How to Choose an LLM: Benchmarks, Cost, and Setup

Our comparison of the leading models ranks GPT, Claude, Gemini, and the main open-model families. A leaderboard can narrow the field, but it cannot tell you which model will work best in your product.

There is no universal winner. The model at the top is often the slowest and most expensive, and it may still perform poorly on a narrow task. Ask a more specific question: which model is best for this task, with these constraints and this budget?

This guide explains how to answer it. We start with benchmarks, move through the practical trade-offs, and finish with deployment choices, including whether to use an API or self-host.

Benchmarks: what they measure, and what they hide

Model launches arrive with pages of benchmark results. These scores are useful for narrowing the field, but they do not predict performance on your own workload.

Start with the composite scores, which combine several tests into one number:

The Artificial Analysis Intelligence Index combines several difficult benchmarks in a single figure. It gives a quick view of the current leaders.
LMArena, formerly called Chatbot Arena, uses blind human comparisons. People read two anonymous answers and choose the better one, capturing preferences that exam-style tests miss.

Use these scores for a shortlist, then look at the individual tests that matter for your use case:

Benchmark	What it really measures	When to care
MMLU-Pro	Broad knowledge and reasoning across 50+ subjects, multiple choice	A general “is it smart?” baseline
GPQA Diamond	Graduate-level science questions even experts get wrong	Deep technical or scientific work
AIME / math	Competition math - multi-step, no partial credit	Quantitative, logic-heavy tasks
SWE-bench Verified	Fixing real bugs in real software repositories	Coding and software agents
LiveCodeBench	Fresh coding problems, refreshed to resist memorization	Coding, when you worry about cheating
Terminal-Bench / τ-bench	Using tools and a terminal to finish multi-step jobs	Agents that act, not just answer
MMMU	Reasoning over images, charts, and diagrams	Multimodal and document work
Humanity’s Last Exam	Deliberately brutal expert questions across fields	Gauging frontier reasoning headroom

Benchmarks also have four recurring problems:

Contamination. Popular questions can leak into training data, allowing a model to recall an answer rather than reason through it. Fresh tests such as LiveCodeBench are designed to reduce this problem.
Teaching to the test. Once a benchmark matters commercially, labs optimize for it. The score may reflect narrow test preparation rather than general skill. This is Goodhart’s law in practice: when a measure becomes a target, it becomes less useful as a measure.
Averages hide weak spots. A high composite score can hide poor performance on a specific task such as parsing your invoices, matching your brand voice, or reasoning about your domain.
The benchmark is not your workflow. A model that solves PhD-level physics questions may still write poor support replies.

Use benchmarks to choose two or three candidates, then test them on your own data. Build a small evaluation set from tasks you do every week and score each candidate on it. Our guide to running evaluations for AI solutions explains the process.

The trade-offs that actually decide it

Once two models perform similarly on your evaluation set, practical constraints usually decide between them.

Quality - measured on your work

Your evaluations matter more than a public rank. A model that places third on a general index but first on your tasks is the better choice for your product.
Reasoning effort is adjustable. Current models let you trade thinking time for speed and cost. Difficult analysis may need high effort; support-ticket classification usually does not.
Consistency matters more than an occasional great result. Automated systems need a model that is reliably good, not one that alternates between excellent and incorrect answers.

Cost - read past the headline price

Input and output have different prices. Output tokens typically cost three to six times as much as input. A verbose model can therefore cost more than a terser model with a higher-looking rate.
Prompt caching can reduce repeated cost. Reusing the same large instructions or documents across calls can cut that portion of the bill by up to about 90%.
Batch discounts. Work that isn’t time-sensitive (overnight summaries, bulk tagging) often runs at roughly half price through batch endpoints.
Reasoning tokens are billable. Models may generate hidden reasoning before answering. A low token rate combined with heavy reasoning can cost more than a higher rate on a concise model.
Measure cost per task. Token price is only the unit rate; the bill also depends on how many tokens the model needs. Claude Sonnet 5 is twice as cheap as GPT-5.6 Sol per output token but costs about 70% more per finished task, $1.72 against $1.01. Our comparison of current models includes a method for measuring your own workload.

Speed - two numbers, not one

Time to first token (TTFT) is how long until the answer starts. Low TTFT feels snappy and is what matters for chat, voice, and anything a person waits on.
Throughput (tokens per second) is how fast the full answer streams out. It’s what matters for long documents and bulk jobs. A model can be great at one and mediocre at the other - pick for the experience you’re building.

Capabilities - match the model to the shape of the work

Context window. Large windows, often 1M tokens, can hold an entire codebase or long document. Quality may still fall in the middle of a very long prompt, known as the “lost in the middle” effect. A large window does not replace careful context selection.
Modality. Work involving images, audio, PDFs, or video needs a model designed for those inputs.
Tool use and function calling. For an agent, check whether the model reliably selects the correct tool and arguments. General benchmark scores do not always reveal this ability.
Structured output. If you need clean JSON every time, check how well the model sticks to a schema. Flaky formatting quietly breaks pipelines.

Operations and risk

Privacy and compliance. Where do prompts go, and for how long? For regulated data, look for zero-retention options, SOC 2 / HIPAA / GDPR posture - or keep the data in-house with an open model.
Licensing and openness. Open-weight models can be downloaded, fine-tuned, and self-hosted; closed models are governed by API terms. This decides whether self-hosting is even on the table.
Reliability. Check rate limits, uptime, and deprecation schedules. Pin specific versions and configure a fallback so a provider outage does not stop your application.
Ecosystem fit. SDK quality, OpenAI-compatible endpoints, and Model Context Protocol (MCP) support determine how much integration code you need. A small quality gain may not justify a poor fit with your stack.

Improve the setup before upgrading the model

When a model underperforms, try these changes before moving to a more expensive tier:

Improve the prompt and examples. Clear instructions and a few worked examples often close the gap at little cost.
Add retrieval (RAG). If the model lacks your data or current facts, retrieve the relevant documents at query time. This is usually cheaper than upgrading the model.
Fine-tune for a narrow requirement. Fine-tuning can enforce a style or format at scale or improve a smaller model on a specific task. Use it after prompting and retrieval because it adds data and maintenance work.

A smaller model with relevant context and a clear prompt can outperform a frontier model with a poor setup. Test those changes before spending more on tokens.

Self-hosted vs. as-a-service

You can call a provider’s API or host a model yourself. For most teams, an API is the better default. It is faster to ship, requires no model infrastructure, scales on demand, and gives you access to current releases. The trade-offs are per-token billing and sending data to an external provider.

Self-hosting makes sense in three cases: privacy or data-residency rules require prompts to stay inside your environment; volume is high enough that GPUs cost less than API usage; or you need custom models, control over rate limits, or predictable latency.

The main tools are:

Local use: Ollama runs an open model with one command, LM Studio provides a desktop interface, and llama.cpp supports laptops and CPUs.
Production serving: vLLM and SGLang are high-throughput serving engines used by many hosted providers. NVIDIA offers NIM microservices and Triton for its GPUs.
Managed open models: Together AI, Fireworks AI, Groq, and Baseten host open weights without requiring you to operate the hardware.

As a rule, start with an API and move to self-hosting when privacy, volume, or operational control justifies the additional work.

A simple way to decide

A practical selection process looks like this:

Prototype on a strong model so you can test the idea before optimizing cost.
Write a small eval set from real tasks, with answers you’d actually accept.
Test cheaper and open models against the same set. Keep the least expensive one that passes, based on cost per finished task rather than token price.
Close any gaps with better prompts, then retrieval, then fine-tuning - in that order.
Choose hosting according to privacy, volume, and operational requirements.
Pin versions, add a fallback, and monitor cost and quality because the best option will change.

Where MindsHub comes in

Model releases and prices change quickly. Avoid building the application around one provider so a model change remains a configuration update instead of a rewrite.

MindsHub follows this model-neutral approach. Unified Inference connects Anthropic, OpenAI, Google, SpaceXAI, and Meta alongside Kimi, DeepSeek, and Qwen, so you can test an open model and a frontier model on the same task and adjust reasoning effort, and switch from a dropdown while keeping the same work, history, and memory. MindsHub Cowork provides the workspace where an agent handles the complete task and returns the finished result.

For current model details, read the comparison of the leading models, then compare what they cost per task. You can also view MindsHub pricing.

Frequently asked questions

What’s the single most important factor when choosing an LLM? Performance on your tasks, measured with a small evaluation set. Cost, speed, context, and hosting are constraints around that result.

Can I trust benchmark leaderboards? Use them to build a shortlist. Watch for contaminated data and benchmark-specific tuning, then test the finalists on your own work.

Should I self-host or use an API? Use an API by default because it is faster to ship and requires no model infrastructure. Self-host when privacy rules, high volume, or operational control justify it, using tools such as Ollama locally or vLLM and NVIDIA’s stack in production.

Do I need to fine-tune a model? Usually not at first. Better prompts and retrieval (RAG) solve many gaps more cheaply. Fine-tune when you need a consistent format at scale or specialization in a smaller model.

Which benchmarks matter for coding and for agents? For coding, look at SWE-bench Verified and LiveCodeBench. For agents that use tools, look at Terminal-Bench and τ-bench - they measure whether a model can actually do things, not just answer questions.

How do I keep up as models change? Keep the evaluation set current, pin model versions, and use a setup such as Unified Inference that allows model changes without an application rewrite.

MindsHub by MindsDB puts every major model behind one endpoint. Unified Inference serves frontier and open models through the OpenAI and Anthropic request formats on one API key and one bill, and MindsHub Cowork is the agent workspace on top of that catalog - delegate entire projects and collect finished, shareable results, with work running on interchangeable open-source agent harnesses, Anton and Hermes. Founded 2018 in Berkeley. Backed by Benchmark, Mayfield, Y Combinator, and NVIDIA.