How to Choose an LLM for Your Use Case

Q: How to Choose an LLM for Your Use Case

Define task complexity, volume, latency, privacy, budget, and integration needs before evaluating any models. Frontier models ($2.50-$75/M tokens) for complex reasoning, mid-tier ($0.15-$4.40/M tokens) for most workloads, small models for high-volume simple tasks. Cost scales dramatically from $60/month at 1K requests daily to $60K/month at 1M requests daily for the same model. Closed APIs offer simplicity and frontier capabilities while open-weight models provide control and cost savings at high volumes. Open-weight models require significant engineering investment for GPU instances, inference serving, and maintenance infrastructure.

Start with Requirements, Not Models

The single biggest mistake in LLM selection is starting with 'which model is best?' The right starting question is 'what do I need, and which model meets those needs most efficiently?' Starting from models leads you to rationalize your choice after the fact. Starting from requirements leads you to the model that actually fits.

Define your requirements across six dimensions before you look at a single model. Task complexity: does your use case require multi-step reasoning and nuanced judgment, or pattern completion and format transformation? Volume: how many requests per day, and does that volume change predictably? Latency: are users waiting in real time, or is this a background process? Privacy: are you processing sensitive, regulated, or confidential data? Budget: what is your monthly ceiling, and how does it break down per request? Integration: what infrastructure do you already have, and how hard would it be to swap providers?

Task complexity is the most important dimension because it governs which capability tier you need, which in turn governs cost by an order of magnitude. High-complexity tasks — legal analysis, multi-step reasoning, architectural planning, nuanced long-form writing — require frontier models or reasoning models. Medium-complexity tasks — document summarization, code review, customer support, report generation — are well-served by capable mid-tier models at a fraction of the cost. Low-complexity tasks — classification, entity extraction, format transformation, simple Q&A with provided context — often work as well or better with small, fast, cheap models optimized for throughput.

Volume interacts with cost in ways that are easy to underestimate. A model that costs $0.002 per request at 1,000 requests per day costs $60 per month — trivial. At 100,000 requests per day, the same model costs $6,000 per month — significant. At 1,000,000 requests per day, it costs $60,000 per month — mission-critical economics. Your volume tier determines whether cost optimization is a rounding error or a product constraint. Estimate your realistic peak volume before you commit to any model.

Write your requirements as a one-page document before you evaluate any specific model. This forces clarity on what matters, prevents feature-envy from swaying your decision, and gives you a reference to return to when vendors make compelling marketing claims. Revisit the requirements document when you re-evaluate in 6 months — requirements change as products evolve, and your model choice should change with them.

Understanding Model Tiers: Frontier, Mid, and Small

The LLM market in 2025 has three clear capability tiers, each with a distinct cost profile and appropriate use case range. Understanding which tier your task belongs to is the most important shortcut in model selection.

The frontier tier contains the most capable models available: GPT-4o, o3, Claude Sonnet 4.5, Claude Opus 4, and Gemini 2.5 Pro. These models excel at complex reasoning, nuanced judgment, and tasks requiring deep domain knowledge. Pricing runs from $2.50 to $75 per million output tokens. Frontier models are the right choice when output quality directly drives revenue or risk — customer-facing premium experiences, high-stakes analysis, complex code generation, or legal and medical reasoning where errors are costly.

The mid tier balances quality and cost for the majority of production workloads: GPT-4o Mini, Claude Haiku 3.5, Gemini 2.5 Flash, and o4-mini for reasoning tasks. These models handle classification, extraction, summarization, moderate writing, standard code generation, and conversational AI with quality that is indistinguishable from frontier models for most users on most tasks. Pricing runs from $0.15 to $4.40 per million output tokens — typically 10–50× cheaper than frontier. For most teams, the mid tier is the right default starting point.

The small and local tier covers models designed for deployment on constrained hardware, edge inference, or extreme cost sensitivity: Llama 4 Scout, Mistral Small, Phi-3 Mini, Gemma 2, and similar models under 12 billion parameters. These models are viable for simple classification, basic extraction, and template-filling tasks where volume is very high or infrastructure constraints prevent cloud API calls. Self-hosted small models can cost less than 1% of cloud API pricing at scale, but they require engineering investment to deploy, update, monitor, and scale reliably.

The boundaries between tiers are not fixed — they shift rapidly as new models release. Claude Haiku 3.5 today outperforms GPT-4 (from 2023) on most benchmarks, despite being priced in the mid tier. The practical implication: always re-evaluate your tier assignment when a major new model releases in the tier above yours. What required a frontier model 6 months ago may now be handled by a mid-tier model with equivalent quality at one-tenth the cost.

Open-Source vs Closed-Source: When Each Wins

The open-source vs closed-source decision is fundamentally about trading simplicity for control. Closed APIs (OpenAI, Anthropic, Google) give you instant access to frontier capabilities with zero infrastructure management, automatic updates, provider-managed reliability, and clear SLAs. Open-weight models (Llama, Mistral, Gemma, Qwen) give you full control over deployment, data governance, customization, and long-term cost at high volumes — at the cost of managing that infrastructure yourself.

Choose closed APIs when: you are moving fast and infrastructure overhead would slow you down; your volume is low to moderate (under ~1M tokens per day); your data governance requirements are satisfied by an enterprise DPA; you cannot afford the engineering investment of self-hosted model management; or you need the absolute highest quality (frontier closed models still outperform open-weight models at the extreme capability ceiling). The majority of teams building AI products today should start with a closed API and only evaluate open-weight alternatives after their volume and requirements justify the switch.

Choose open-weight models when: you are processing data that cannot leave your infrastructure (healthcare PHI, financial PII, classified government data, trade secrets); your volume has reached the point where cloud API costs are a significant business expense (typically above $10K/month in API fees); you need fine-tuning on your own domain-specific data and want to own the resulting model weights; you need deterministic, offline inference with no dependency on external service availability; or your regulatory environment requires full auditability of the model and inference stack.

The hidden cost of open-weight models is engineering time. Deploying and maintaining a Llama 4 Maverick instance requires: GPU instances or specialized inference hardware (A100 or H100-class for frontier models); inference serving software (vLLM, TGI, SGLang); autoscaling configuration; model update management as new versions release; monitoring and alerting for model quality and infrastructure health; and team expertise to manage all of the above. Budget 2–4 weeks of engineering time for an initial production-quality deployment, and ongoing maintenance of 10–20% of one engineer's time.

Fine-tuning is the strongest argument for open-weight models and the most frequently misunderstood. Fine-tuning an open-weight model on your domain data can produce significantly better results than prompt engineering a frontier closed model on specialized tasks — legal document classification, medical coding, domain-specific code generation. But fine-tuning requires a labeled dataset (typically 1,000–10,000 examples for meaningful improvement), compute budget for training runs, and expertise in the fine-tuning process. Evaluate whether your use case genuinely benefits from domain adaptation before committing to the open-weight path.

Privacy, Compliance, and Data Governance

If you are processing sensitive data, your model selection must be governed by compliance requirements before capability considerations. The question is not 'which model is best?' but 'which models are legally permissible for this data?' Answering this question incorrectly exposes your organization to regulatory penalties, contractual liability, and reputational damage that far outweigh any performance advantage from a better model.

Healthcare data (PHI under HIPAA in the US): OpenAI, Anthropic, and Google all offer BAAs (Business Associate Agreements) that cover API usage for healthcare applications. However, BAAs have conditions — your system prompts and application architecture must meet specific requirements. Self-hosted Llama on your own HIPAA-compliant infrastructure eliminates the dependency on a vendor BAA entirely. For mental health and other sensitive healthcare sub-domains, evaluate whether any cloud API exposure is acceptable given your patient population.

Financial data (PII, transaction data, trading strategies): PCI DSS governs payment card data and generally requires controlled infrastructure where data in transit and at rest is auditable. GDPR in Europe requires that personal data of EU citizens be processed under adequate data protection agreements, with data transfer to non-EU providers subject to additional scrutiny. SOC 2 Type II certification (which OpenAI, Anthropic, and Google all hold) is a baseline requirement for enterprise financial services integrations but is not sufficient for all use cases.

Legal documents and trade secrets: law firms and legal departments processing privileged communications should evaluate whether cloud API data retention policies are compatible with attorney-client privilege. While OpenAI and Anthropic offer zero-retention API configurations (where no request data is stored), the contractual and ethical implications of sending privileged communications through any external service should be reviewed by counsel. Many law firms default to self-hosted models for this reason.

Practical compliance checklist before selecting a model provider: (1) identify the regulatory frameworks that apply to your data; (2) obtain the vendor's DPA, BAA, or equivalent agreement and have legal review it; (3) verify the vendor's data retention configuration options and default settings; (4) check where inference is performed geographically — EU data processed in US infrastructure may require additional safeguards under GDPR; (5) document your compliance rationale so you can demonstrate it to auditors. If any of these steps is unclear, default to self-hosted open-weight models until the compliance path is established.

A Practical Decision Framework

Use this framework to narrow your selection to 2–3 candidates before running your own evaluation. Start with the compliance gate: is any of your data subject to regulations that restrict which providers you can use? If yes, resolve the compliance question first — it eliminates most closed-API options for the highest-sensitivity data. If no, proceed with the full provider landscape.

Next, assess task complexity and match it to a tier. Simple, high-volume tasks (classification, extraction, basic templated generation) start with GPT-4o Mini, Claude Haiku 3.5, or Gemini 2.5 Flash. If quality is insufficient after testing, step up to the mid-quality tier. Complex tasks requiring nuanced judgment (analysis, nuanced writing, complex code) start with Claude Sonnet 4.5 or GPT-4o. Hard reasoning tasks (competition math, complex debugging, PhD-level science, multi-step planning) start with o4-mini; if still insufficient, use o3 or Claude Opus 4.

Then apply the domain filter. Coding and software development Claude Sonnet 4.5 for quality, o4-mini for hard problems, DeepSeek V3 for cost efficiency. Long-form writing Claude Sonnet for quality and voice consistency, GPT-4o as second choice. Multilingual applications Gemini 2.5 Pro for broadest language coverage, Qwen 2.5 for Chinese, Mistral Large for European languages. Customer support at scale Claude Haiku or GPT-4o Mini for cost, with fallback to Sonnet for complex issues. Data-sensitive use cases Llama 4 Maverick (self-hosted) for frontier quality, Mistral Small for cost efficiency.

Apply the volume and budget filter. Under $500/month API budget closed APIs, mid tier, no restrictions. $500–$5,000/month closed APIs, frontier tier for high-value tasks, mid tier for volume. $5,000–$20,000/month evaluate whether open-weight self-hosting breaks even; it typically does at this range for sustained workloads. Above $20,000/month open-weight self-hosting is almost always cheaper; the question is whether your team has the capacity to manage it reliably.

Finally, shortlist 2–3 candidates and run a structured evaluation on your actual task distribution. The framework above narrows the field; only your own data narrows it to one. Document your requirements, your shortlist rationale, and your evaluation results — this becomes the foundation for re-evaluating when new models release or your requirements change.

Domain-Specific Model Recommendations

Software development and coding: Claude Sonnet 4.5 is the current quality leader for general-purpose coding — readable code, strong documentation, excellent at following format constraints and code style guidelines. For hard algorithmic problems, competitive programming, and complex debugging, o4-mini delivers superior reasoning with extended thinking chains. For high-volume, cost-sensitive code generation (autocomplete, boilerplate, test generation), GPT-4o Mini and DeepSeek V3 offer the best economics. If you need to self-host, Llama 4 Maverick is competitive with GPT-4o on most coding tasks.

Long-form writing, editing, and content: Claude Sonnet is the consensus quality leader for long-form prose — it maintains voice consistency across long documents, follows nuanced editorial guidelines, and produces naturally flowing narrative. GPT-4o is the second choice, particularly strong for structured formats (reports, business proposals, technical documentation). For multilingual content, Gemini 2.5 Pro covers the broadest language range with the highest fidelity. For high-volume content production at reduced cost, Claude Haiku 3.5 or GPT-4o Mini work well for structured formats with clear templates.

Customer support and conversational AI: Claude Sonnet and GPT-4o are both strong foundations; the practical decision often comes down to refusal calibration (Claude tends to be less restrictive on professional queries while maintaining safety), integration ecosystem (OpenAI has broader third-party integrations), and latency profile (GPT-4o Mini and Claude Haiku are faster for high-volume deployments). For voice-based support where latency is critical, Gemini 2.5 Flash and GPT-4o Mini have the fastest TTFT in the mid tier. Build a domain-specific system prompt and test on a representative sample of your actual support tickets before committing.

Data analysis, extraction, and structured generation: mid-tier models handle the majority of extraction and classification tasks with quality equivalent to frontier models at a fraction of the cost. GPT-4o Mini, Claude Haiku 3.5, and Gemini 2.5 Flash all excel at JSON extraction, named entity recognition, classification, and structured output generation with proper prompting. Reserve frontier models for complex, ambiguous documents where the model needs genuine judgment rather than pattern matching. For tabular data analysis with Python code generation, GPT-4o and Claude Sonnet both produce reliable pandas/SQL code.

Scientific research, legal review, and healthcare: for research, Gemini 2.5 Pro leads on multimodal analysis (charts, equations, figures) and benefits from its 1M-token context for literature synthesis; o3 and o4-mini lead on hard mathematical reasoning. For legal and healthcare use cases, resolve the compliance gate first (see the privacy section) — then Claude Opus 4 for long-document legal analysis and nuanced judgment, GPT-4o Mini or Claude Haiku for structured extraction at scale. Any healthcare clinical decision support deployment must be validated on your specific patient population; general medical benchmarks do not substitute for clinical validation.

What Your Budget Actually Gets You

Under $100/month: you have full access to all mid-tier closed models with substantial volume. At $0.15 per million input tokens (GPT-4o Mini), $100 buys approximately 650 million input tokens — enough for 650,000 requests at 1,000 tokens each, or 32,000 requests per working day. For most early-stage applications, a $100/month budget is not a constraint — it is more than sufficient to build, test, and launch a real product.

$100–$1,000/month: this range covers serious production usage of mid-tier models or moderate usage of frontier models. At $3.00 per million input tokens (Claude Sonnet 4.5), $1,000 buys approximately 330 million input tokens — enough for 33,000 requests per working day at 10,000 input tokens each. Most B2B SaaS products with AI features operate in this range until they reach significant user scale. The economics support either mid-tier models at high volume or frontier models at moderate volume.

$1,000–$10,000/month: this is the range where model selection has meaningful business impact. At this spend level, optimizing between model tiers can save $3,000–$8,000/month without quality impact if you route tasks appropriately. Build a multi-model architecture (mid-tier for routine tasks, frontier for complex ones) rather than routing everything through a single frontier model. At the upper end of this range, evaluate whether open-weight self-hosting with spot GPU instances breaks even for your sustained workload.

Above $10,000/month: at this spend level, serious infrastructure evaluation is warranted. Open-weight models on dedicated GPU infrastructure typically cost $0.001–$0.003 per 1,000 tokens fully loaded (hardware, networking, engineering overhead) compared to $0.003–$0.015 for comparable closed-API mid-tier models — a 3–10× cost reduction at sustained high volume. However, the engineering investment is substantial, and the savings require sustained high volume to justify the overhead. Get precise cost modeling from your infrastructure team before committing.

Hidden costs that compound at scale: prompt caching (OpenAI and Anthropic both offer significant discounts for repeated prompt prefixes — factor this into your model), output token ratio (output is 4–5× more expensive than input for most models; optimize output length without sacrificing quality), retry rates (failed API calls that need retry add 5–20% to effective cost in production), and evaluation infrastructure (running your evaluation set against new model candidates is a recurring cost that needs to be budgeted explicitly).

Latency Requirements by Use Case

Latency requirements vary by more than an order of magnitude across use cases, and they completely change which models are viable. Before evaluating any model on quality or cost, define your latency budget — it will eliminate candidates more decisively than any other single criterion.

Real-time voice AI is the most demanding latency environment. End-to-end latency (speech recognition + LLM inference + text-to-speech synthesis) must stay under 800ms for natural-feeling conversation — which means LLM response latency must stay under 300ms TTFT. In mid-2025, GPT-4o Mini and Gemini 2.5 Flash consistently achieve 100–200ms TTFT under normal load. Frontier models (GPT-4o, Claude Sonnet) are marginal — often 300–500ms TTFT — and reasoning models are entirely unsuitable for real-time voice.

Interactive chat and copilot experiences: users tolerate 1–2 seconds before a response feels slow; below 500ms TTFT feels fast. Mid-tier models (GPT-4o Mini, Claude Haiku, Gemini Flash) reliably achieve this. Standard frontier models (GPT-4o, Claude Sonnet) are usually acceptable but degrade under provider load. Reasoning models with extended thinking (o3, o4-mini in thinking mode) produce TTFT of 5–60 seconds — too slow for interactive chat but acceptable for users who understand they are waiting for deep analysis.

Background processing, document analysis, and async workflows: latency budget is measured in seconds to minutes, not milliseconds. TTFT is irrelevant; total processing time and cost efficiency matter. This is where frontier models and long-context models shine — Claude Opus 4 processing a 100-page legal document, Gemini 2.5 Pro analyzing an entire codebase, or o3 working through a complex multi-step analysis. Users understand they are waiting for a substantive result.

Batch jobs and offline processing: throughput (requests per minute, total volume per day) matters more than per-request latency. Provider batch APIs (OpenAI Batch API, Anthropic Batch API) offer 50% discounts in exchange for 24-hour completion windows — ideal for nightly data processing, large-scale classification jobs, or generating content at volume. At batch scale, a mid-tier model with batch discounts often costs less than 0.5% of the equivalent frontier model at interactive pricing.

Enterprise Considerations

Enterprise LLM deployments have requirements that individual or startup usage does not: guaranteed uptime, data residency controls, audit logging, volume pricing, fine-tuning access, and support SLAs. Evaluate each of these explicitly — the right model for a developer experiment is often the wrong model for an enterprise production deployment.

Uptime and SLAs: OpenAI Enterprise and Anthropic's enterprise tier both offer contractual uptime commitments and dedicated capacity that reduces the queueing delays that affect standard API users during peak load. Google Cloud's Vertex AI hosts Gemini models with enterprise-grade SLAs backed by Google's infrastructure commitments. If your application's availability is tied to AI availability — customer-facing products, mission-critical workflows — an enterprise tier with SLA commitments is worth the premium over standard API pricing.

Data residency: for organizations with strict geographic data requirements, verify where inference is performed. AWS Bedrock hosts Claude and other models with US and EU inference regions. Google Cloud Vertex AI supports EU-specific regions for Gemini. Azure OpenAI Service supports EU data residency for OpenAI models. Self-hosted open-weight models give you complete geographic control. Do not assume that a US-headquartered provider infers your data in the region you want — verify explicitly.

Fine-tuning and customization: OpenAI's fine-tuning API supports GPT-4o Mini and GPT-3.5, with GPT-4o fine-tuning available to enterprise accounts. Anthropic offers fine-tuning for enterprise customers with sufficient volume. Google offers fine-tuning for Gemini models via Vertex AI. For full customization control, open-weight models allow fine-tuning on any hardware with full weight ownership. Fine-tuned closed-API models cost more per token than the base model but can dramatically improve quality on specialized tasks with smaller prompts.

Volume discounts and committed use: OpenAI, Anthropic, and Google all offer volume discount pricing and committed use agreements at enterprise scale. If your monthly API spend is above $10,000, contact each vendor's enterprise sales team — discounts of 20–50% off list pricing are common at this scale and can significantly change the economics of your model selection. Get competing quotes before signing any committed use agreement.

Integration and API Practical Guidance

The OpenAI API format has become the de facto standard for LLM integration. Anthropic, Mistral, Groq, Together AI, OpenRouter, and many other providers offer OpenAI-compatible APIs that accept the same request format and return the same response structure. Building your application against the OpenAI API format with a configurable base URL and API key means you can switch providers — or add fallback providers — without rewriting your integration logic.

Streaming responses are standard for interactive applications. All major providers support server-sent events (SSE) streaming that delivers tokens as they generate, enabling word-by-word display. Streaming dramatically improves perceived responsiveness — users see the response starting in 200–500ms even if the full response takes 5–10 seconds. Implement streaming from day one for any user-facing text generation feature; retrofitting it later is more work than building it initially.

Rate limits are a practical production concern. OpenAI, Anthropic, and Google all impose requests-per-minute (RPM) and tokens-per-minute (TPM) limits that increase with your spend tier. At low spend levels, rate limits can become a bottleneck before cost does. Common mitigation strategies: exponential backoff retry logic, request queuing to smooth burst traffic, and distributing load across multiple API keys or provider accounts. For extreme throughput requirements, contact provider enterprise teams — custom rate limit increases are available for committed enterprise customers.

SDK ecosystem and tooling: OpenAI and Anthropic both maintain official SDKs for Python, TypeScript/JavaScript, and other languages that handle authentication, retries, streaming, and error handling. The LangChain and LlamaIndex frameworks provide higher-level abstractions that work across providers. For evaluation, Promptfoo, Braintrust, and LangSmith all integrate with the major providers. The tooling ecosystem around OpenAI is the broadest; Anthropic's is close; other providers vary significantly in third-party support.

Switching costs are lower than you think if you architect for portability. The most common portability pitfall is tightly coupling to a provider's unique feature — OpenAI function calling with a specific schema, Anthropic's extended thinking, Gemini's native multimodal API. Where possible, use OpenAI-compatible function calling (now supported by most major providers) rather than provider-specific APIs. Keep your system prompts in a separate configuration layer rather than hardcoded in application logic so you can tune them per-provider without code changes.

Red Flags: When to Reconsider Your Choice

Your error rate is climbing without a clear cause. LLM providers silently update models, and those updates can shift model behavior in ways that break production applications — format changes, increased refusals, different default behavior for edge cases. If your error rate, refusal rate, or quality metrics trend up over 1–2 weeks without any change on your side, run your evaluation set against the current model version and compare against your historical baseline. A silent model update may require prompt adjustments or a provider switch.

Your cost is growing faster than your usage. API costs should scale approximately linearly with request volume. If your cost-per-request is increasing, common causes include: average output length growing (model is generating more verbose responses over time), input prompt length creeping up (conversation history accumulation, context inflation), or caching becoming less effective as your prompt diversity increases. Instrument and monitor cost-per-request alongside request volume to catch this pattern early.

Quality variance is too high for your use case. If your model produces excellent output 80% of the time and unacceptable output 20% of the time, and you cannot reduce that variance with prompt engineering, consider switching tiers rather than continuing to prompt-engineer. Some tasks have irreducible variance at a given capability tier — the consistent solution is a more capable model, not a better prompt. Measure variance explicitly rather than trying to subjectively assess it.

Vendor red flags: significant pricing changes with short notice (OpenAI and Anthropic both have histories of price changes — sometimes cuts, occasionally increases for specific models); feature deprecations on short timelines; capacity issues that affect your SLA; or data governance changes that affect your compliance posture. Monitor provider status pages and developer community forums to catch these early. Maintain at least one tested fallback provider configuration so you can switch within days if needed.

You are using a frontier model for a task that does not require it. This is a quiet red flag that does not look like a problem but costs money unnecessarily. Run an experiment: take your production traffic, route 10% to a mid-tier model, and measure quality outcomes. If the mid-tier model matches quality at one-tenth the cost, you have a cost optimization that improves your margins without harming users. Many teams discover that 60–80% of their frontier model usage could be handled by a cheaper model without meaningful quality impact.

Start Fast, Iterate Based on Evidence

Do not overthink initial model selection. The cost of a suboptimal early choice is low — a few weeks of API fees and the time to re-evaluate. The cost of analysis paralysis is high — delayed product development, missed learning, and decisions made without real usage data. Pick a reasonable starting point: when uncertain and without specific domain requirements, Claude Sonnet 4.5 or GPT-4o are both excellent defaults that cover the widest range of tasks reliably.

Instrument your application from day one to capture the signals that will drive future decisions. Log every request and response with metadata: model used, token counts (input and output separately), latency (TTFT and total), error code if applicable, and any downstream quality signal you can capture (user thumbs up/down, completion rate, follow-up questions that indicate the first answer was insufficient). This data is your evaluation set for future model comparisons.

Re-evaluate model selection on a regular cadence — every 3–6 months is appropriate for most teams. The frontier moves fast: a model that was the clear best choice 6 months ago may have been surpassed by two or three competitors. Use your instrumented production data as the evaluation set for new models. Run new candidates against a sample of your production traffic (or replay logs) and compare quality, cost, and latency against your current baseline.

A/B testing is the most reliable method for production model comparisons. Route a fraction of real traffic (start with 5–10%) to a new model candidate, run for 1–2 weeks, and compare aggregate quality signals between the control and experiment groups. For applications with explicit user feedback (thumbs, ratings, completion), use that as your quality metric. For applications without explicit feedback, use proxy metrics: conversation continuation rate, follow-up question rate, user session length. Statistical significance typically requires at least 1,000 samples per variant — plan your test duration accordingly.

Allow yourself to be surprised. The model that benchmarks best on public leaderboards may underperform on your specific workload. The model that costs 3× more may produce outputs that require 50% less post-processing. The cheaper model may match frontier quality on your classification task. Your production data is the ground truth — trust it over benchmark rankings, community sentiment, or marketing claims. Build the infrastructure to measure, and then let measurement drive decisions.