model-routing

A tiny ~10K parameter router called tinyrouter learns which open model to use per question on MMLU, outperforming individual models by optimizing allocation.

SupraLabs releases Supra-Router-51M, a tiny 51M parameter model for routing prompts to appropriate larger or smaller models, enabling low-latency orchestration. A companion dataset is also released.

TinyRouter is a tiny 10K-parameter LLM router that learns to route each question to the best specialist model from a pool of open-source LLMs, using evolutionary training. It achieves performance matching or exceeding individual models on MMLU and math benchmarks.

This article argues that common LLM cost advice focusing on token reduction is too shallow, and that the more impactful strategy in production is to route different workflow steps to different models rather than using a single default model.

The article outlines first principles for effective AI model routing, emphasizing keeping models distinct, limiting pool size, using relative real-world benchmarks, and evaluating past routing decisions for better performance.

A user comments on a routing issue with an Anthropic model warning not to fall for headlines, while another user criticizes the hard guardrails on 'Fable 5' (likely a Claude variant).

Vercel's AI Gateway now supports routing rules that allow developers to dynamically rewrite model routes (e.g., from retired models like Claude Fable-5 to Claude Opus-5) without code changes, ensuring production workloads remain resilient.

EntroRouter proposes a single-round model routing framework that uses entropy regulation to balance accuracy and computational cost, achieving 98.3% of the strongest expert's accuracy while reducing costs by 48.25%.

Proposes a two-stage cascaded framework for cost-aware LLM serving that clusters queries and routes them to cost-effective models, then escalates low-quality outputs to stronger models. Retains 97-99% of accuracy while reducing inference cost.

Row-Bot is an open-source, local-first desktop AI assistant that integrates multiple model providers, tools, memory, and workflows for real work.

This paper proposes the Agent-as-a-Router framework, which transforms model routing into a dynamic, iterative process. Based on task type and real-time execution feedback, it selects the most suitable LLM to improve coding performance and cost efficiency.

Coinbase reportedly reduced AI token spend by half through smart routing to cheaper models like GLM 5.2 and Kimi 2.7 and implementing caching, highlighting a trend in AI cost optimization.

Clement Delangue of Hugging Face explains that 70% of queries to frontier models like ChatGPT could be handled locally for free, arguing that routing to specialized models will redistribute value from large models to a long tail of smaller, more efficient models.

Workweave/router is a tool for smart model routing directly within Claude, Codex, and Cursor, enabling efficient selection of AI models.

Combined results from CursorBench and DeepSWE benchmarks to create a cost-vs-correctness leaderboard for AI coding models, finding that GPT-5.5 Medium offers the best cost/output ratio for everyday coding and that maxing reasoning effort rarely pays off.

Harvey is experimenting with inference-time model routing and blended intelligence techniques to improve agent performance in legal tasks, highlighting both promise and risks.

A seasoned AI engineer shares key skills for 2026, including systematic output reading, context engineering, tool description discipline, eval design, model routing, prompt versioning, confidence scoring, streaming architecture, fallback chains, latency budgets, failure cataloguing, agent-vs-workflow decisions, and failure post-mortems as portfolio content.

Sakana AI releases Fugu Ultra, an orchestration layer that routes subtasks across multiple models via a unified OpenAI-compatible endpoint, matching performance of leading systems.

A detailed blog post explaining the Sakana Fugu technical report, which introduces orchestrator AI models that route tasks to specialized models, achieving collective intelligence.

An analysis of the emerging applied AI layer in enterprises, outlining key components such as building workflow-specific features, intelligent model routing, change management via FDEs, and domain-specific go-to-market strategies. Argues that this layer will create sustainable moats and value despite some critiques.