How to compare LLMs for an AI application

The best LLM for an AI application is the one that meets that application's quality, safety, latency, cost, and operational requirements on representative tasks. General benchmarks are useful for narrowing the field, but they cannot tell you which model will perform best on your prompts, data, tools, users, and failure modes. Compare candidates with the same test set, the same application configuration, and a scorecard that makes trade-offs explicit.

This guide gives you a repeatable way to choose a model and keep the decision defensible when models, prompts, providers, or requirements change.

Start with the application, not the model leaderboard

A model can rank highly on a general benchmark and still be a poor fit for your application. A model that is strong at broad reasoning may be less reliable at extracting fields from your documents, following your policy, calling your tools, or answering from your knowledge base.

Write down what the application must do before you shortlist models:

• Tasks: What jobs will the model perform, such as classification, extraction, summarization, question answering, coding, routing, or multi-step tool use?
• Inputs: What languages, document types, context lengths, and user variations will it receive?
• Outputs: Does the application require free-form text, valid JSON, a schema-conforming object, a citation, a tool call, or a refusal?
• Failure costs: Which errors are unacceptable? Consider unsafe advice, fabricated facts, privacy leaks, missed escalations, invalid actions, and silent omissions.
• Service limits: What are the maximum acceptable p95 latency, error rate, token usage, and cost per successful task?
• Operating constraints: Do you need a specific region, retention policy, deployment option, provider, or audit trail?

These requirements become your evaluation criteria. They also determine whether a model should be rejected before you compare its average quality score. A candidate that violates a hard safety, privacy, schema, or latency constraint is not rescued by a higher overall average.

Build a test set that looks like production

Your test set is the instrument that turns model selection from opinion into a controlled comparison. Use the same inputs for every candidate, and keep the application logic constant while you compare models.

Include several kinds of cases:

1. Representative cases. Sample real requests across the application's main tasks, users, languages, and input lengths.
2. Known failures. Add incidents, support tickets, rejected outputs, and cases that exposed weaknesses in the current system.
3. Edge cases. Test ambiguity, missing information, malformed input, long context, conflicting instructions, unusual formatting, and unsupported requests.
4. Adversarial cases. Test prompt injection, policy conflicts, jailbreak attempts, sensitive data, and instructions embedded in retrieved documents.
5. Workflow cases. For agents and tool-using systems, include the full journey, not just the final answer. Capture tool selection, arguments, ordering, retries, and stopping behavior.

Keep a label or expected outcome for every case. That might be an exact answer, a set of allowed labels, required fields, a reference answer, a policy decision, or a rubric that a domain expert can apply. OpenAI describes the basic evaluation loop as defining the task, running the evaluation on test inputs, and analyzing the results. Anthropic likewise recommends creating benchmark tests specific to the use case and testing with actual prompts and data. See the OpenAI evaluation guide and Anthropic's model selection guidance.

For a first useful comparison, start with the cases you already know matter most. Galtea's LLM evaluation guide recommends beginning with real failures and using a golden dataset to make quality changes visible. Expand the set as production reveals new failure modes.

Evaluate every candidate through the same harness

An apples-to-apples comparison requires more than sending the same user message to different APIs. Hold the surrounding system constant:

• Use the same system and user instructions, unless prompt adaptation is itself part of the comparison.
• Use the same retrieved documents, conversation history, tool definitions, and tool results.
• Use the same generation settings where the providers expose comparable controls.
• Record the model identifier, provider, version, prompt version, tool definitions, retrieval configuration, and evaluation-set version.
• Run enough repetitions to identify variance when the model or task is nondeterministic.
• Separate model quality from provider or infrastructure effects, such as queueing, network time, and rate limits.

If you change the prompt for one candidate, you are comparing model and prompt configurations, not models alone. That can still be the right experiment, but name the unit being compared accurately.

Use a scorecard that reflects the real decision

Do not collapse every requirement into one vague quality rating. Score the dimensions that can change the outcome:

Treat some measures as gates rather than weighted factors. For example:

• Safety pass rate must exceed the approved threshold.
• Structured output must be valid for every critical workflow.
• p95 latency must stay below the product limit.
• The model must satisfy the application's data handling requirements.

Only candidates that pass the gates should reach the weighted score. This prevents a model from compensating for a serious safety or reliability failure with cheap tokens or fast responses.

Separate quality metrics by task

There is no single quality metric that works for every LLM application. Match the metric to the output and the failure:

• Classification: accuracy, precision, recall, F1, and the cost of false positives versus false negatives.
• Extraction: field-level accuracy, completeness, type validity, and whether unsupported fields are left empty rather than invented.
• Question answering: correctness, relevance, groundedness, citation accuracy, and appropriate abstention.
• Summarization: factual consistency, coverage of required points, omission rate, and readability.
• Generation: a rubric with explicit criteria, paired preference review, and human assessment for high-impact decisions.
• RAG: retrieval quality and generation quality measured separately. A strong generator cannot compensate for missing or irrelevant context.
• Agents: task completion, correct tool choice, valid arguments, state transitions, number of steps, unsafe actions, and recovery from tool errors.

Use automated checks where the expected behavior is objective. Use an LLM judge only with a clear rubric and calibration against human judgments. For regulated or high-stakes workflows, keep domain experts in the loop for the cases where a wrong answer has material consequences.

Test safety and failure behavior, not only happy paths

A model selection test that contains only ordinary requests will reward fluency and miss the failures that matter most. Include tests for:

• Hallucinated facts and unsupported certainty.
• Incorrect or incomplete answers when the source context is missing.
• Refusing a valid request or complying with a prohibited one.
• Revealing secrets or personal information from prompts, context, or tool output.
• Prompt injection in user input and retrieved content.
• Unsafe tool calls, excessive permissions, and actions taken without required confirmation.
• Bias or quality differences across languages, user groups, and input formats.
• Repeated runs that produce materially different decisions.

Record the failure itself, not only the aggregate score. A candidate with a slightly lower mean score may be the better choice if its worst failures are easier to detect and contain. Conversely, a high average can hide a small set of severe failures.

Compare latency and cost per successful task

The cheapest model per call is not necessarily the cheapest model for the application. Count the full path:

cost per successful task =
model cost
+ tool and retrieval cost
+ retry cost
+ evaluation or review cost
+ failure handling cost


Measure cost alongside quality and latency under realistic inputs. Record token counts, response time, retries, and completion status. Report p50 and p95 latency, not just an average, because users experience the tail. Also measure cost per successful task, not only cost per request. A low-cost model that needs retries or human correction may lose to a more capable model.

Consider routing when the workload contains easy and hard cases. A lower-cost model can handle routine requests while a stronger model handles cases that meet an escalation rule. Test the routing policy as part of the application, including false escalations and missed escalations. Do not assume a multi-model design is better until the evaluation shows that its added complexity pays for itself.

Test tool use, structured outputs, and multi-turn behavior

A model that writes a good standalone answer may still fail inside your application. Test the interfaces your system depends on:

• Does it return valid JSON or the required schema every time?
• Does it choose the right tool for the request?
• Are tool arguments complete, correctly typed, and within allowed values?
• Does it handle tool errors without repeating a dangerous action?
• Does it preserve relevant state across turns?
• Does it stop when the task is complete?
• Does it ask for clarification when a required value is missing?
• Does it cite or quote the source material when the workflow requires evidence?

Evaluate the complete trace, not just the final text. For a multi-step assistant, a correct final answer can hide an unsafe intermediate action or an unnecessary disclosure.

Use a weighted decision model after the gates

Weights should represent business risk and user experience, not whatever is easiest to measure. A regulated decision-support application may weight safety, groundedness, and traceability more heavily than a low-risk internal drafting tool. A real-time voice application may make p95 latency a hard gate.

Here is an illustrative scorecard after hard gates have been passed. Scores are normalized from 0 to 100 and are examples, not measurements of named models.

The totals are close because the weights expose the trade-off. Candidate B is slightly better under this weighting because it is cheaper and faster, even though Candidate A has stronger task quality and safety scores. If safety is a hard gate at 95 and Candidate B scores 91, Candidate A wins without using the weighted total. If the application changes from asynchronous document processing to real-time customer support, revisit the weights and thresholds rather than reusing this example.

Keep the raw results beside the total. A single number helps rank candidates, but the slices, failures, and evidence explain the decision.

Run a minimal model comparison in seven steps

Use this protocol when you need a defensible first decision:

1. Define the job. Write the tasks, users, inputs, outputs, hard constraints, and failure costs.
2. Create the set. Combine representative cases, known failures, edge cases, and safety cases. Label expected outcomes or define a review rubric.
3. Choose the measures. Select task-specific quality metrics, safety tests, reliability checks, latency measures, and cost accounting.
4. Freeze the harness. Keep prompts, context, tools, settings, and test-set version constant across candidates.
5. Run the candidates. Capture outputs, traces, token usage, latency, errors, and model metadata. Repeat enough cases to see variance.
6. Review the evidence. Apply hard gates first, then calculate the weighted score. Inspect failures by task, language, input type, severity, and workflow step.
7. Validate in production conditions. Use a shadow run, limited rollout, or controlled routing experiment. Compare live quality, safety, latency, cost, and user outcomes before making the default permanent.

The decision is not finished when a spreadsheet names a winner. It is finished when the chosen configuration has passed the application's constraints and you have a way to detect regression after release.

Make the decision reproducible

Store the decision with enough context for another team member to reproduce it:

• The model and provider identifiers.
• The prompt, system instructions, and generation settings.
• The evaluation-set contents, labels, and version.
• The application and retrieval configuration.
• The tool definitions and permissions.
• The metric definitions, judge rubric, and human-review instructions.
• The gates, weights, thresholds, and rationale.
• The raw outputs, traces, failures, and aggregate results.
• The date of the run and the decision owner.

Re-run the comparison when you change the model, prompt, retrieval logic, tool schema, safety policy, or major application behavior. Model selection is a maintained engineering decision, not a one-time procurement exercise.

Galtea's AI evaluation platform is designed for this workflow: teams can turn product requirements into repeatable tests, generate representative datasets, combine automated metrics with expert review, and compare models, prompts, and system versions before release. It also supports simulation and production monitoring so the same quality standard can be checked after deployment.

Method and scope

This is a practical model-selection framework, not an independent benchmark or ranking of providers. The scorecard values, candidate results, and weighted totals are illustrative examples. Replace them with measurements from your own application, test set, constraints, and operating environment.

The framework covers model and application configurations, including prompts, retrieval, tools, structured outputs, safety behavior, latency, cost, and production validation. Public model benchmarks can help create a shortlist, but they are not a substitute for testing representative inputs. Recheck provider capabilities, pricing, limits, and data-handling terms before making a production decision.

Frequently asked questions

Should I choose the model with the highest benchmark score?

No. Use public benchmarks to build an initial shortlist, then test candidates on representative application data. Choose the configuration that meets your requirements with acceptable quality, safety, latency, cost, and operational risk.

How many examples do I need to compare LLMs?

There is no universal number. Start with a well-labeled set that covers important tasks, known failures, and high-risk cases, then add examples when production exposes a new failure mode.

Should I use an LLM as a judge?

An LLM judge can evaluate open-ended outputs at scale, but it needs a specific rubric and calibration against human judgments. Use deterministic checks for schemas, exact values, tool arguments, and latency, and keep expert review for high-impact or ambiguous cases.

Is a larger model always better?

No. A larger model may improve difficult tasks while adding latency, cost, or operational complexity. A smaller model is often the better choice for routine, high-volume work if it passes the application's quality and safety gates.

Should I compare providers or models first?

Define the application requirements first, then compare the model and provider configurations that can satisfy them. Data handling, region, rate limits, availability, and observability may be hard constraints even when model outputs are similar.

When should I use more than one model?

Use multiple models when evaluation shows a meaningful benefit from routing or specialization. Test the routing policy, escalation errors, added latency, and added failure modes before adopting the extra complexity.

How do I know when to rerun the comparison?

Rerun it after a model, provider, prompt, tool, retrieval, or safety-policy change, or when user inputs shift materially. Keep a regression set of important past failures so each configuration is compared with the same history.

Apply the framework to your own system

Bring one representative workflow, its known failures, and the constraints that matter to your team. Galtea's AI evaluation platform helps teams turn those requirements into repeatable tests, compare models and system versions, and carry the same quality standard into simulation and production monitoring.