Commonstack

One API to access the best AI models in the world.
Faster agents, lower costs.

[Commonstack @commonstack_ai]

Fraction of the bill. Same results.

Fully local, open source, works with any client.

Just > pipx install uncommon-route

https://t.co/6bJ9WhtB52

[Hanchen Li @lihanc02]

A lot of routing work evaluates isolated prompts, but real agent systems are fundamentally multi-step and budget-constrained. Cool to see benchmarks moving toward execution-grounded, end-to-end evaluation instead of just token-level proxies.

TwinRouterBench is a strong step toward realistic agentic routing evaluation — especially the separation between static supervision and dynamic SWE-bench execution. Excited to see where this goes!

[Commonstack @commonstack_ai]

Great to see TwinRouterBench accepted to the #RLEval Workshop at #CAIS2026!

Per-step routing is quickly becoming essential infrastructure for agentic systems: each planning, coding, retrieval, and verification call should use the cheapest sufficient model without hurting final task success.

Proud to open-source TwinRouterBench and contribute a practical benchmark for this problem.

[Yuhang Yao @yuhang_yao]

Excited to share that TwinRouterBench has been accepted to the #RLEval Workshop at #CAIS2026 🎉

As LLM apps become long-horizon agents, one request can trigger many model calls across planning, tool use, retrieval, coding, and verification.

That makes per-step LLM routing a core infrastructure problem: sending each call to the cheapest sufficient model without breaking downstream success.

TwinRouterBench introduces:

⚡ Static track: 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench

🚀 Dynamic track: live SWE-bench Verified evaluation with official task resolution + realized API spend

Key result: a router trained on static labels achieves comparable SWE-bench resolve rate while cutting API cost by ~53% vs. an unrouted Opus 4.6 baseline.

Paper: https://t.co/dVZaZvvUk5
Code: https://t.co/ZRnswiW3T7
Dataset: https://t.co/65l1rEcOs7
Website: https://t.co/O45KZwLPCt
#LLM #AgenticAI #LLMRouting #Benchmark #SWEBench

[Commonstack @commonstack_ai]

@yuhang_yao @lihanc02 @RLCommons 🎉🎉

[Alex Mirran @alex_mirran]

https://t.co/imNJtDZqls

[Commonstack @commonstack_ai]

Step-level routing matters. The benchmark to measure it is open today.

Bench: https://t.co/cCyKNLxuKl

The current leader: https://t.co/6bJ9Whu8UA

Paper coming soon on ArXiv.

[Commonstack @commonstack_ai]

How do you evaluate an LLM router fairly?

Most benchmarks look at prompts, but routers operate at an agentic-step level. A router that saves money but breaks the task could be worse than no router.

We open-sourced TwinRouterBench to measure this honestly.
🧵

[Commonstack @commonstack_ai]

Conflict of interest? acknowledged!

We know our router (UncommonRoute) currently leads the leaderboard.

Open submissions, locked pricing, public scoring code. If a different router wins, the leaderboard will say so.

[Commonstack @commonstack_ai]

Run Claude Code with Commonstack in 4 steps:

- generate an API key
- set 4 environment variables
- run claude
- /status to verify

Set it up now in 5 minutes with @alex_mirran.

[Commonstack @commonstack_ai]

Quickstart -> https://t.co/Lnf9xBw2bJ.

[Commonstack @commonstack_ai]

GPT-5.5 is live on https://t.co/L4uejEYZ40! 🚀🚀

Use the strong reasoning and coding capabilities of GPT-5.5 in your application or with your favorite agentic harness.

[Commonstack @commonstack_ai]

Here's a guide for using Commonstack with your OpenClaw agent -> https://t.co/cCV3swMcOY.

[Alex Mirran @alex_mirran]

https://t.co/w13o7xWEYe

[Commonstack @commonstack_ai]

DeepSeek-V4-Flash is now live on https://t.co/ksndyxekOL

Time to feed your agents!

[DeepSeek @deepseek_ai]

DeepSeek-V4-Flash

🔹 Reasoning capabilities closely approach V4-Pro.
🔹 Performs on par with V4-Pro on simple Agent tasks.
🔹 Smaller parameter size, faster response times, and highly cost-effective API pricing.

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