Text classification and sentiment analysis have shifted from task-specific fine-tuned transformers to general-purpose large language models. Modern LLMs detect nuanced language, sarcasm, and mixed polarity without labeled training data, but production pipelines require careful prompt design, structured output parsing, and cost management for long inputs.
Why LLMs for Classification?
Traditional classifiers need curated datasets and retraining for every new label or schema change. LLMs perform zero-shot and few-shot inference from natural instructions, which makes them ideal when categories evolve weekly or when you need aspect-based sentiment that depends on broader conversational context. Models such as Qwen 3 32B, Llama 3.3 70B, and DeepSeek R1 671B MoE available on Oxlo.ai handle these tasks across multilingual and high-reasoning scenarios without pipeline rework.
Prompt Design and Structured Outputs
Free-text responses from an LLM are fragile to parse. A better approach is to constrain the output with a schema. Oxlo.ai supports JSON mode and function calling, so you can enforce keys such as sentiment, confidence, and aspects directly in the API call. Keep instructions explicit: list the allowed labels, define confidence as a float between 0 and 1, and supply two or three in-prompt examples for ambiguous domains.
Handling Long Inputs and Context Windows
Classifying long documents, legal filings, or extended customer chat histories often exceeds the context limits of older embedding-based approaches. On Oxlo.ai, DeepSeek V4 Flash offers a 1 million token context window, and Kimi K2.6 supports 131K tokens. For material that still exceeds these limits, chunk with overlap and aggregate results, but in practice long-context models preserve cross-sentence coherence better than chunking heuristics.
Cost and Infrastructure Considerations
Token-based pricing scales directly with input length, which makes classifying long documents or running batch jobs over large corpora unpredictable. Oxlo.ai uses flat per-request pricing, so the cost of a sentiment analysis call does not increase when you pass a lengthy product review or a full support transcript. For long-context and agentic workloads, this request-based model can be 10-100x cheaper than token-based alternatives. See https://oxlo.ai/pricing for current plan details.
Implementation Example
The following Python snippet uses the OpenAI SDK with Oxlo.ai as a drop-in replacement. It requests a JSON object so downstream code can parse the result without regex.
import openai
import json
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{
"role": "system",
"content": (
"You are a sentiment classifier. Analyze the user's message and "
"respond with a JSON object containing 'sentiment' (positive, negative, or neutral), "
"'confidence' (0.0 to 1.0), and 'aspects' (list of key topics)."
)
},
{
"role": "user",
"content": "The battery life is amazing, but the customer service was a nightmare."
}
],
response_format
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