Large language models have moved well beyond chat demos into production systems that handle contracts, write code, moderate content, and drive agentic workflows. Yet the shift to production exposes a predictable pain point: token-based billing makes costs volatile, especially when processing long documents or running multi-step agents. Oxlo.ai approaches this with request-based pricing, charging one flat cost per API call regardless of prompt length, which makes long-context and agentic workloads significantly more predictable.
Intelligent Document Processing at Scale
Legal, financial, and healthcare teams routinely ingest PDFs, scans, and transcripts that exceed tens of thousands of tokens. With token-based billing, a single long document can generate unpredictable costs. Oxlo.ai’s flat per-request model removes that variance.
For deep analysis of lengthy contracts or research papers, models like DeepSeek V4 Flash support a 1 million token context window, while Kimi K2.6 offers advanced reasoning across 131K contexts. Because Oxlo.ai bills by request, summarizing a 100-page document costs the same as a one-line greeting.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[
{"role": "system", "content": "You are a legal assistant. Extract all indemnification clauses."},
{"role": "user", "content": "..."} # long document text
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")
Streaming responses arrive without cold starts on popular models, so document pipelines keep moving.
Agentic Coding and Tool Use
Software teams are building agents that read codebase context, call linters, open pull requests, and iterate on feedback. These workflows are inherently multi-turn and can carry large system prompts. On token-based platforms, the cost scales with every file included in context. On Oxlo.ai, the price remains flat per request.
Models such as DeepSeek R1 671B MoE excel at complex reasoning and coding tasks, while Qwen 3 32B and Minimax M2.5 handle agentic tool use. Oxlo.ai Coder Fast and DeepSeek Coder provide specialized endpoints for generation and refactoring.
The platform supports function calling and JSON mode, which lets agents return structured outputs that feed directly into CI/CD pipelines.
tools = [
{
"type": "function",
"function": {
"name": "run_tests",
"description": "Execute the test suite",
"parameters": {
"type": "object",
"properties": {
"target": {"type": "string"}
},
"required": ["target"]
}
}
}
]
response = client.chat.completions.create(
model="qwen3-32b",
messages=[{"role": "user", "content": "Refactor auth.py and run tests"}],
tools=tools,
tool_choice="auto"
)
Multimodal Customer Support
Support teams increasingly handle screenshots, audio transcripts, and chat history together. Oxlo.ai offers vision models such as Gemma 3 27B and Kimi VL A3B for image understanding, alongside Whisper Large v3 for audio transcription and Kokoro 82M for text-to-speech responses.
Because the API is fully OpenAI SDK compatible, adding vision to an existing chat pipeline requires only a model name change.
response = client.chat.completions.create(
model="gemma-3-27b-it",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What error does this screenshot show?"},
{"type": "image_url", "image_url": {"url": "https://example.com/error.png"}}
]
}
]
)
After transcription by Whisper, a support agent built on Llama 3.3 70B or GPT-Oss 120B can generate a response, which Kokoro can vocalize for phone channels. All endpoints share the same base URL and authentication.
Automated Content and Asset Generation
Marketing and creative teams use LLMs to draft copy, then generate matching imagery. Oxlo.ai unifies these steps under one API. Text generation runs through models like Llama 3.3 70B or GLM 5, while image generation uses Flux.1, Stable Diffusion 3.5, SDXL, or Oxlo.ai Image Pro and Ultra.
image = client.images.generate(
model="flux.1",
prompt="A technical diagram showing API request flow, dark mode, minimal",
size="1024x1024"
)
With no cold starts, batch jobs that interleave text and image calls proceed without delay. Request-based pricing means a detailed prompt with extensive negative constraints costs the same as a simple one.
Retrieval-Augmented Generation and Embeddings
RAG systems embed proprietary documents, store vectors, and retrieve relevant chunks at query time. Oxlo.ai provides embedding models including BGE-Large and E5-Large through the standard embeddings endpoint.
embed = client.embeddings.create(
model="bge-large",
input="Oxlo.ai offers flat per-request pricing for open-source LLMs."
)
# Later, retrieve and generate
response = client.chat.completions.create(
model="kimi-k2.6",
messages=[
{"role": "system", "content": "Answer using only the provided context."},
{"role": "user", "content": retrieved_context + "\n\nQuestion: How is billing structured?"}
]
)
For agentic RAG, Kimi K2 Thinking and Kimi K2.5 apply advanced chain-of-thought reasoning to synthesize retrieved sources. Because Oxlo.ai charges per request rather than per token, expanding context with additional retrieved chunks does not inflate the API bill.
Why Request-Based Pricing Wins in Production
Production workloads share a common trait: they are repeated, automated, and sensitive to cost variance. A token-based bill that doubles because users uploaded longer files is difficult to forecast. Oxlo.ai’s flat per-request pricing can be 10-100x cheaper than token-based alternatives for long-context workloads, and it simplifies budgeting from prototype to scale.
The platform hosts 45+ open-source and proprietary models across seven categories, all accessible through a single OpenAI-compatible endpoint at https://api.oxlo.ai/v1. There are no cold starts on popular models, and features like streaming, JSON mode, and multi-turn conversations work out of the box.
For teams exploring options, Oxlo.ai offers a free tier with 60 requests per day across 16+ models, including a 7-day full-access trial. Paid plans scale predictably, and Enterprise customers receive dedicated GPUs with guaranteed savings over their current provider. See https://oxlo.ai/pricing for details.
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