AI inference costs usually scale with token count, but Oxlo.ai uses a flat per-request pricing model that changes how you should optimize. Instead of trimming prompts to save tokens, you optimize for request count, payload structure, and model selection. The following strategies show how to minimize spend on Oxlo.ai while keeping latency low and output quality high.
Design for Flat Per-Request Pricing
Most providers bill by the token, so long system prompts, large retrieval contexts, and verbose JSON schemas directly increase cost. On Oxlo.ai, a single API request costs the same whether you send a 50-word prompt or a 100,000-token context. This means the primary metric to optimize is the number of round trips, not prompt length.
Strategy: consolidate work into fewer requests. If you are building a RAG pipeline, retrieve all relevant documents and stuff them into a single prompt rather than issuing multiple chained calls. If you are running evaluations or batch processing, group items into one request using structured JSON or bullet lists, then parse the combined output.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_API_KEY"
)
# One request with multiple documents
response = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[{
"role": "user",
"content": (
"Summarize the following three reports:\n\n"
"Report 1: ...\n\n"
"Report 2: ...\n\n"
"Report 3: ..."
)
}]
)Batch Long-Context and Agentic Workloads
Long-context and agentic workloads are where Oxlo.ai diverges most sharply from token-based competitors. Because cost does not scale with input length, you can pass entire conversation histories, code repositories, or multi-page documents without penalty. Agent loops that build context over time, tool outputs, and chain-of-thought traces can all live in a single request window.
Practical tip: use models with large context windows such as DeepSeek V4 Flash (1M context) or Kimi K2.6 (131K context) to keep multi-step reasoning in one shot. Fewer round trips mean fewer requests, which directly lowers your bill.
Consolidate Agentic Steps into Single Calls
When you pay per request, every tool call, planner step, or reflection loop costs money. Oxlo.ai supports function calling and multi-turn conversations, so you can often embed decision logic inside the prompt rather than orchestrating it externally.
For example, instead of a workflow where one request classifies a ticket, a second request extracts entities, and a third request drafts a response, you can ask a capable model such as GLM 5 or Qwen 3 32B to perform all three subtasks in one structured output. Use JSON mode to enforce a parseable schema.
response = client.chat.completions.create(
model="glm-5",
response_format={"type": "json_object"},
messages=[{
"role": "system",
"content": (
"You are a support analyst. Given a ticket, output JSON with "
"keys: classification, entities, and draft_reply."
)
}, {
"role": "user",
"content": "My order #12345 has not arrived after two weeks."
}]
)Select the Right Model and Tier
Oxlo.ai hosts 45+ models across seven categories, from lightweight embeddings to large reasoning MoEs. Not every task needs a 744B parameter model. For routine extraction, formatting, or translation, use efficient general-purpose models such as Llama 3.3 70B or Qwen 3 32B. Reserve heavyweights like DeepSeek R1 671B or GPT-Oss 120B for deep reasoning and complex coding.
Take advantage of the free tier to benchmark. It includes 60 requests per day across 16+ models, plus a 7-day full-access trial. If you outgrow the free limit, the Pro and Premium plans offer 1,000 and 5,000 requests
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