When GPT-4 launched in 2023, teams defaulted to it. It was the most capable model available and the safest choice. Two years later, models that cost 95% less handle most of the tasks those same products actually run. But nobody went back to check.
"Switch to a cheaper model" isn't advice you can act on — it's a way to break your product without knowing why. The question isn't which model is cheapest. It's which model is cheapest for your specific task, at your acceptable quality floor.
Here's how to answer that properly.
The Cost Gap Is Larger Than Most Teams Realise
The price difference between model tiers widened significantly through 2025 as providers released capable smaller models. As of February 2026:
| Model | Input ($/1M) | Output ($/1M) | Compared to GPT-4o |
|---|---|---|---|
| GPT-4o | $2.50 | $10.00 | Baseline |
| o1 | $15.00 | $60.00 | 6x more expensive |
| Claude 3.5 Sonnet | $3.00 | $15.00 | Similar |
| o3-mini | $1.10 | $4.40 | Similar |
| Claude 3.5 Haiku | $0.80 | $4.00 | ~3x cheaper |
| Mistral Small | $0.20 | $0.60 | ~13x cheaper |
| GPT-4o-mini | $0.15 | $0.60 | ~17x cheaper |
| Gemini 2.0 Flash | $0.10 | $0.40 | ~25x cheaper |
A team spending $3,000/month on GPT-4o for classification and summarisation could spend under $200/month on GPT-4o-mini or Gemini 2.0 Flash — if the quality holds. At that scale, the annual difference is $33,600.
The question is always whether the quality holds. And for most Tier 1 tasks, it does.
Why "Just Use a Cheaper Model" Fails
Teams try it, see a quality drop on a few examples, revert immediately, and conclude the cheaper model doesn't work. This is the wrong conclusion.
The problem is the absence of a process. Without a proper evaluation, you cannot distinguish:
- "This model is genuinely worse for this task"
- "This prompt needs adjusting for the cheaper model"
- "This task is within the cheaper model's capability range but the prompt was written for GPT-4o"
- "The cheaper model is fine for 90% of cases but fails on specific edge cases we need to handle"
Each of these has a different resolution. Reverting immediately gets you none of that information.
Task Taxonomy: Match the Model to the Work
Not all AI tasks require the same capability. Most products run a mix of task types. The key is matching each task to the cheapest model that handles it reliably — not applying one model to everything.
Tier 1 — Structured Tasks
What they are: Classification, entity extraction, JSON formatting, summarisation to a template, yes/no decisions, data normalisation.
Why cheaper models work: The output space is constrained. There is a right answer, and a well-prompted smaller model reliably produces it. The task does not require nuanced reasoning or complex instruction-following — just pattern recognition and format compliance.
Models to use: GPT-4o-mini ($0.15/$0.60), Claude 3.5 Haiku ($0.80/$4.00), Gemini 2.0 Flash ($0.10/$0.40), Mistral Small ($0.20/$0.60).
Typical saving vs GPT-4o: 10–25x on cost per task.
Example tasks: Classifying a support ticket by category, extracting named entities from a document, summarising a customer call transcript into a structured format, deciding whether an input matches a set of rules.
Tier 2 — Instruction-Following Tasks
What they are: Drafting content, answering questions in context (RAG), code explanation, translating user intent into actions, generating structured responses from unstructured inputs.
Why model choice matters: Output quality depends on instruction-following fidelity, tone calibration, and the ability to weigh competing constraints. Cheaper models can handle these tasks but require better prompts — more explicit instructions, clearer constraints, and sometimes few-shot examples.
Models to try: GPT-4o-mini with carefully written prompts, Claude 3.5 Haiku for shorter contexts, Gemini 2.0 Flash for fast structured output. Fallback to GPT-4o, Claude 3.5 Sonnet, or Mistral Large if quality doesn't hold after prompt tuning.
Typical saving: 2–8x after prompt optimisation.
Example tasks: Drafting a reply to a customer email, answering questions using retrieved context, generating a product description from attributes, summarising a document in a specific voice.
Tier 3 — Reasoning Tasks
What they are: Multi-step planning, complex code generation, evaluating ambiguous or conflicting inputs, mathematical reasoning, architectural decisions.
Why expensive models earn their cost: The task requires holding multiple constraints simultaneously, reasoning across steps, and catching the failure modes of earlier steps. Cheaper models produce plausible-looking but incorrect outputs — the risk isn't a format error, it's a logically wrong answer that isn't obviously wrong.
Models to use: o3-mini ($1.10/$4.40) for reasoning-heavy tasks at lower cost than o1, GPT-4o for complex instruction-following with reasoning, Claude 3.5 Sonnet for long-context analysis requiring careful judgment.
Do not use for Tier 3: GPT-4o-mini, Gemini 2.0 Flash, Claude 3.5 Haiku. These models produce confident-sounding but unreliable outputs on reasoning-heavy tasks.
Example tasks: Generating a migration plan from one system architecture to another, debugging a complex multi-service error, producing code that satisfies multiple competing constraints, evaluating whether a legal clause meets a set of criteria.
The Five-Step Evaluation Process
This process applies when you've identified a Tier 1 or Tier 2 task currently running on an expensive model and want to determine whether a cheaper model can replace it.
Step 1: Audit Your Model Usage
Before changing anything, establish what you have. Which endpoints call which models? What are typical prompt sizes and output lengths for each task?
If you don't know the answer, this is where an AI cost tracking tool helps. Per-model spend visibility tells you which tasks are responsible for the largest proportion of your bill — that's where a successful switch has the highest impact. A task costing $800/month is worth more evaluation effort than one costing $15/month.
Step 2: Classify Each Task by Tier
Map each LLM call in your product to Tier 1, 2, or 3. Most products have three to seven distinct LLM tasks. Classification and extraction are almost always Tier 1. Drafting and Q&A are usually Tier 2. Complex reasoning or planning is Tier 3.
Start with Tier 1 tasks. They have the highest probability of a clean switch and the lowest risk if the switch doesn't work perfectly.
Step 3: Build an Evaluation Set
For each task you want to test, collect 50–100 real examples with known-good outputs. If you don't have human-labelled outputs, use your current model's outputs as a quality baseline — you're testing whether the cheaper model matches, not whether the current model is perfect.
Define a rubric before you run anything. What does "good enough" mean for this specific task?
- Classification: accuracy rate above X%
- Summarisation: all required fields present, no hallucinated content, word count within range
- Extraction: precision and recall on entity types above X%
- Code generation: unit tests pass
Without a rubric, "it looks worse" is not a useful evaluation outcome.
Step 4: Run Head-to-Head Offline
Send the same inputs through both models. Score each output against your rubric. Look at the distribution, not just the average.
A cheaper model might score 92% accuracy against your rubric on average but fail completely on a specific input pattern. If that pattern represents 3% of your real traffic, that's manageable. If it represents 30%, it isn't. Examine the failures — understand whether they're addressable with prompt changes or represent a fundamental capability gap.
If quality is below your threshold, try each of these before concluding the cheaper model can't do the job:
- Simplify the prompt — remove unnecessary instructions the smaller model may be ignoring
- Add few-shot examples — show the model the expected output format explicitly
- Split complex tasks into two simpler calls
- Try a different cheaper model — GPT-4o-mini and Gemini 2.0 Flash have different strengths
Step 5: A/B Test in Production
Route 5–10% of real traffic to the cheaper model. Monitor your quality signals alongside cost signals. Let it run for one to two weeks with sufficient volume to be statistically meaningful.
If quality holds, ramp to 100%. After switching, verify the cost reduction is showing up in your AI spend tracking. A 17x cheaper model should produce a clearly visible drop in per-task spend. If it doesn't, something else changed — prompt length, task volume, or a different task routing to the same model.
Common Failure Modes
Context length mismatch. A cheaper model has a smaller effective context window, or degrades in quality at long contexts even within its stated limit. Symptoms: truncated outputs, confused responses when the document is long, ignoring earlier instructions in a long prompt. Fix: check maximum context window before testing; consider chunking long documents for Tier 1 tasks.
Instruction-following regression. The cheaper model ignores parts of a complex prompt — missing required output fields, wrong format, ignoring stated constraints. Symptoms appear on a subset of inputs where the instructions are more complex. Fix: simplify the prompt, separate complex instructions into sequential calls, or be more explicit about output format with JSON schema.
Latency surprises. Some cheaper models are faster (GPT-4o-mini, Gemini 2.0 Flash); some are comparable to more expensive models. If your product has latency requirements, benchmark p50 and p95 response times, not just average cost. A cheaper model that introduces 3x latency may not be a good trade for a user-facing feature.
Prompt sensitivity. Cheaper models are typically more sensitive to prompt phrasing than larger models. A prompt written for GPT-4o may produce inconsistent results on GPT-4o-mini without adjustment. This is normal — budget time for prompt tuning as part of the evaluation, not as a sign that the model doesn't work.
When Not to Switch
Some tasks should stay on the most capable model regardless of cost.
Safety-critical outputs. Any AI output that a user acts on immediately without human review — medical triage, financial recommendations, legal summaries — should use the most capable model available. The cost of an error exceeds the cost of the API fee.
Tasks where wrong answers are invisible. If your product generates outputs that look correct but may be subtly wrong (architectural advice, compliance checks, code that passes basic tests but has logic errors), a cheaper model's confident-sounding incorrect outputs are a risk. The failure mode is quiet.
Long-context tasks. Tasks requiring accurate reasoning across 50,000+ tokens of context should stay on models with strong long-context performance (Claude 3.5 Sonnet, Gemini 1.5 Pro). Cheap models tend to lose coherence on long contexts even when the context window is nominally large enough.
Tasks without an eval set. Don't switch blind. If you haven't built an evaluation set for a task, you have no way to know whether the cheaper model is performing adequately. Build the eval first.
Measuring the Outcome
The clearest confirmation that a model switch worked is a measurable, sustained drop in per-task spend — visible at the provider level, not just inferred from a pricing calculation. If you switched 80% of your GPT-4o calls to GPT-4o-mini, your OpenAI bill should reflect that clearly within a billing cycle.
Connect OpenAI and Anthropic to StackSpend to track before and after spend at the model level and confirm the savings are real.