How to Choose the Right LLM Model

 

Choosing an AI model looks like a small decision.
Then the first invoice arrives.
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Or legal asks where the data went.
Or a customer gets a confident wrong answer.
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Most teams pick the model that wins the demo.
Production asks different questions.
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The infographic below lists seven of them.
Here is how to use each one.
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1. Use case fit
Start with the job, not the model.
Coding, research, and chat need different strengths.
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2. Reasoning quality
Multi-step problems need a stronger model.
Routine questions rarely do.
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3. Speed and cost
Latency is how long users wait for a reply.
You pay per token, a small chunk of text.
Multiply cost per request by monthly volume before you commit.
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4. Context and memory
The context window is how much a model reads at once.
Long contracts and large files need a bigger one.
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5. Tool and agent support
Can it trigger actions in your systems?
Can it return data other software can read?
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6. Privacy and deployment
Decide where your data is allowed to go.
A vendor's cloud. Private hosting.
Or an open-source model you run yourself.
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7. Reliability and testing
Test on your real work before you scale.
Count how often it gets answers wrong.
Check that the same question gets the same answer.
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WHERE TEAMS GET IT WRONG
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Most teams compare models on reasoning.
That is what benchmarks measure.
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The checks that most often stall projects are 3, 6, and 7.
Cost at scale. Where data goes. How it fails.
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In the regulated environments I've worked in, the first question was rarely which model is smartest.
It was where the data goes, and who signs off.
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One more thing most teams miss.
Whatever you choose will likely be surpassed within months.
Build so you can swap models without rebuilding the workflow.
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This is the AI Execution Gap in a single decision.
The distance between what a model can do and what your organization can run, trust, and afford.
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RUN THIS BEFORE YOUR NEXT AI DECISION
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Write one page per use case:
→ The task it serves
→ Why this model
→ Monthly cost at real volume
→ Where the data goes
→ How it was tested
→ Who re-checks when a better model ships
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If nobody owns that last line, nobody owns the decision.

https://lnkd.in/p/d2CcXrig