Improving LLM Accuracy for Cost

Improving LLM accuracy usually invites assumptions about cost. Teams often believe they must pay a premium for larger models, longer contexts, or repeated retries to get reliable output. That assumption holds on token-based platforms, where every input character and every decoding step adds to the bill. On Oxlo.ai, the equation changes. Because the platform uses flat per-request pricing regardless of prompt length, you can deploy strategies that improve accuracy without the usual cost penalty. The goal is not to spend more. It is to route, structure, and ground each request so the right model produces the right answer the first time.

Model Cascading: Start Small, Escalate Smart

Not every prompt needs a 671B parameter reasoning model. A large fraction of production traffic consists of classification, summarization, or simple extraction tasks that smaller models handle well. The cascading pattern sends the request to a fast, efficient model first, and only promotes it to a larger model if the first response fails a quality gate.

On token-based providers, this pattern can backfire. You pay for both the initial attempt and the fallback, and long prompts in the fallback step are billed at full token rates. Oxlo.ai charges per request, not per token, so a two-step cascade costs exactly two requests. You can use DeepSeek V3.2 or Qwen 3 32B as a first-line worker and reserve DeepSeek R1 671B MoE or GLM 5 for the exceptions.

import openai

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_OXLO_API_KEY"
)

def classify_with_fallback(text):
    # Step 1: Try the fast, efficient model
    first = client.chat.completions.create(
        model="deepseek-v3.2",
        messages=[{"role": "user", "content": f"Classify this ticket: {text}"}],
        max_tokens=50
    )
    content = first.choices[0].message.content

    # Simple quality gate: if the model is uncertain, escalate
    if "uncertain" in content.lower() or "unknown" in content.lower():
        second = client.chat.completions.create(
            model="deepseek-r1-671b",
            messages=[{"role": "user", "content": f"Classify this ticket carefully: {text}"}],
            max_tokens=200
        )
        return second.choices[0].message.content
    return content

Structured Outputs and Function Calling

A common source of poor accuracy is not the model's reasoning but the parsing stage. When a model returns free-form text, your application must extract fields with regex or secondary prompts. That extraction layer introduces errors and often forces a retry. Oxlo.ai supports JSON mode and function calling across its chat models, which lets you constrain the output to a valid schema on the first attempt.

Reducing retries is a direct cost win on any platform, but on Oxlo.ai the benefit is mechanical. A request that returns valid JSON costs the same as a request that returns prose. You do not save money by forcing the model to be concise. You save money by eliminating the second and third requests that recover from a malformed response.

import json

from pydantic import BaseModel

class Extract