Using LLMs for Complex Coding Tasks

Large language models have moved past simple autocomplete. Today, developers use LLMs for architectural refactoring, cross-language migration, bug diagnosis in million-line repositories, and autonomous agentic coding workflows. These tasks demand more than surface-level pattern matching. They require deep reasoning, extensive context windows, reliable tool use, and pricing that does not punish you for passing the entire codebase into the prompt. The infrastructure you choose determines whether these workloads are practical or prohibitively expensive.

Selecting Models for Deep Reasoning

Complex coding is not a single-turn chat. It involves planning, hypothesis generation, and iterative debugging. You need models that expose chain-of-thought reasoning or are explicitly tuned for code.

Oxlo.ai hosts several models designed for this tier of work. DeepSeek R1 671B MoE excels at deep reasoning and complex coding problems where step-by-step logic matters. Kimi K2.6 offers advanced reasoning with a 131K context window and strong agentic coding capabilities. Qwen 3 32B provides multilingual reasoning and agent workflow support, while GLM 5, a 744B MoE, targets long-horizon agentic tasks. For general-purpose workloads that still require rigor, Llama 3.3 70B serves as a reliable flagship.

Because Oxlo.ai offers 45+ models across seven categories through a single OpenAI-compatible endpoint, you can route high-stakes refactoring to a heavy reasoning model and faster edits to a lightweight code-specialized model such as Qwen 3 Coder 30B or DeepSeek V3.2 without changing your SDK configuration.

Leveraging Long Context for Codebases

Real-world code does not fit into a 4K token window. A complex task might require ingesting dozens of source files, dependency graphs, configuration schemas, and documentation simultaneously. Long-context models eliminate the brittle work of manual chunking and retrieval augmentation.

On Oxlo.ai, DeepSeek V4 Flash supports a 1M token context window and functions as an efficient MoE with near state-of-the-art open-source reasoning. Kimi K2.6 also brings a 131K context window to the table, making it practical to pass entire modules or large diffs in a single request.

Here is where pricing structure becomes critical. Token-based providers scale cost linearly with input length. Feeding a 100K token codebase into a model for every turn of an agent loop generates a massive bill. Oxlo.ai uses flat per-request pricing, so the cost of a complex coding task is predictable whether your prompt is 500 tokens or 50,000 tokens. For long-context and agentic workloads, this can make the workflow significantly more economical. You can see the details at https://oxlo.ai/pricing.

Grounding LLMs with Function Calling

Reasoning alone is not enough. Complex coding agents must verify their work by running tests, reading files, or querying documentation. Function calling lets the model delegate execution to external tools and observe the results.

Oxlo.ai supports function calling, JSON mode, streaming, and multi-turn conversations on its chat/completions endpoint. The API is fully OpenAI SDK compatible, so you can point your existing client at Oxlo.ai by changing the base URL and API key.

The following Python example shows a minimal agent loop that plans a change, generates code, and runs a test via a tool. The model runs on Oxlo.ai, and the tool runs locally.

python
import openai
import json
import subprocess

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

def run_tests(test_path: str) -> str:
    """Run pytest and return the output."""
    result = subprocess.run(
        ["pytest", test_path, "-v"],
        capture_output=True,
        text=True
    )
    return result.stdout + result.stderr

tools = [
    {
        "type": "function",
        "function": {
            "name": "run_tests",
            "description": "Run pytest on a given file and return output",
            "parameters": {
                "type": "object",
                "properties": {
                    "test_path": {"type": "string"}
                },
                "required": ["test_path"]
            }
        }
    }
]

messages = [
    {"role": "system", "content": "You are an expert software engineer. Plan changes, write code, and verify with tests."},
    {"role": "