without a target to compare against.
02
Shortlist by family, not by name
Use the rubric to decide roughly what you need, smaller or frontier, open or closed, then pick two or three candidates. The leaderboard narrows the field quickly.
03
Measure on your own examples
Run the shortlist against real inputs with known good answers. Score capability first, then check cost, speed, and refusals on the very same runs.
04
Re-check on a schedule
Models and prices move often. Revisit the comparison when your volume grows, a new model lands, or the numbers quietly stop adding up.
— Common questions —
Comparing large language models, answered.
How do I compare large language models fairly?+
Build a small set of your own real examples with known good answers, score each model on them, then weigh capability against cost, speed, context, and data handling. Public benchmarks are a starting point for a shortlist, not a verdict on what fits your work.
Which is the best large language model?+
There is no single best. The right model depends on your task, your budget, your latency needs, and your data rules. A frontier model wins on hard reasoning and coding; a smaller, cheaper model wins on high-volume, simple work. Compare on your own use, not on a general ranking.
Are open or closed models better?+
They are different trade-offs. Open-weight models give you control, privacy, and the option to self-host or fine-tune; closed API models are simpler to adopt and often more capable at the very top end, but tie you to a supplier. Choose by where your data must sit and how much control you need.
How much do large language models cost to run?+
Cost is charged per token, split between what you send and what the model generates, multiplied by your volume. Estimate realistic prompt and response sizes at your expected scale rather than trusting a headline per-token price, and remember that retries and corrections add to the true figure.
Where can I see up-to-date model rankings?+
The Institute of AI's LLM Leaderboard is community-scored and independent, so you can compare current models without vendor spin, then test the shortlist on your own task before committing.
— Compare on the evidence —
Pick the model that fits,
not the one that shouts.
Shortlist on independent, community-scored data from the Institute of AI, then test the finalists on your own work. That is how you compare large language models without the hype.
— Go deeper
— Platforms
The LLM leaderboard
Community-scored, independent rankings you can filter and compare, then test on your own task before you commit.
— For business
LLMs for business
How to choose, deploy, and govern large language models across an organisation, without betting the roadmap on hype.
— Learn AI
AI glossary
Plain-English definitions of tokens, context windows, and the other terms you meet when you compare models.