Defined term
Frontier model
The leading-edge foundation models with the highest reasoning, coding, and multimodal capabilities.
Frontier models are the most capable models available at a given moment: Anthropic Claude Opus family, OpenAI GPT/o-series flagship, Google Gemini Pro/Ultra, etc. They handle the hardest reasoning tasks but cost more per call and have higher latency. Production architectures typically reserve frontier models for high-stakes decisions and use smaller models for routine work.
When it matters
When the workflow requires hard reasoning, multi-step planning, or rare-language coverage. Pay the premium for the steps that need it; route everything else to smaller models.
Real example
A multi-LLM workflow using Claude Opus 4 only for the final answer-synthesis step on high-stakes legal-research queries (5% of traffic, 30% of value). Routine retrieval and classification stays on Haiku.
KPIs to watch
% of inference calls routed to frontier models (target: 5-15% for cost-optimized workflows), quality lift on those calls (must justify 10-20× cost vs mid-tier), latency P95 on frontier-routed calls (<10s typical).
Related terms
LLM (Large Language Model)
A transformer-based model trained on language data to predict and generate text.
Multi-LLM architecture
Routing different tasks to different models based on cost, quality, latency, and capability tradeoffs.
Foundation model
A large model pre-trained on broad data, then adapted to many downstream tasks.
Context window
The maximum number of tokens a model can process in a single request.
See it in action
We use this every week
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