Document summarization is one of the most common production workloads for large language models, but running it at scale exposes a hard infrastructure problem: cost grows linearly with document length on token-based platforms. For teams processing reports, legal contracts, or research papers, a single inference call can consume tens of thousands of tokens before the model generates a single summary sentence. This is where inference economics and context architecture matter as much as prompt engineering.
The Challenge of Summarizing Long Documents
Most real-world documents do not fit neatly into a few paragraphs. A standard corporate filing, technical manual, or patient history can easily exceed 50,000 tokens. When you are billed per token, every paragraph you add to the prompt increases cost before the model even begins to reason. The result is a forced trade-off between fidelity, where you include the full text, and economy, where you chunk the document and lose cross-section context.
Architectural Approaches to Document Summarization
Engineers typically choose between three patterns:
- Stuffing: Pass the entire document in a single prompt. This preserves global context and requires the least orchestration, but it demands a model with a large context window.
- Map-reduce: Split the document into chunks, summarize each independently, then summarize the summaries. This works with smaller context windows but introduces redundancy and can miss themes that span chunks.
- Refine: Iteratively update a running summary as each new chunk is processed. This reduces information loss but requires serial API calls and state management.
As context windows have expanded to 128,000 tokens and beyond, stuffing has become the preferred architecture for production pipelines. It eliminates chunking logic, reduces latency by avoiding multiple round trips, and simplifies error handling. The prerequisite is an inference provider that offers models with sufficiently large context windows and pricing that does not punish long inputs.
Why Context Window Size Changes the Economics
On token-based providers, input tokens often cost as much as or more than output tokens. A 100,000-token prompt might therefore dominate the bill, especially if you are iterating on prompts or running batch jobs across a document corpus. Multi-turn refinement and evaluation loops compound the problem.
Oxlo.ai uses request-based pricing. You pay one flat cost per API call regardless of prompt length. For long-context summarization, this means you can send an entire legal brief to Kimi K2.6 with its 131,000-token context window, or an entire book to DeepSeek V4 Flash with its 1,000,000-token context window, without watching input tokens drive up cost. The same per-request rate applies whether your prompt is ten tokens or one hundred thousand.
This pricing model also removes the penalty for experimentation. You can test zero-shot against few-shot prompts, run A/B summaries across multiple models, or build evaluation pipelines that score output quality, all without token economics eroding your margin. See https://oxlo.ai/pricing for current plan details.
Implementing Summarization with the OpenAI SDK
Oxlo.ai is fully OpenAI SDK compatible. If you are already using the OpenAI client, you can switch the base_url and start summarizing immediately. The example below sends a full document to Kimi K2.6 with streaming enabled.
from openai import OpenAI
import os
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ["OXLO_API_KEY"]
)
long_document = """
[Insert your document text here. Kimi K2.6 supports up to 131,072 tokens
and DeepSeek V4 Flash supports up to 1,000
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