One API. One key. Access all major AI models. We turn model access into infrastructure — so you can focus on building your product.
"Commonstack is building the AI infrastructure for the agentic era — making it dramatically easier for developers and enterprises to build AI agents and intelligent applications, while significantly reducing the cost and complexity of using AI."
Nine core capabilities that turn fragmented AI model access into a unified, reliable, and cost-efficient infrastructure layer.
A lightning-fast local LLM smart router for Cursor, Claude Code, Codex & OpenAI SDK. Route by difficulty. Refuse habitual waste.
$ pip install uncommon-routeMost AI coding tools use a one-size-fits-all strategy — every request, no matter how trivial, goes to the most expensive model. A single developer's monthly bill can balloon 3–5× unnecessarily.
"Design a fault-tolerant distributed database"→ Top model ✓"Calculate 2 + 2"→ Top model ✗ (wasteful)Every prompt is classified in real time — matched to the right model, not the most expensive one.
Choose the strategy that fits your needs — or let auto decide for you.
Default choice — intelligently balances cost and quality for every request.
The AI model landscape changes every few weeks. When a new model drops, how does the router know whether to use it — and for which tasks? The answer is a continuous feedback loop built directly into the UncommonStack platform.
Static routing rules become stale. A model that was "best for complex code" last month may be surpassed by a cheaper alternative today. Without a feedback mechanism, the router can't adapt.
UncommonStack collects real-world routing feedback from every request on the platform. When a new model appears, it enters a live evaluation pipeline — automatically benchmarked against existing routing decisions. The router's difficulty classifier and model-tier mappings are updated continuously, without manual intervention.
Every routed request generates a signal: which model was used, what was the task difficulty, and what was the outcome.
New models are automatically inserted into RouterBench — our 4-phase benchmark system — and scored against the existing model pool.
Ground truth routing labels are refreshed. The classifier learns which new model handles which task tier best, at what cost.
Updated routing policies are pushed to UncommonRoute. Users automatically benefit from the new model without changing any code.
A rigorous benchmark pipeline that generates ground-truth routing labels for multi-step agent tasks. It powers both the Self-Evolving Router and our published evaluation results.
Passed cases / Total valid casesDid the routed path complete the task above the target threshold?
100 × Σ save_test / Σ save_gtHow much of the ground-truth cost savings did the router actually capture? Scored 0–100.
baseline.cost − cost_test (USD)Nominal dollar savings using fixed tier prices — comparable across experiments and time.
Claude Opus 4.6$25/1MGPT-5.4$15/1MClaude Haiku 4.5$5/1MGemini 3 Flash$3/1MMiniMax M2.5$1.15/1MQwen3.5-27B$1.56/1MDeepSeek V3.2$0.38/1MGLM-4.5-Air$0.85/1MNot theoretical — validated across 131 agent coding sessions, 763 handwritten prompts, and 200+ model routing experiments.
Near-zero-perception local ultra-fast decision making. The routing engine runs entirely on your machine — no network round-trip, no API call, no latency overhead.
"Systems thinking beats model maximalism."
Stop paying for the brand. Start paying for the result. Find hidden gems that are fast, well-priced, and highly successful.
A team of competitive programmers, industry veterans, and researchers — united by a shared mission to build the AI infrastructure for the agentic era.
Competitive programming excellence
UC Berkeley, CMU, Columbia, Imperial College London, University of Toronto
Experience across Google, Apple, Microsoft, and ByteDance
Backed by Sequoia China (HongShan)
From multimodal model routing, to Skills and MCP tool ecosystems, to model fine-tuning, reinforcement learning, and inference acceleration — we are building a complete AI stack for the next generation of intelligent systems.
Our vision is to make it dramatically easier for developers and enterprises to build AI agents and intelligent applications, while significantly reducing the cost and complexity of using AI.
