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There is no independently verified 2026 benchmark establishing one LLM router as the fastest or cheapest for every workload. A practical shortlist is LiteLLM, Portkey (now branded PRISMA AIRS AI Gateway), OpenRouter, Requesty, Kong AI Gateway, Cloudflare AI Gateway, and Helicone. Treat these as candidates, not a ranked list: compare them with your own models, providers, regions, traffic, and routing policies.

What an LLM router does—and what to compare

“LLM routing” describes two related but distinct jobs. A gateway may steer a request among deployments or providers of the same model to balance load, cost, latency, or availability. A model router may instead select a different model for a request—for example, sending simpler work to a less expensive model and reserving a stronger model for harder tasks.

Before choosing a tool, decide which job you need it to perform. Then compare candidates on the policy they can apply, their effect on end-to-end performance, and the operational controls your team requires.

  • Routing objective: deployment balancing, latency- or cost-aware selection, provider fallback, or quality-based model choice.
  • Latency: separate time added by the gateway from provider and model response time. Compare p50 and tail latency, such as p95 or p99, under the same workload.
  • Total cost: include model input and output charges, gateway or service fees, retries, caching, and any markup—not just a router’s advertised rate.
  • Control and deployment: check hosted versus self-hosted options, key and data custody, regions, network path, and how much routing policy you can tune.
  • Reliability: examine fallback and retry behavior, health checks, cooldowns, and failure isolation.
  • Operations: assess logging, spend attribution, governance, access control, and the effort needed to deploy and maintain the service.
  • Quality: measure task success or preference against a fixed-model baseline. A lower bill is not a win if routed answers are materially worse.

Seven LLM routing tools to evaluate

The candidates below cover different needs and are not interchangeable. The available evidence supports a shortlist, not a comparative performance ranking.

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Tool What the available product information establishes Potential reason to evaluate it
LiteLLM Its official router documentation describes weighted selection, latency-based and cost-based routing, routing groups, session affinity, and fallbacks. Its pricing page lists open-source self-hosting at $0 and an Enterprise offer priced annually according to capacity, deployment architecture, and support needs. Teams seeking a self-hosted gateway and configurable provider or deployment routing.
Portkey / PRISMA AIRS AI Gateway Portkey’s official site currently says “Portkey is now PRISMA AIRS AI Gateway” and presents gateway, observability, guardrails, governance, and prompt-management capabilities. Current commercial pricing and partner terms are not established here. Teams assessing a broader gateway and governance platform, not only request routing.
OpenRouter Its official provider-routing documentation describes provider-selection controls. Those controls and latency characteristics have not been established as equivalent to self-hosted gateways. Teams considering a managed provider-routing option.
Requesty Its official site positions it as an AI gateway and LLM router. Requesty also published the June 23, 2026 comparison that names this shortlist and reports latency estimates. Teams evaluating a gateway and router, while treating its comparative figures as vendor-reported claims.
Kong AI Gateway Kong’s official product page establishes an AI gateway offering. The available information does not establish directly comparable latency results or detailed current routing behavior. Teams already assessing Kong as an API-platform candidate; verify the routing features relevant to your use case.
Cloudflare AI Gateway Cloudflare publishes official AI Gateway documentation. The available information does not establish a general lowest-latency result. Teams considering Cloudflare’s edge platform as part of their gateway evaluation.
Helicone Its official site describes an AI gateway and LLM observability offering. The available information does not establish it as a like-for-like dynamic model router. Teams that want to assess gateway and monitoring capabilities together.

How to interpret latency and savings claims

Gateway overhead is not the same as total request latency. The gateway is one part of a request path that also includes network conditions, the selected provider, model response time, and any retries. A router that adds little overhead could still produce slower overall responses if its policy selects a slower route; a policy that improves latency for one traffic mix may not do so for another.

Requesty’s June 23, 2026 comparison includes platform-specific overhead estimates, including p50 figures. Because Requesty published the comparison and is one of the vendors named, treat those numbers as vendor-reported, not as independent measurements. No equivalent independent cross-vendor 2026 latency benchmark is established here, so the figures do not support a universal fastest-to-slowest ranking.

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A separate result often relevant to model selection comes from RouteLLM, a 2024 research paper by Isaac Ong and coauthors. The paper reports over 2x cost savings in certain evaluated benchmark cases using preference-data-trained routers that choose between stronger and weaker models. That is a bounded result from the paper’s evaluated settings, not a promised saving for a production workload or a measurement of the seven gateways above.

How to benchmark candidates on your workload

Use a replay or controlled canary built from representative requests. Hold the workload and measurement method steady, and record enough information to distinguish gateway overhead from provider behavior.

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Rank #3
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  1. Define the question. For gateway overhead, compare equal model and provider choices. For complete policy outcomes, compare each tool’s routing policy, including the selected model or provider.
  2. Use production-like traffic. Include your real prompt mix, model mix, regions, concurrency, logging, cache behavior, and retry settings. Note any differences from production.
  3. Capture each request’s path. Record model and provider, region, prompt and completion token counts, routing decision, gateway timing, provider timing, retries, and cache hits.
  4. Measure outcomes together. Compare p50 and tail latency, total request cost, and task-quality results. Include service fees and retry costs where applicable.
  5. Repeat and document the conditions. Keep the same workload and configuration across candidates, and record the test date because product features and model prices change.

This approach helps answer two different questions without conflating them: how much latency the gateway adds, and whether the complete routing policy improves cost, speed, reliability, or quality for your requests.

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Which tool should you evaluate first?

Start with the capability that matters most rather than a generic “best” label. LiteLLM is a clear candidate to examine if self-hosting and configurable routing are priorities. Consider Portkey / PRISMA AIRS if you are assessing a broader gateway, governance, and observability platform. OpenRouter and Requesty belong on a managed provider-routing shortlist; Kong and Cloudflare merit evaluation in the context of their respective API-platform and edge ecosystems. Include Helicone when observability is a major part of the decision, and verify whether its routing behavior matches your needs.

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These are evaluation starting points, not performance conclusions. Before committing, confirm current product behavior, deployment options, pricing, and policy controls directly with each provider, then compare candidates under the same workload.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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