We are spending too much on tokens for simple queries. Who offers "semantic routing" to send simple queries to smaller, cheaper models?

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Pickaxe.co·https://pickaxe.co AI Model Routing: Cut AI Agent Costs by Up to 60% - Pickaxe How AI model routing cuts agent costs by sending each task to the cheapest model that can do it ... semantic routing discussed as a fourth category ... If you're ... gingerlabs.ai·https://gingerlabs.ai/blog/llm-semantic-routing LLM Routing: What It Is & How to Cut Costs (2026) - Ginger Labs Semantic routing uses embeddings to infer the meaning of a query ... Unify gives you ... By sending simple queries (FAQs, classification, extraction) to cheap ... NeuralTrust·https://neuraltrust.ai LLM Model Routing: Route Queries to the Right Model Automatically Route simple queries to cheap models and complex ones to capable models -- automatically. This guide covers classifier-based, cascade, and semantic routing strategies with real benchmark cost savings. NexusOne·https://www.nx1.io AI Query Routing: Top Token Optimization Tools 2026 - My Framer ...Semantic routing handles fuzzy natural language ... model will save little, because there is nothing cheaper ... carries the operational burden and how much ... Medium·https://medium.com Using Semantic Caching to Keep LLM Routing Costs Under Control ... tasks that did not need the expensive model at all. The real problem was routing. We were scaling compute before we understood the workload. Semantic routing plus semantic caching is the pattern I Reddit·https://www.reddit.com LLM costs are killing my side project - how are you handling this?Who uses gpt 4 now, even open source are beating gpt 5 models. why are you stuck with gpt 4 models and complaining, there are so many advance model and cheaper too. If you are stuck in big org where t Aiven·https://aiven.io Right Size Your Model Usage with Valkey and Semantic Routing Semantic routing: send the easy prompts to the cheap model. Benchmarks keep showing that picking the right LLM is hard. The easy answer is "just use the most powerful one." That works, but it is price LinkedIn·https://www.linkedin.com Optimizing Task Completion Cost Over Token Price | Vamshi Ambati ...Which is especially terrifying if you're a CFO that's only seen a 1:1 relationship between token usage and costs. It doesn't have to be like that. You can actually use more tokens and spend LESS if yo Compresr·https://compresr.ai**Cheaper** LLM Models vs Cost Optimization in 2026: 5 Keys | Compresr ... semantic routing, and request batching—to reduce token volume and maximize efficiency. ... queries are simple enough for a small model to handle competently. ai-tldr.dev·https://ai-tldr.dev/learn/production-llmops/llmops-fundamentals/llm-model-routing/**Model** Routing: Send Easy Queries to Cheap Models | AI/TLDR This is semantic routing: the router matches the query ... Reasoning models (those that spend tokens ... But if the cheap model has a smaller context window than ...

Yes. What you’re describing is generally called LLM/model routing, semantic routing, or mixture-of-models (MoM): a lightweight router decides whether a request needs a frontier model or can be handled by a cheaper one.

Here are the options I’d look at:

The one I'd investigate first

vLLM Semantic Router is particularly close to your description. It explicitly treats the problem as:

user query → determine complexity/intent → choose model → execute

and supports multiple routing signals and model-selection algorithms.

There is also Aurelio's Semantic Router, which is interesting if your main goal is extremely cheap/low-latency routing. It uses semantic representations rather than burning another expensive LLM call to decide which LLM should answer.

One important economic catch

Don't automatically use an LLM as the router.

If your architecture is:

GPT-5.5 → router LLM → GPT-5.5 or cheap model

you've added another inference call, potentially eating a substantial chunk of the savings. Current routing implementations therefore increasingly use embeddings, classifiers, heuristics, or small local models for the routing decision. vLLM, for example, documents a 3B–7B router as sufficient for semantic routing.

A particularly sensible architecture is:

Request → cheap classifier/semantic router →

• simple: nano/mini/Flash-Lite/etc.
• medium: mid-tier model
• hard: frontier model

with an escalation path if the cheap model is uncertain.

For your specific goal—"we're spending too much on tokens for simple queries"—I'd shortlist vLLM Semantic Router, NVIDIA NeMo Switchyard, and Aurelio Semantic Router, then compare them against simply implementing a tiny classifier yourself. The latter can actually be surprisingly effective and avoids paying for a router on every request.

If you tell me which models/API you're currently using (OpenAI, Anthropic, Gemini, OpenRouter, etc.), I can narrow this down to the 3 cheapest/easiest routing solutions and show the architecture + expected savings.

To cut token costs by sending simple queries to smaller, cheaper models, you can use specialized AI gateways and open-source routing frameworks that feature semantic or complexity-based routing.

Options offering semantic and intelligent model routing span open-source tools, managed gateways, and enterprise platforms:

1. • What it does: An open-source, high-performance routing layer designed for the vLLM ecosystem. It uses a lightweight classifier (such as ModernBERT) to evaluate incoming intent, complexity, and safety signals, sending simple or non-reasoning queries to smaller models while escalating complex tasks to heavyweights.
   • Best for: Self-hosted infrastructure looking for low-latency, Rust-powered routing natively integrated with model serving engines.
2. RouteLLM
   • What it does: An open-source framework specifically built for serving and evaluating LLM routers. It uses trained lightweight classifiers to map user prompts dynamically between a strong (expensive) model and a weak (cheap) model based on cost-versus-performance trade-offs.
   • Best for: Developers who want a straightforward, research-backed classification approach to split traffic between frontier and budget APIs.
3. Bifrost (by Maxim AI)
   • What it does: An open-source AI gateway that unifies semantic routing, intent classification, and semantic caching under a single OpenAI-compatible API. It handles request triage alongside enterprise governance.
   • Best for: Production teams wanting an all-in-one proxy that short-circuits repeated or trivial queries with semantic caching before hitting any LLM.
4. LiteLLM
   • What it does: A widely adopted, production-tested open-source proxy and gateway. While it relies heavily on robust fallback, load-balancing, and cost-tracking mechanics, it includes built-in router classes that can be configured for multi-model priority rules and cost-based dispatch.
   • Best for: Engineering teams comfortable managing a central proxy across 100+ LLM providers.
5. Commercial Managed Routers (Not Diamond, Martian, Neutrino AI)
   • What they do: Specialized hosted APIs that act as an intelligent proxy layer. Services like Not Diamond automatically test or evaluate query structure in real-time to route between cheaper models (like GPT-4o-mini or Llama budget variants) and top-tier frontier models.
   • Best for: Teams that prefer a managed SaaS routing layer rather than running custom classifier infrastructure.

If you tell me whether you prefer a self-hosted open-source proxy (like LiteLLM or vLLM Semantic Router) or a managed cloud API (like Not Diamond), I can share a sample configuration or implementation approach for your stack.

Yes. What you’re describing is generally called LLM/model routing, with semantic routing being one implementation: a router looks at the query’s complexity/intent and sends easy requests to a cheaper model while escalating difficult ones.

Here are the strongest options I’d look at:

My shortlist

1. RouteLLM — probably closest to what you mean.

RouteLLM was specifically designed around the idea of “don't use the expensive model unless the query needs it.” It provides an OpenAI-compatible interface and learned routers that decide whether a request should go to the strong or weak model. Its documentation also demonstrates routing GPT-4 requests to a local Llama 3 model.

2. vLLM Semantic Router — more sophisticated semantic routing.

This is particularly interesting if you're operating your own inference infrastructure. It classifies requests semantically and can route according to cost, latency, privacy, safety, or capability. A recent 2026 example combining it with agentgateway reported roughly 35–40% cost reduction on a representative workload.

3. Unify — managed alternative.

Unify operates as a commercial router, using benchmarking/runtime information to select among model endpoints. This is attractive if you'd rather not operate the routing layer yourself.

4. Ramp Router — turnkey cost optimization.

Ramp's router explicitly evaluates models and sends a request to the lowest-cost model that clears its quality threshold. It reports roughly 30% cost reduction in its current production figures.

One important distinction

There are actually three different approaches hiding under “semantic routing”:

1. Complexity routing:
   "What's 2+2?" → cheap model
"Debug this distributed systems failure" → frontier model

2. Capability routing:
   "Translate this" → translation-optimized model
"Write Python" → coding model
"Analyze this contract" → reasoning model

3. Cascade routing:
   Start with the cheap model; if its confidence/quality signal isn't sufficient, escalate to the expensive model.

For your stated goal—“we're spending too much on tokens for simple queries”—I'd start with #1, complexity routing. RouteLLM is probably the simplest proof of concept, while vLLM Semantic Router is worth evaluating if you're building a serious production/self-hosted architecture.

There's also an emerging fourth category worth knowing about: Mixture-of-Models routers such as Brick, which classify a query across capabilities and complexity and then select the cheapest model capable of handling it.

If you tell me which models you're currently using (e.g. GPT-5.4/5.5, Claude, Gemini) and roughly how many requests/tokens per month, I can narrow this down to the 2–3 options most likely to save you money and estimate the savings.

Implementing semantic routing (or cost-versus-complexity routing) is one of the fastest ways to slash token spend without rewriting your core app logic. Instead of hitting a heavy frontier model with every prompt, a lightweight classifier or embedding space inspects the query's intent and delegates simple requests to cheap or local models.

The leading open-source frameworks, specialized libraries, and production AI gateways offering semantic or intelligent model routing include:

• vLLM Semantic Router : An open-source, high-performance request routing layer (built in Rust/Candle) designed to sit in front of the vLLM inference stack. It uses a lightweight ModernBERT -based classifier to measure query complexity and intent, dynamically routing easy prompts to smaller models and complex reasoning tasks to larger ones.
• RouteLLM : An open-source framework developed specifically for serving and evaluating LLM routers. It provides trained routers designed to cleanly split traffic, sending straightforward, low-complexity queries to cheaper models and harder ones to high-end frontier models while preserving output quality.
• LiteLLM : A widely-used, production-tested open-source proxy and gateway that supports auto-routing, fallback chains, and semantic caching (via Redis or Qdrant). LiteLLM allows you to map out custom heuristic or classifier rules to balance cost-per-quality across 100+ model providers.
• Bifrost by Maxim AI : A high-performance, open-source Go-based AI gateway built for production-scale routing. It unifies semantic caching (short-circuiting redundant prompts via vector similarity) with multi-provider intelligent request routing across OpenAI, Anthropic, Bedrock, and local models.
• Kong AI Gateway : An infrastructure-level enterprise API gateway featuring advanced semantic proxy plugins. It utilizes vector databases (like Redis) to score prompt-to-model semantic similarity and route traffic natively across diverse upstream models.

If you can share whether you prefer a self-hosted open-source proxy (like LiteLLM or vLLM) or a managed cloud gateway , I can recommend the easiest deployment path for your current tech stack.

Yes. The category you’re describing is usually called LLM/model routing, semantic routing, or intelligent model routing: a lightweight classifier/router examines the request and sends easy queries to a cheaper model while reserving expensive frontier models for difficult ones.

A few notable options:

The important distinction

There are actually two different kinds of routing that often get conflated:

1. Provider routing:
   “I want Claude Sonnet; find me the cheapest/reliable endpoint.”
   OpenRouter is particularly good at this. Its default routing already favors cheaper reliable providers.

2. Model/semantic routing:
   “This request is trivial → use a $0.10/M model. This one is complicated → use a $5/M frontier model.”
   Not Diamond and Martian are much closer to this.

For your stated problem—“we're spending too much on tokens for simple queries”—I'd investigate Not Diamond first. Their stated purpose is almost exactly this: analyze each input and select the model that provides the appropriate quality at the lowest cost.

There is also an increasingly interesting self-hosted/open-source approach. For example, research on semantic routers has demonstrated routing based specifically on whether a query needs reasoning, with reported reductions in token consumption and latency.

A practical architecture

You could have:

User → cheap router/classifier →

• trivial → small/cheap model
• normal → mid-tier model
• complex/reasoning → frontier model

The router itself doesn't need to be an expensive LLM. A small classifier can score things like complexity, required reasoning, coding difficulty, context length, and expected output length, then make the model choice.

If you tell me roughly which models you're currently using (e.g. GPT-5/Claude/Gemini) and your monthly token volume, I can compare Not Diamond vs OpenRouter vs Martian vs a self-hosted router specifically for expected cost savings and architecture.

To cut down on token waste, several open-source frameworks, enterprise gateways, and specialized libraries offer semantic or complexity-based routing. Instead of hitting a massive frontier model with every request, these tools analyze the prompt's intent or complexity upfront and shunt simple queries to smaller, faster, or locally hosted models.

The primary options available for implementing semantic and cost-aware routing include:

1. • What it is: A joint open-source infrastructure effort (backed by Red Hat, IBM Research, AMD, and Hugging Face) designed to sit in front of the vLLM inference engine.
   • How it works: It uses lightweight classifiers (such as fine-tuned ModernBERT models) or embedding spaces to evaluate intent, safety, and complexity, dynamically sending basic queries to smaller models and complex reasoning queries upstream. It also natively supports semantic caching.
2. RouteLLM
   • What it is: An open-source framework specifically built for serving and evaluating cost-budgeted "mixture-of-models" setups.
   • How it works: It provides trained router controllers explicitly optimized to serve as a gatekeeper, judging whether a prompt can be safely handled by a cheap model or if it requires a heavy-duty frontier model.
3. Semantic Router (by Aurelio Labs)
   • What it is: A blazing-fast, lightweight Python/Rust-based decision-making library.
   • How it works: It maps out utterances and intent in a semantic vector space using encoder models (like Cohere or OpenAI embeddings). Rather than making an extra LLM call to figure out where a query should go, it matches the semantic meaning instantly via vector math to route or gate tool usage.
4. Bifrost (by Maxim AI) & Portkey Gateway
   • What it is: Production-grade AI API gateways.
   • How it works: They provide drop-in, OpenAI-compatible proxy endpoints that feature built-in semantic routing, semantic caching (to instantly return answers to re-phrased repeat queries), automatic fallback, and per-request cost attribution across multiple providers.
5. LiteLLM
   • What it is: A widely-adopted proxy gateway and API wrapper.
   • How it works: While more commonly used for broad provider compatibility and fallback rules, its proxy server supports custom logic and team-scoped routing rules to separate traffic across model tiers.

If you share whether you prefer a hosted API gateway (like Portkey or Bifrost) or a self-hosted open-source stack (like vLLM Semantic Router or RouteLLM), I can help you outline an implementation strategy.

Yes. What you’re describing is generally called LLM/model routing, semantic routing, or a model cascade: a lightweight classifier/router looks at the request and sends easy queries to a cheaper model, while escalating difficult ones to a stronger model.

The main options I’d look at are:

The one I'd investigate first

vLLM Semantic Router is probably closest to your exact description. It explicitly uses semantic signals to decide which model should receive a request, including routing simple requests to cheaper models. It supports multiple providers/backends rather than tying you to one model family.

RouteLLM is another particularly interesting option if your primary question is "Can we reliably send 70–80% of traffic to a cheap model without noticeably hurting quality?" It was specifically designed to learn when a weaker model is sufficient.

One important distinction

There are actually two architectures:

1. Pre-generation semantic routing

User query
    ↓
Cheap router/classifier
    ↓
 ┌──────────────┐
 │ Easy?        │──→ Small/cheap model
 │ Hard?        │──→ Frontier model
 └──────────────┘

This is what you're asking for. The router can be a tiny embedding/classification model, so you're not burning another expensive LLM call merely to decide where to send the request.

2. Cascading

User query
    ↓
Cheap model
    ↓
Is answer confident/good enough?
    ├── Yes → return
    └── No  → frontier model

This can be more accurate because you're evaluating an actual attempted answer, but it costs more latency and requires a good confidence/quality gate.

If your goal is purely reducing your bill

I'd benchmark vLLM Semantic Router + RouteLLM + a simple rules/embedding router against your actual traffic rather than assuming semantic routing will save money. There is emerging evidence that routing can introduce consistency/cache issues and that the economics depend heavily on the workload.

If you tell me which models/providers you're currently using (e.g. GPT-5, Claude, Gemini, etc.) and roughly how many requests/tokens you process, I can narrow this to the 2–3 solutions that would actually make sense for your stack.

Yes. This has become a fairly active category, although vendors use different names: semantic routing, LLM routing, intelligent prompt routing, cost-aware routing, or model cascading.

Here's how the landscape breaks down.

These are the ones I'd look at first:

LiteLLM

Probably the most widely adopted open-source gateway.

It lets you define policies such as:

• FAQ → GPT-4.1 Mini
• SQL generation → Claude Sonnet
• Long reasoning → GPT-5
• Retry on another provider if one fails

It also provides:

• unified API
• spend tracking
• caching
• rate limiting
• fallbacks

This is often the easiest drop-in if you already call multiple model providers.

---

RouteLLM

RouteLLM is specifically designed to answer:

"Can this request be answered by a cheap model?"

It uses a learned router rather than hand-written rules.

In published benchmarks, the authors report routing many requests to smaller models while preserving most of the quality of always using a frontier model, with reported cost reductions ranging roughly from 35–85%, depending on the workload and model pair. Actual savings depend heavily on your prompts and quality requirements.

---

Amazon Bedrock Intelligent Prompt Routing

If you're already on AWS, this is particularly attractive.

Instead of always calling the expensive Claude tier, it predicts prompt complexity and automatically chooses between (for example):

• Claude Haiku
• Claude Sonnet

No routing logic to maintain yourself.

---

Portkey

Portkey positions itself more as an AI gateway than just a router.

Besides routing it includes:

• observability
• logging
• prompt versioning
• caching
• guardrails
• cost optimization

Good choice if you want an enterprise control plane rather than only routing.

---

OpenRouter

OpenRouter's auto-routing can select among hundreds of models from dozens of providers using a single API. It's convenient if you don't want to manage provider integrations yourself, though it's more focused on provider/model selection than deep semantic classification.

---

Semantic Router (open source)

Instead of asking an LLM which model to use, it embeds the incoming prompt and compares it to known intents.

Example:

"Reset my password"
        ↓
Customer Support
        ↓
Llama 3 8B

"Explain quantum computing"
        ↓
Complex reasoning
        ↓
GPT-5

Because routing is embedding-based, decisions are typically much faster and cheaper than using another LLM to classify the request.

What most production teams actually do

The highest ROI usually comes from combining several techniques:

1. Semantic routing to decide whether the request is simple or complex.
2. Semantic cache to avoid recomputing similar prompts.
3. Small-model-first cascade, escalating only when needed.
4. Provider failover for reliability.
5. Cost and latency monitoring to refine routing policies over time.

For many SaaS workloads, this pattern can substantially reduce token spend because a large fraction of traffic consists of FAQs, summarization, extraction, or classification tasks that don't require a frontier model.

If you're building a new system today, a practical stack would be:

• Open source: LiteLLM + RouteLLM (or Semantic Router) + your preferred model providers.
• Managed: Portkey if you want governance and observability, or Amazon Bedrock Intelligent Prompt Routing if you're already committed to AWS.

To stop overpaying for simple queries, you can implement semantic routing—either via managed AI gateways or open-source infrastructure—to automatically dispatch basic tasks (like chitchat, simple classification, or short extractions) to smaller, cheaper models, while escalating complex reasoning or coding tasks to frontier models.

The top options offering semantic and cost-aware routing include:

• vLLM Semantic Router : An open-source, system-level intelligence and routing layer built by Red Hat, IBM Research, AMD, Hugging Face, and others. It utilizes lightweight classifiers (like ModernBERT) to analyze incoming prompt intent and complexity, routing simple requests to smaller local or cheaper models and complex ones to reasoning models.
• Bifrost (by Maxim AI) : An open-source high-performance enterprise AI gateway that natively unifies semantic routing, dual-layer semantic caching, and governance under a single OpenAI-compatible API.
• Requesty : A managed, high-performance AI gateway built in Rust that handles smart classification-based request routing (dispatching to the ideal model by task type) alongside semantic caching.
• Portkey : A production-ready AI gateway featuring comprehensive model catalog support across 1,600+ models. It supports smart routing, fallbacks, and extensive guardrails to manage multi-model cost structures.
• LiteLLM : A mature, widely adopted proxy that provides built-in router and fallback capabilities. While it acts as the master translation layer across 100+ providers, it can be paired with classifier logic or semantic caches to achieve cost-based routing.
• Semantic Router (by Aurelio) : A lightweight, open-source Python library designed for super-fast, embedding-based decision making and intent classification without needing an extra LLM call just to figure out where to route.

If you can share whether you prefer a managed cloud gateway or a self-hosted open-source stack (and what models you currently use), I can help you choose the best integration path.