MODEL SELECTION GUIDE · LAST REVIEWED JULY 15, 2026
How to choose an AI model: start with the task, not the leaderboard.
Short answer: define the task contract first: acceptable quality, latency, cost, context size, tools, and failure tolerance. Choose the least expensive route that meets that contract, then keep a measured escalation path for harder work.
Six questions before selecting a model
- What must be correct, and what can be approximate?
- Does the task need code execution, tool use, vision, audio, or a structured response?
- How much context must be processed and retained?
- What latency can the user tolerate?
- What is the unit cost at expected volume?
- What should happen if the preferred route is unavailable?
Task-first routing matrix
| Workload | Primary decision factors | Typical route |
|---|---|---|
| Routine writing, rewriting, translation | Low latency, consistent tone, low unit cost. | Efficient route, with quality sampling rather than premium-by-default. |
| Customer support | Grounding, policy adherence, escalation, and stable formatting. | Reliable route with retrieval, guardrails, and human escalation for edge cases. |
| Code generation and debugging | Repository context, tool use, testability, and reasoning quality. | Coding-capable route; verify with tests rather than trusting a model label. |
| Research and long documents | Context handling, citations, source fidelity, and time-to-answer. | Long-context or research-capable route with source retrieval and explicit uncertainty. |
| Extraction and classification | Structured output reliability, throughput, and deterministic validation. | Efficient route with schemas, validation, and a retry or review queue. |
| Agents and multi-step workflows | Tool calling, state, observability, cost bounds, and safe failure behavior. | Task-aware route with per-step budgets and an approved fallback policy. |
Build an escalation ladder
Do not design routing as one “best model” choice. Use three levels: an efficient default for routine tasks, a capable route for complex or high-value tasks, and an explicit override for cases where the application needs a specific model or feature. Promote a task only when a measurable rule says the default is insufficient—for example, tool requirements, context length, validation failure, or a high-risk decision.
Measure decisions with a representative evaluation set
Create a small, versioned set of real task examples for each workload. Record output quality, error type, latency, and cost by route. Re-run the set whenever a model, prompt, provider, or fallback policy changes. This is more useful than relying on a broad public benchmark that does not match your product.
How ELTEX fits
ELTEX is designed for this task-first approach: smart routing can handle a default decision, while plan and model access determine which routes are available. Production teams should keep explicit overrides for workflows with non-negotiable model, tool, compliance, or quality requirements.
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Further reading
Provider capabilities and request formats change frequently. Use current primary documentation such as the OpenAI API reference and the Anthropic API overview when defining a production route.