Complex tasks expose the gap between demo-grade prompts and production-grade systems. When a large language model must reason across multiple steps, ingest hundreds of pages of context, or orchestrate external tools, latency, cost, and accuracy stop being abstract metrics and become hard constraints on what you can ship. This guide examines how to select models, architect workflows, and manage context when the problem cannot be solved in a single turn.
What Makes a Task "Complex" for LLMs?
A complex task is not simply a long prompt. It is any workload that requires the model to maintain coherence across multiple cognitive phases. Common patterns include multi-step reasoning with hidden intermediate states, tool use chains where the output of one API call becomes the input to the next, structured extraction from large unstructured documents, and agentic loops that iterate until a goal state is reached. These patterns fail more often at the integration layer than at the model layer. Your inference backend must support long contexts, function calling, and deterministic output formats without introducing cold-start latency that breaks iterative flows.
Model Selection for Cognitive Load
Not every complex task requires the largest model, but every complex task requires the right model category. On Oxlo.ai, you can route workloads across several specialized families without managing multiple provider accounts.
- Deep reasoning: For mathematical proofs, complex coding, or logic puzzles, use DeepSeek R1 671B MoE, Kimi K2 Thinking, or GLM 5. These models expose chain-of-thought reasoning that improves reliability on tasks where a single jump in logic is insufficient.
- Agentic orchestration: When the model must drive tool use over many turns, Qwen 3 32B, GLM 5, and Minimax M2.5 offer strong function-calling accuracy and instruction following.
- Long-context ingestion: If the task requires analyzing an entire codebase, legal corpus, or multi-turn conversation history exceeding 100K tokens, DeepSeek V4 Flash supports a 1M context window, while Kimi K2.6 provides 131K context with advanced reasoning and vision.
- Code generation and refactoring: Qwen 3 Coder 30B, DeepSeek Coder, and Oxlo.ai Coder Fast are optimized for syntax-aware generation and diff production.
Oxlo.ai hosts these alongside general-purpose flagships like Llama 3.3 70B and multimodal options such as Kimi VL A3B and Gemma 3 27B, so you can downgrade or upgrade a workflow without rewriting client code.
Building Agentic Workflows with Tool Use
Function calling is the glue that turns an LLM from a chatbot into a system component. A robust agent loop requires the model to emit structured tool calls, your application to execute them, and the results to be fed back into context. Oxlo.ai supports function calling,
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