Text Generation Models for LLM: Oxlo Use Cases

#aiinfrastructure #oxlo #ai

Text generation remains the dominant workload for production LLM applications, yet token-based billing creates a cost cliff as prompts grow. Every additional paragraph of context, turn of conversation, or tool response adds to the meter, making long-context RAG, agentic workflows, and complex coding prohibitively expensive. Oxlo.ai replaces token-based metering with request-based pricing: one flat cost per API call regardless of input length. For long-context and agentic use cases, this can make Oxlo.ai 10-100x cheaper than token-based providers such as Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale. The platform offers 45+ open-source and proprietary models, is fully compatible with the OpenAI SDK, and serves popular models with no cold starts.

Long-Context Document Processing and RAG

Retrieval-augmented generation often requires stuffing large document chunks or entire files into the prompt. Models such as DeepSeek V4 Flash support a 1 million token context, while Kimi K2.6 offers 131K context and advanced reasoning. On token-based platforms, sending a full legal contract, codebase, or research paper can dominate your bill before a single completion token is generated. Oxlo.ai charges a flat rate per request, so a 100K input prompt costs the same as a 1K input prompt. This makes it practical to pass entire documents to Llama 3.3 70B or GPT-Oss 120B without preprocessing truncation strategies that sacrifice recall.

Agentic Workflows with Function Calling

Agents rely on multi-turn conversations, tool outputs, and system prompts that grow with each step. Models like Qwen 3 32B, GLM 5, Minimax M2.5, and DeepSeek V3.2 are designed for agentic tool use and long-horizon tasks. Because Oxlo.ai does not meter tokens, an agent loop that appends lengthy JSON tool responses does not trigger runaway costs. You can use function calling and multi-turn chat/completions endpoints without compressing history to save tokens.

Below is a minimal Python example using the OpenAI SDK with Oxlo.ai. It calls a model that supports function calling and streams the response:

import openai
import os

client = openai.OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key=os.environ.get("OXLO_API_KEY")
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="qwen3-32b",
    messages=[{"role": "user", "content": "What is the weather in Berlin?"}],
    tools=tools,
    stream=True
)

for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Deep Reasoning and Code Generation

For tasks that require extended chain-of-thought reasoning or complex coding, Oxlo.ai hosts DeepSeek R1 671B MoE, Kimi K2.5 and Kimi K2 Thinking, and specialized code models such as Qwen 3 Coder 30B and DeepSeek Coder. These models generate long reasoning traces and multi-file diffs. On token-based infrastructure, both the lengthy input prompts describing a codebase and the extended reasoning outputs incur steep costs. With Oxlo.ai, the request price remains fixed regardless of how many reasoning tokens the model produces, making it viable to let DeepSeek R1 think through a problem without token budgeting.

Structured Output with JSON Mode

Production pipelines often require machine-readable output rather than freeform text. Oxlo.ai supports JSON mode across compatible models, letting you enforce valid JSON schemas without fragile prompt engineering. This is useful for extracting entities, generating configuration files, or building eval pipelines.

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{
        "role": "user",
        "content": "Extract the name, date, and total from this invoice: Invoice #9923, Date 2024-05-01, Total $450.00"
    }],
    response_format={"type": "json_object"}
)

print(response.choices[0].message.content)

Multilingual and General-Purpose Chat

Not every use case requires reasoning or agents. For general chat, translation, and content drafting, Oxlo.ai offers Qwen 3 32B for multilingual reasoning, Mistral, and the general-purpose Llama 3.3 70B flagship. Because the platform is a drop-in replacement for the OpenAI SDK, you can switch existing chat applications to Oxlo.ai by changing the base_url and model name.

Vision and Multimodal Text Generation

Text generation is not limited to text inputs. Models such as Kimi VL A3B and Gemma 3 27B accept image inputs and generate descriptive or analytical text. You can use the same chat/completions endpoint to pass base64-encoded images and receive structured captions or visual reasoning outputs, again under the same flat per-request pricing.

Getting Started with Oxlo.ai

The Oxlo.ai API is available at https://api.oxlo.ai/v1 and works with the official OpenAI Python, Node.js, and cURL clients. There are no cold starts on popular models, so first requests return immediately. New accounts can start on the Free plan, which includes 60 requests per day and access to 16+ free models, plus a 7-day full-access trial. Paid plans scale to Pro and Premium tiers for higher daily volumes, with Enterprise options offering dedicated GPUs and custom pricing.

For current plan details, see the Oxlo.ai pricing page. Because pricing is per request, you can estimate costs directly from your expected traffic without token calculators.