Optimizing LLMs for Complex Tasks

#aiinfrastructure #oxlo #ai

Complex tasks, whether multi-step software engineering, autonomous research, or multi-modal analysis, expose the limitations of single-shot prompting. As context windows grow and reasoning capabilities improve, the bottleneck shifts from raw model intelligence to orchestration: how you decompose work, manage state across turns, and select the right model for each phase. This article covers practical patterns for optimizing large language models for demanding workloads, and where infrastructure choices such as Oxlo.ai's request-based pricing change the economics of agentic execution.

Decomposition and Chain-of-Thought Reasoning

Complex tasks rarely succeed in a single pass. Decomposition breaks a problem into discrete subtasks that can be validated, retried, or routed to specialized models. Chain-of-thought reasoning further improves reliability by forcing the model to articulate intermediate steps before emitting a final answer.

Oxlo.ai hosts several models optimized for these patterns. DeepSeek R1 671B MoE and Kimi K2.6 support advanced reasoning and agentic coding, while Kimi K2.5 and Kimi K2 Thinking expose explicit chain-of-thought traces. GLM 5, a 744B MoE, targets long-horizon agentic tasks that require planning over many steps.

A simple but effective pattern is to use a high-level planner model to generate a task list, then delegate each item to a faster model or a code specialist. Because Oxlo.ai charges one flat cost per request, not per token, running a planner with a long system prompt followed by multiple tool calls does not inflate your bill the way token-based providers do.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.oxlo.ai/v1",
    api_key="YOUR_API_KEY"
)

# Step 1: Plan with a reasoning model
plan_response = client.chat.completions.create(
    model="deepseek-r1-671b",
    messages=[{
        "role": "system",
        "content": "You are a task planner. Break the user request into subtasks."
    }, {
        "role": "user",
        "content": "Build a Python CLI that fetches weather data and caches it locally."
    }]
)

plan = plan_response.choices[0].message.content

# Step 2: Execute each subtask with a generalist or code model
for subtask in parse_subtasks(plan):
    client.chat.completions.create(
        model="qwen3-32b",
        messages=[{"role": "user", "content": subtask}],
        tools=[{"type": "function", "function": {"name": "write_file", ...}}]
    )

Tool Use and Function Calling

Function calling turns an LLM from a text generator into an actor that can query APIs, execute code, or interact with databases. For complex tasks, you will often chain multiple tool calls across several turns, accumulating context as the workflow progresses.

Oxlo.ai supports function calling and multi-turn conversations on models such as Llama 3.3 70B, Qwen 3 32B, and Minimax M2.5. Because the platform is fully OpenAI SDK compatible, you can drop existing tool-use code in by changing the base URL.

import json
from openai import OpenAI

client = OpenAI(base_url="https://api.oxlo.ai/v1", api_key="YOUR_API_KEY")

tools = [{
    "type": "function",
    "function": {
        "name": "run_sql",
        "description": "Execute a read-only SQL query",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string"}
            },
            "required": ["query"]
        }
    }
}]

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{
        "role": "user",
        "content": "What was the total revenue last quarter? Use the run_sql tool."
    }],
    tools=tools,
    tool_choice="auto"
)

if response.choices[0].message.tool_calls:
    call = response.choices[0].message.tool_calls[0]
    args = json.loads(call.function.arguments)
    # Execute args["query"] and feed the result back into the conversation

With competitors such as Together AI, Fireworks AI, OpenRouter, Replicate, and Anyscale, costs scale with every token in the system prompt, tool definitions, and conversation history. Oxlo.ai's request-based pricing removes that penalty, so you can include verbose JSON schemas and long tool descriptions without worrying about input length.

Managing Long Context for Agentic Workloads

Agentic workflows consume context quickly. A single ReAct loop can append observations, tool outputs, and error traces across dozens of turns. When pricing is token-based, each extra paragraph of context increases cost. Oxlo.ai uses flat per-request pricing, so long inputs do not change the price of the call. This makes it significantly cheaper for long-context and agentic workloads compared to token-based providers.

For tasks that require massive context windows, DeepSeek V4 Flash offers a 1 million token context and efficient MoE architecture. Kimi K2.6 supports 131K context with advanced reasoning and vision, making it suitable for analyzing large codebases or lengthy documents in a single session.

messages = [{"role": "system", "content": "You are a software architect. ..."}]

while not task_complete:
    response = client.chat.completions.create(
        model="kimi-k2.6",
        messages=messages,
        tools=tools,
        stream=True
    )

    # Append assistant message and any tool results
    messages.append({"role": "assistant", "content": response_text})
    messages.append({"role": "tool", "content": tool_result})

Streaming responses let you emit partial results to the user while the model continues reasoning, which improves perceived latency in interactive agents. Oxlo.ai supports streaming on all chat models with no cold starts on popular models, so the first chunk arrives immediately.

Selecting the Right Model for Each Phase

Not every step in a complex pipeline needs the largest model. Routing simple tasks to smaller, faster models reduces latency and cost, even under flat pricing. Oxlo.ai offers 45+ models across 7 categories, so you can match capability to requirement.

Deep reasoning: DeepSeek R1 671B MoE for complex coding or math.
Agentic coding and vision: Kimi K2.6, or Kimi K2.5 for chain-of-thought reasoning.
General orchestration: Llama 3.3 70B or Qwen 3 32B for multilingual agent workflows.
Code generation: Qwen 3 Coder 30B, DeepSeek Coder, or Oxlo.ai Coder Fast.
Long-horizon planning: GLM 5 (744B MoE).
Efficient high-volume steps: DeepSeek V3.2 or DeepSeek V4 Flash for coding and reasoning.

For vision tasks, Gemma 3 27B and Kimi VL A3B handle image inputs. For audio pipelines, Whisper Large v3 and Kokoro 82M cover transcription and text-to-speech. Oxlo.ai also provides embeddings via BGE-Large and E5-Large for retrieval steps that feed context into the main agent loop.

Implementation Tips for Production Agents

When moving from prototype to production, structure and observability matter more than model size.

Use JSON mode to constrain outputs to valid schemas. This is especially useful when a model must emit structured parameters for the next tool in a pipeline. Oxlo.ai supports JSON mode across compatible models.

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{
        "role": "user",
        "content": "Generate a structured bug report from this traceback."
    }],
    response_format={"type": "json_object"}
)

Keep system prompts versioned and explicit. Because Oxlo.ai does not charge per token, you can afford to be detailed in your instructions, but you should still monitor prompt quality to avoid confusing the model.

Finally, evaluate your total cost structure. Token-based providers scale charges with input length, which penalizes agentic patterns. Oxlo.ai's flat per-request pricing can be 10-100x cheaper for long-context workloads. You can view current plans at https://oxlo.ai/pricing.

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