---
title: "How to Choose an AI Model for a Production Application"
description: "Evaluate quality, latency, cost, context, tool use, deployment, and governance with your own workload."
language: en
canonical: https://navinresearch.com/blog/models/choose-ai-model.md
topics: ["choose AI model", "LLM comparison", "production model selection"]
---

# How to Choose an AI Model for a Production Application

Evaluate quality, latency, cost, context, tool use, deployment, and governance with your own workload.

**Short answer:** Choose from a task-specific scorecard, not a universal leaderboard. First eliminate models that fail mandatory privacy, deployment, modality, or tool requirements. Then compare end-to-end task success, p95 latency, and total cost on representative traffic.

## Implementation steps

1. Write hard constraints and quality thresholds before testing.
2. Use the same prompts, tools, retrieval data, and retry budget for candidates.
3. Measure the full pipeline rather than isolated model calls.
4. Plan a fallback and a repeatable model-upgrade process.

## Validation checklist

- Include peak load and rate-limit behavior.
- Test refusals, malformed inputs, and long contexts.
- Record exact model identifiers and evaluation dates.

## Common mistakes

- Picking the highest benchmark average.
- Ignoring operational limits and output-token cost.
- Coupling business logic to one provider's response shape.

## Practical decision

Ship the smallest design that passes the checks above. Record the model, prompt, tool, dataset, and runtime versions used in testing so later changes can be compared rather than guessed. Recheck the linked documentation before relying on provider-specific limits, model names, prices, or preview features; those details change more quickly than the engineering principles in this note.

## Official sources

- [OpenAI, Evaluations guide](https://platform.openai.com/docs/guides/evals)
- [Mistral AI, Models overview](https://docs.mistral.ai/getting-started/models/models_overview/)
- [DeepSeek, Models and pricing](https://api-docs.deepseek.com/quick_start/pricing)

---

Reviewed: 2026-08-24. This concise engineering note is educational, not a claim that Navin Research created or independently verified the cited provider technology.