Natural language processing has moved beyond specialized pipelines for tokenization, part-of-speech tagging, and named entity recognition. Modern large language models handle classification, summarization, translation, and structured extraction within a single inference call. For engineering teams, the challenge is no longer model architecture but inference economics, especially when prompts grow to thousands of tokens for document-level tasks.
From Pipelines to Prompts: The Unified NLP Stack
Traditional NLP stacks required distinct models for each task. Today, a general-purpose LLM like Llama 3.3 70B or Qwen 3 32B can perform sentiment analysis, entity extraction, and question answering through prompt engineering and tool use. Oxlo.ai hosts these models behind a unified API that is fully OpenAI SDK compatible, so you can switch from prototyping on closed providers to production on open models by changing the base URL.
Long-Context Document Processing
Real-world NLP workloads often involve analyzing entire reports, legal contracts, or codebases. Input length directly impacts cost on token-based platforms. Oxlo.ai uses request-based pricing, so you pay one flat cost per API call regardless of prompt length. For long-context NLP, this can be significantly cheaper than token-based alternatives. Models such as DeepSeek V4 Flash support a 1 million token context window, and Kimi K2.6 handles 131K tokens with advanced reasoning, making them suitable for document QA and multi-section analysis without truncation.
Structured Extraction with JSON Mode
Many NLP pipelines require machine-readable output. The following example uses Oxlo.ai with the OpenAI SDK to extract entities from unstructured text. We will use JSON mode to guarantee valid output.
import openai
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": "Extract person, organization, and location entities as JSON."},
{"role": "user", "content": "Apple Inc. is planning to open a new office in Berlin by 2026, according to Tim Cook."}
],
response_format={"type": "json_object"}
)
print(response.choices[0].message.content)
Because Oxlo.ai offers no cold starts on popular models, the first request after idle time returns as quickly as subsequent ones. This matters for NLP microservices that must respond to sporadic traffic.
Multilingual Translation and Reasoning
Global applications need NLP across languages. Qwen 3 32B is built for multilingual reasoning, while GLM 5 and Kimi K2.5 handle advanced chain-of-thought reasoning across mixed-language inputs. You can route translation tasks to the same chat completions endpoint without managing separate translation APIs.
response = client.chat.completions.create(
model="qwen-3-32b",
messages=[
{"role": "system", "content": "Translate the user's text into English and preserve the technical terminology."},
Top comments (0)