Large language model accuracy is not determined solely by pre-training weights. In production, the same checkpoint can produce answers ranging from hallucinated to precise depending on how you structure prompts, manage context, and configure decoding parameters. Because inference architecture matters as much as model selection, optimizing accuracy requires a systematic approach to the entire request pipeline, from token generation strategy to the commercial terms of your API provider.
Select a Model Architecture Matched to the Task
Parameter count is a poor proxy for task fit. A 70B dense model may outperform a 400B+ mixture-of-experts (MoE) on simple summarization, while the MoE excels at deep reasoning or complex coding. The first step in improving accuracy is to match the model architecture to your workload.
Oxlo.ai hosts more than 45 open-source and proprietary models across seven categories, all exposed through a single OpenAI-compatible endpoint. For deep reasoning and math, DeepSeek R1 671B MoE or Kimi K2.6 (131K context, advanced reasoning, and vision) are strong candidates. For multilingual agent workflows, Qwen 3 32B is optimized for tool use across languages. If you need near-state-of-the-art open-source reasoning with an exceptionally large context window, DeepSeek V4 Flash supports 1M tokens in an efficient MoE architecture. General-purpose workloads often land on Llama 3.3 70B or GLM 5 (744B MoE, long-horizon agentic tasks). Because Oxlo.ai is fully OpenAI SDK compatible, switching between these checkpoints is a one-line configuration change.
Engineer Prompts for Determinism and Coverage
Accuracy improves when you reduce ambiguity. Use detailed system prompts, explicit output schemas, and few-shot examples to anchor the model to the correct distribution. For tasks that demand structured output, JSON mode removes the need for fragile regex parsing and cuts post-processing errors.
One often overlooked constraint is the cost of long prompts. Adding few-shot examples, detailed rubrics, or extensive context increases input token count. On token-based providers, this directly raises cost and often forces developers to truncate useful context. Oxlo.ai uses request-based pricing: one flat cost per API request regardless of prompt length. This means you can include comprehensive system instructions and multiple examples without incremental token charges, which often improves accuracy on edge cases.
from openai import OpenAI
client = OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="deepseek-r1-671b",
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