Should I fine-tune a small open model or just prompt a frontier model for high-volume classification?

Deciding between fine-tuning a small open-source model (like Llama 3 8B or Mistral 7B) and prompting a frontier model (like GPT-4o or Claude 3.5 Sonnet) for high-volume text classification comes down to a trade-off between cost at scale, latency , and engineering overhead.

Here is how the two approaches stack up across key dimensions:

• Cost at Scale: Fine-tuning a small model wins decisively here. Inference on a self-hosted or serverless small model (via providers like Together AI, DeepInfra, or AWS Bedrock) is typically 5× to 20× cheaper per million tokens than calling a top-tier frontier model API. If you are classifying millions or billions of items, the upfront training cost is paid back rapidly.
• Latency and Throughput: Small models can be heavily optimized, quantized, and hosted on dedicated GPUs (or via high-throughput engines like vLLM), yielding predictable, low-latency responses. Frontier APIs are subject to rate limits, queue times, and external network latency which can bottleneck high-volume pipelines.
• Accuracy and Complex Reasoning: Frontier models excel out-of-the-box at nuanced, zero-shot, or highly multi-faceted classification tasks. A small model can match or beat a frontier model on a specific taxonomy after fine-tuning, but it requires a clean, labeled training dataset (usually 500 to 5,000+ examples per class).
• Maintenance and Iteration: Prompting a frontier model means when your taxonomy changes, you just rewrite the prompt. Fine-tuning a small model means you must curate new data, retrain, evaluate, and redeploy every time your classification categories evolve.

When to Fine-Tune a Small Open Model

• You have high volume: The monthly classification volume is high enough that API costs for frontier models would run into thousands of dollars.
• Your taxonomy is stable: The categories, rules, and definitions change rarely.
• Data privacy is critical: You cannot send raw customer or proprietary text to a third-party frontier API and must run inference in a secure, isolated VPC or on-premise.
• Speed is paramount: You need sub-50ms responses at massive concurrency.

When to Prompt a Frontier Model

• Low to medium volume: The total volume doesn't justify the engineering effort of managing training data, fine-tuning infrastructure, and model hosting.
• Evolving taxonomy: You are still experimenting with labels, adding new classes weekly, or handling open-ended extraction.
• Zero or low data: You don't have hundreds of high-quality, manually labeled examples to bootstrap a fine-tuning dataset.
• High complexity: The classification relies on complex multi-step reasoning, external context, or deep world knowledge.

The Hybrid "Best of Both Worlds" Approach

A popular modern pattern is to use a frontier model to generate the training data , then fine-tune a small model. You write a robust prompt for GPT-4o or Claude, run a subset of your unlabeled data through it to create a high-quality synthetic dataset, and use that dataset to fine-tune an 8B open model. This gives you the reasoning bootstrap of the big model with the speed and economy of the small one.

To help narrow down the right choice for your specific project, tell me:

• What is your approximate monthly volume (number of items to classify)?
• Do you already have a labeled dataset , or starting from scratch?
• How often do you expect your classification categories to change?

If this is truly high-volume, narrow classification, I’d generally fine-tune a small open model—but I would first use a frontier model to establish a strong accuracy baseline and potentially generate/clean training data.

The key is that you shouldn't frame it as fine-tune vs. prompt as a permanent binary choice. A very good production architecture is often small model for the bulk + frontier model for the uncertain tail.

My rule of thumb

Research generally supports this direction: specialized small models can reach or exceed general LLMs on classification with surprisingly little labeled data; one EMNLP study found specialized models could reach break-even with general models with about 100 labeled examples on average across its tasks.

What I'd actually build

Stage 1 — frontier baseline

Take, say, 2–10k representative examples and run your best frontier prompt against them. Measure:

• macro-F1, not just accuracy
• per-class recall
• confusion matrix
• calibration/confidence
• latency
• cost per 1M classifications

Don't assume the frontier model is the ground truth. For classification, the hard part is often consistent label boundaries, not raw reasoning capability.

Stage 2 — train a small model

Fine-tune a 1–8B open-weight model—or even a much smaller encoder if your inputs are short.

For a straightforward intent/topic classifier, I would absolutely benchmark an encoder model alongside a generative 1–8B model. There is evidence that classification-specific fine-tuning can beat instruction-tuned causal LLMs on F1.

Stage 3 — don't force the small model to handle the tail

Use something like:

                  ┌─ high confidence ──> small fine-tuned model
incoming text ────┤
                  └─ uncertain ────────> frontier model

You can even train the small model to explicitly defer when it's outside its competence.

That changes the economics dramatically: 90–99% of requests can potentially be handled by the cheap model while the expensive model only sees difficult cases.

One important distinction

If by "classification" you mean something like:

"Which of these 37 predefined intents does this customer message belong to?" Fine-tune.

If you mean:

"Read this complicated document and determine which regulatory category applies, explaining your reasoning." I'd start with the frontier model, and only distill/fine-tune once you've accumulated enough examples of the difficult cases.

And if the classification depends on frequently changing knowledge, don't try to bake that knowledge into the fine-tune; use retrieval/context and keep the model's learned behavior separate from current facts.

The economics matter more than the model size

At high enough volume, inference dominates the equation. Recent production analyses consistently find that small tuned models can be dramatically cheaper than frontier APIs when the task is narrow and stable, although there isn't a universal break-even volume because serving/engineering costs vary.

So my default recommendation would be:

Frontier model → create/evaluate labels → fine-tune small model → confidence-based frontier fallback.

If you give me monthly classification volume, average input tokens, number of classes, required accuracy/F1, and your latency target, I can calculate roughly where the break-even point is and what model size I'd start with.

For high-volume classification, I’d usually start with prompting a strong model, then distill/fine-tune only if the economics or latency demand it.

My rule of thumb

The economics have shifted toward prompting because frontier APIs now offer batch discounts and caching. For example, OpenAI's Batch API is 50% cheaper than synchronous processing, while Anthropic similarly offers 50% batch discounts; Anthropic also offers substantially cheaper cached-input pricing.

The architecture I'd actually build

1. Start with a frontier model as your teacher.

Give it:

• a strict label schema
• 10–50 representative examples
• explicit edge-case rules
• structured/JSON output
• a confidence or ambiguity field

Measure accuracy on a held-out, human-labeled test set, not just whether the outputs look good.

2. Then test a small open model against those labels.

Use the frontier model's outputs as candidate training data, but have humans verify a representative subset—especially borderline cases.

Fine-tune something like a 3B–8B model if it can hit your required accuracy.

3. Deploy a cascade rather than choosing one model.

For example:

             ┌─ high-confidence → small model → result
Input ───────┤
             └─ uncertain/complex → frontier model → result

This can give you most of the cost/latency advantages of the small model while retaining frontier-model accuracy on difficult examples.

The key economic calculation

Don't compare:

frontier API price vs. cost of running an open model

Compare total cost per correctly classified item.

For a small model, include:

• GPU/CPU inference
• autoscaling capacity
• engineering/ML maintenance
• fine-tuning experiments
• evaluation
• model updates
• monitoring
• failures/retries

And for the frontier model:

• input tokens
• output tokens
• batch/caching discounts
• retries
• any required human review

For example, current GPT-5.4 pricing is $2.50/M input tokens and $15/M output tokens, with batch pricing available, while Anthropic lists Sonnet 4.6 at $3/M input and $15/M output and Haiku 4.5 at $1/M input and $5/M output, with 50% batch pricing.

So don't assume "open model = cheaper." At moderate volumes, API inference can be surprisingly competitive once you account for infrastructure.

When I'd definitely fine-tune

I'd move toward a small fine-tuned model when you have something like:

• 100k–1M+ classifications/month
• stable label definitions
• relatively short inputs
• a well-defined taxonomy
• thousands+ of good examples
• a meaningful latency requirement
• a measurable API-cost problem

At that point, the small model can be extremely attractive.

One important current wrinkle: if you're considering OpenAI specifically, its current pricing page says the fine-tuning platform is being wound down for new users, so I wouldn't design a new architecture around OpenAI fine-tuning.

Bottom line:
Prompt first → establish a strong accuracy baseline → distill/fine-tune a small open model → use a frontier-model fallback.

If you give me your approximate items/month, average input length, number of classes, required accuracy, and latency target, I can do a concrete $/1M classifications comparison between frontier prompting, a fine-tuned small model, and a cascade.

For high-volume classification, you should fine-tune a small open-weight model (like Llama 3 8B or Mistral 7B) rather than relying on prompt-engineered frontier models. At high volumes, prompt overhead and token costs for frontier APIs accumulate rapidly, making a specialized small model significantly cheaper, faster, and more private.

Comparison of Approaches

When to Choose Each Method

• Fine-Tuning a Small Open Model:
  • High request volume: Economically wins out when handling hundreds of thousands to millions of calls per month.
  • Strict latency requirements: Smaller models process and return classification labels much faster.
  • Data security constraints: Essential if your classification text contains PII, HIPAA-regulated data, or proprietary info that cannot leave your infrastructure.
  • Stable taxonomy: Your classification categories are fixed and well-represented by a training set of 500+ examples.
• Prompting a Frontier Model:
  • Low or unpredictable volume: Ideal if you are processing fewer than tens of thousands of requests monthly, where fixed training costs outweigh API savings.
  • Rapid prototyping: Best for testing a new taxonomy before committing to data collection and model training.
  • Constantly shifting schemas: If your categories change weekly, editing a prompt is faster than retraining weights.

To help decide the best path, tell me:

• What is your estimated monthly volume of classification requests?
• Do you have an existing labeled dataset ready for training?

For high-volume classification, I’d usually start with a frontier model + a very tight prompt/eval, then distill/fine-tune a small open model if the economics justify it.

The key is that classification is unusually friendly to fine-tuning: fixed labels, measurable accuracy, and repetitive inputs. OpenAI’s own guidance recommends starting with prompting, establishing an eval baseline, and then fine-tuning when the remaining problems are consistency/behavior rather than missing context.

My rule of thumb

Why I'd lean toward the small model at scale

Once the task is stable, you're paying a frontier model to repeatedly solve essentially the same narrow problem. A fine-tuned model can encode the decision boundary into its weights, giving you:

• much lower inference cost at sustained volume
• lower and more predictable latency
• shorter prompts
• easier horizontal scaling
• potentially complete control over deployment/data

Distillation is particularly attractive: generate high-quality labels with the frontier model, curate them, then train the small model against those outputs. That's an established workflow specifically intended to trade some general capability for much cheaper task-specific inference.

But don't jump straight to fine-tuning

I'd build this pipeline:

1. Frontier baseline → 2. Eval → 3. Error analysis → 4. Fine-tune small model → 5. Compare economics + accuracy → 6. Add fallback if necessary

For example, suppose you're classifying 20M documents/month into 30 categories.

You might have:

                    ┌─ easy/common ──> fine-tuned 3B/7B model
Input ──> router ───┤
                    └─ uncertain ────> frontier model

That can be substantially better than choosing one model for everything. Recent work on small-model fine-tuning similarly finds that narrow structured tasks can benefit substantially from LoRA + deterministic post-processing, although results are very task-dependent.

One important distinction

If by "classification" you mean something like:

"Is this email about billing, shipping, refunds, or account access?"

I'd strongly favor fine-tuning a small model once you have enough representative examples.

If you mean:

"Read this 10-page document and determine which of 80 nuanced regulatory categories apply, explaining your reasoning."

I'd keep a frontier model, or at least use a hybrid. The complexity and long-tail cases make the small model's apparent cost advantage less decisive.

The break-even calculation

Don't compare just $/1M tokens. Compare:

total cost = inference + GPU/hosting + engineering + fine-tuning + monitoring + error cost

And include the cost of mistakes.

If a misclassification costs $10 and the frontier model is 0.5 percentage points more accurate, that accuracy difference can completely dominate the inference-cost difference.

Conversely, if it's a low-stakes classifier processing billions of records, even a modest per-example saving becomes enormous.

So my default architecture would be: frontier model initially → collect/clean production examples → fine-tune a 1–8B-ish open model → use the small model for the bulk of traffic → route uncertain/high-value cases to the frontier model.

That gives you a way to capture the frontier model's quality without paying frontier prices for every classification.

If you give me volume/month, average input tokens, number of labels, required accuracy, and whether you can run GPUs, I can calculate the approximate break-even point between the two approaches.

Deciding between fine-tuning a small open-source model (like Llama 3 8B or Mistral 7B) and prompting a frontier model (like GPT-4o or Claude 3.5 Sonnet) for high-volume classification comes down to a trade-off between cost at scale, latency , and engineering overhead.

Here is a side-by-side breakdown to help you decide at a glance:

1. Cost & Volume ( Volume > 100k requests)

• Small Open Model (Fine-Tuned):
  • Upfront Cost: High (data preparation, training runs, infrastructure setup).
  • Marginal Cost: Very low (hosting on your own GPU instances like AWS/RunPod or serverless endpoints like Together AI/Baseten).
  • Verdict: Wins heavily at massive volume ( > 100k to millions of daily calls). The savings on per-token inference costs quickly pay off the training investment.
• Frontier Model (Prompted):
  • Upfront Cost: Zero (just write a prompt and call the API).
  • Marginal Cost: High. Paying per 1M tokens for a top-tier model adds up fast when processing millions of classification requests.
  • Verdict: Best for low-to-medium volume, or as a baseline to label data for your fine-tuned model.

2. Latency & Throughput

• Small Open Model (Fine-Tuned):
  • Latency: Extremely fast. An 8B model hosted locally or on a dedicated GPU can output a single-token classification label (e.g., JSON or categorical string) in milliseconds.
  • Throughput: High control over batching and concurrent requests if self-hosted.
• Frontier Model (Prompted):
  • Latency: Higher variance, network-dependent, and generally slower per request due to massive parameter size and API queueing.
  • Throughput: Subject to rate limits unless you negotiate enterprise tiers.

3. Accuracy & Capability

• Small Open Model (Fine-Tuned):
  • Accuracy: Can match or exceed frontier models specifically for your narrow taxonomy . By fine-tuning, you teach it your exact edge cases, label definitions, and formatting constraints.
  • Limitation: Poor at generalizing to new, unseen classification categories without retraining.
• Frontier Model (Prompted):
  • Accuracy: Out-of-the-box brilliance. Handles nuanced, multi-layered, or shifting classification taxonomies instantly via few-shot prompting without needing a training dataset.
  • Limitation: Can suffer from verbosity or drift if prompts aren't strictly constrained (e.g., using JSON mode).

4. Engineering & Maintenance Overhead

• Small Open Model (Fine-Tuned):
  • Overhead: High. You need an annotated dataset (usually 500–5000 examples per class), an evaluation pipeline, a fine-tuning framework (like Axolotl or Unsloth), and a deployment/monitoring strategy for infrastructure.
  • Maintenance: You own the model ops, scaling, and handling uptime.
• Frontier Model (Prompted):
  • Overhead: Low. Writing a robust prompt and setting up API retries takes hours, not weeks.
  • Maintenance: Zero infrastructure management; provider handles updates and scaling.

The Golden Rule: Hybrid Approach

If your volume is high, don't guess—use a hybrid workflow:

1. Bootstrap with a Frontier Model: Use GPT-4o or Claude to label your first 2--5k examples and build your initial dataset.
2. Evaluate a Small Model: Fine-tune a small open model (e.g., Llama 3 8B) on that data.
3. Compare & Deploy: Run an A/B test on a validation set. If the fine-tuned small model matches the accuracy, switch to it for production volume to slash your bill.

If you'd like to narrow this down, tell me:

• What is your approximate daily or monthly volume of classification requests?
• Do you have existing labeled training data , or starting from scratch?
• What kind of infrastructure/hosting setup do you prefer (fully managed APIs vs. self-hosted GPUs)?

Choosing between fine-tuning a small open model and prompting a frontier model for high-volume classification comes down to balancing cost at scale, latency, accuracy requirements , and data privacy.

Here is how the two approaches break down so you can decide at a glance:

• Fine-Tuned Small Open Model (e.g., Llama 3 8B, Mistral 7B):
  • Cost Efficiency at Scale : Exceptionally cheap per token once deployed. For millions or billions of classifications, the compute cost of running a small model locally or on a dedicated endpoint is a fraction of frontier API costs.
  • Latency : Extremely fast inference times, especially if optimized (e.g., via TensorRT-LLM, vLLM, or ONNX quantization).
  • Data Privacy : Complete ownership. Data never leaves your infrastructure, making it ideal for PII, HIPAA, or proprietary enterprise data.
  • Customization : Highly tailored to your specific taxonomy, edge cases, and output formatting. It effectively "bakes in" complex prompt instructions so you don't waste tokens repeating a 500-word system prompt every time.
  • Downside : Upfront investment required for data preparation, training infrastructure, evaluation, and ongoing MLOps/maintenance.
• Frontier Model via API (e.g., GPT-4o, Claude 3.5 Sonnet):
  • Time-to-Value : Zero setup time. You can write a prompt and start classifying data within minutes.
  • Handling Complexity : Superior zero-shot or few-shot performance on nuanced, highly ambiguous, or rapidly changing classification schemas.
  • Downside at Volume : Costs scale linearly and aggressively with volume. Processing millions of high-volume rows can result in massive API bills.
  • Downside with Latency & Rate Limits : Network latency, API rate limits, and occasional service outages can bottleneck high-throughput pipelines.

The Decision Framework

1. Volume & Budget : Calculate your expected monthly classification volume (V). Multiply V by the average input/output token cost of a frontier API. If that cost exceeds the hosting/infrastructure cost of a fine-tuned 8B model plus the amortized engineering time to train it, lean toward fine-tuning.
2. Task Complexity & Stability : If your classes are fixed, well-defined, and you have at least a few hundred (ideally a few thousand) labeled examples, a small fine-tuned model will often match or beat a frontier model's accuracy on that specific domain. If your classes change weekly or require massive world knowledge/reasoning, a frontier model is safer.
3. The Hybrid Approach : Often, the winning architecture for high volume is using a frontier model to generate synthetic training data and bootstrap your taxonomy, and then fine-tuning a small open model to handle the high-volume production traffic.

If you want to dive deeper, tell me:

• What is your estimated monthly volume (number of items to classify)?
• How many classes/categories are you dealing with, and do they change often?
• Do you already have a labeled dataset ready to go?

For high-volume classification, I’d generally fine-tune a small open model—but only after using a frontier model to establish a strong baseline.

The key is that classification is unusually favorable to specialization: fixed labels, repeatable inputs, and a measurable objective. A recent 2026 study found fine-tuned encoder models achieved competitive or better classification quality at 1–2 orders of magnitude lower cost and latency than prompted LLMs on standard benchmarks.

My decision rule

Those volume thresholds aren't laws—the break-even depends heavily on input length, API pricing, hardware utilization, and how much prompting you're doing. But the underlying economics strongly favor specialization as volume rises.

I'd build a two-stage system

1. Frontier model → teacher / benchmark

Use a strong frontier model to:

• establish your accuracy ceiling,
• generate or validate labels,
• identify ambiguous examples,
• discover confusing class boundaries.

2. Small open model → production classifier

Fine-tune something in roughly the 0.5B–8B range, preferably with LoRA/QLoRA initially. Then serve it locally or on inexpensive GPU infrastructure.

Interestingly, recent work shows you may not even need 7–8B: a 2026 study found a 0.8B model reached 94.75% F1 on a structured extraction task, while a 4B model with simple JSON prompting came within 0.35 F1 points of an 8B fine-tuned baseline.

One important twist

Don't assume fine-tuned LLM vs frontier LLM is the only choice.

For pure classification, I'd benchmark:

small encoder classifier → small generative model → frontier model

If the task is essentially text → one of 20 labels, a BERT-style encoder or similarly compact classifier may be dramatically cheaper and faster than even a 1–3B generative model. The 2026 comparative study specifically found this pattern for conventional text classification.

The architecture I'd probably ship

                    ┌─ high confidence ──> small model ──> label
Incoming text ──────┤
                    └─ low confidence ───> frontier model
                                             │
                                             └─> label + review/training data

That gives you cheap bulk inference while reserving the expensive model for the difficult tail. The frontier predictions can continuously become training/evaluation data for the smaller model.

Bottom line: If "high-volume" means hundreds of thousands to millions of relatively stable classifications, I would not pay frontier prices indefinitely. Establish the frontier baseline, fine-tune a small open model, and use confidence-based routing so the frontier model handles only ambiguous cases.

If you tell me monthly volume, average input tokens, number of classes, target accuracy, and latency target, I can work out the likely break-even point and suggest a concrete model size.

For high-volume classification, I’d generally start with a frontier model to establish the quality ceiling, then move to a fine-tuned small model if the economics justify it.

Recent 2026 evaluations support that pattern: fine-tuned small/encoder models can deliver similar or better classification quality at dramatically lower latency and cost, while frontier models remain valuable for ambiguous or changing cases.

My rule of thumb

The key question isn't really "fine-tune or prompt?" It's whether your task has enough regularity for specialization to pay off.

When I'd fine-tune

I'd strongly consider a 1–8B-ish open model if you have:

• 10k+ good labeled examples (though you can start with much less for a narrow task)
• A stable label taxonomy
• A relatively deterministic definition of "correct"
• High enough volume that inference cost/latency matters
• The ability to run evaluation and model serving yourself

You don't necessarily need a huge model. A 2026 study found a fine-tuned 0.8B model reached 94.75% F1 on a structured financial classification/extraction task, while a 4B model with prompting got 96.60%; the larger 8B fine-tune was 96.95%.

Another 2026 study found fine-tuned encoder models had 1–2 orders of magnitude lower cost and latency than prompted LLMs on conventional classification benchmarks.

The architecture I'd actually build

Don't make it an either/or:

Small model → confidence/eval gate → frontier fallback

For example:

• 90–98% of easy cases → fine-tuned 1–4B model
• Low-confidence / novel / ambiguous cases → frontier model
• Periodically collect the frontier-reviewed examples
• Retrain the small model
• Continuously evaluate both against a fixed gold set

That gives you the frontier model's capabilities where they're actually needed without paying frontier prices for every item. Recent production-oriented work has likewise found hybrid small-model approaches can substantially reduce cost while retaining strong accuracy.

One important caveat

If your "classification" is actually something like:

Read this messy customer message and determine which of 37 policies applies, taking into account exceptions and context.

I'd prompt the frontier model first. You may discover that what looks like classification is actually reasoning.

If it's more like:

Given this text, output one of {billing, technical, cancellation, sales}.

I'd move toward a small fine-tuned classifier very quickly.

So my default recommendation: prototype with the frontier model, build a serious eval set, and only fine-tune once you can quantify the gap and the per-item economics. Don't fine-tune merely because the volume is high; fine-tune when the task is stable enough that specialization can amortize its engineering and serving cost.

For high-volume classification, the choice between fine-tuning a small open model (like Llama 3/8B or Mistral) and prompting a frontier model (like GPT-4o or Claude 3.5 Sonnet) usually comes down to a balance between token economics/latency and upfront engineering overhead.

Here is how they stack up to help you decide at a glance:

Option 1: Prompting a Frontier Model

• Upfront Effort : Extremely low. You just write a robust prompt and hit an API.
• Cost per 1M tokens : High. Frontier models charge significantly more per input and output token. At high volume, this bill scales aggressively.
• Latency : Higher per request due to model size and network distance, though improving.
• Flexibility : Maximum. If your classification taxonomy changes tomorrow, you just update your prompt text.
• Best used when : Your volume is moderate, your taxonomy changes frequently, or you don't have an ML engineering team to manage training pipelines and hosting infrastructure.

Option 2: Fine-Tuning a Small Open Model

• Upfront Effort : High. You need to curate/label a training dataset, format it, run the fine-tuning job (via Hugging Face, Axolotl, or a managed platform), evaluate the model, and set up an inference server (e.g., vLLM, TGI).
• Cost per 1M tokens : Very low. Once hosted (on your own GPU or a cost-effective cloud endpoint), running a 7B or 8B parameter model is a fraction of the cost of a frontier API.
• Latency : Blazing fast. Small models hosted on dedicated hardware can process thousands of tokens per second with minimal lag.
• Flexibility : Lower. If you add new classification categories or change definitions, you generally have to re-collect data and fine-tune a new version.
• Best used when : Your data volume is massive (millions+ of classifications a month) where API costs would explode, or you have strict data privacy/latency requirements.

The Decision Framework

1. Calculate the Break-Even Volume : Estimate your monthly token consumption. Multiply the volume by frontier API pricing vs. the cost of fine-tuning + hosting a small model (including GPU instance rental like an A10G or H100). Usually, if you are processing tens of millions of tokens per month , fine-tuning pays for itself quickly.
2. The Hybrid Shortcut (Distillation) : If you aren't sure, use the frontier model to label your initial training data , then fine-tune the small open model on that synthetic dataset. This gives you the reasoning quality of the big model with the speed and cheap scale of the small model.
3. Taxonomy Volatility : If your labels change weekly, don't fine-tune—stick to prompting or a few-shot dynamic prompt approach with a cheaper frontier/mid-tier model.

To help narrow this down, could you share:

• Your estimated monthly volume (number of items to classify per month)
• How often your classification labels or criteria change
• Whether you have existing labeled data ready to go