Best LLM inference router

Combined ranking

1. 1
   OpenRouter—12 pts

   GPT #1Claude #1Gemini #4

   Best overall for most practitioners: one OpenAI-compatible API, broad model/provider coverage, automatic provider failover, configurable privacy and price controls, session stickiness, and a no-surcharge Not Diamond-powered Auto Router with an adjustable cost-quality trade-off.

   Claude One API key to 400+ models across dozens of providers with automatic provider fallbacks, price/latency-based routing (:nitro/:floor) and an Auto Router for per-request model choice; pay-as-you-go, OpenAI-compatible, and battle-tested at massive volume, making it the default gateway for indie devs and startups (assumption: typical practitioner = an app developer who wants breadth and uptime without running infrastructure).

   Gemini The ultimate managed aggregator providing access to over 300+ models via a single API with a native openrouter/auto endpoint that dynamically routes to the cheapest and fastest equivalent models.

   Where it falls short

   per GPT The managed, general-purpose router is comparatively opaque and cannot be trained deeply around your application, so it is not ideal when strict infrastructure control or domain-specific routing accuracy is essential.

   per Claude ~5% credit fee plus an extra network hop, and your prompts and billing flow through a middleman — teams with strict data-residency, enterprise procurement, or very high volume eventually go direct or self-host.

   per Gemini It is a purely third-party hosted service with no self-hosted or private VPC deployment options, making it a non-starter for enterprises requiring strict data sovereignty.

2. 2
   Not Diamond—10 pts

   GPT #2Claude #5Gemini #1

   Leading dynamic meta-router utilizing ML classifiers to evaluate prompts in real-time, directing requests to the optimal model based on cost, quality, and latency constraints while supporting custom evaluation datasets.

   GPT Strongest dedicated routing-intelligence layer: pre-trained chat and coding routers, custom routers learned from your evaluation data, and per-request quality, cost, or latency optimization; it is the better choice than OpenRouter when routing accuracy should adapt to a specific workload.

   Claude The strongest true per-prompt router — trains custom routers on your own evals to send each request to the best model while jointly optimizing quality, cost, and latency, delivering real savings versus always calling a frontier model; ranked here on capability in the literal "best model per request" sense rather than adoption.

   Where it falls short

   per GPT It selects the model but is not a complete one-key inference gateway, so you must operate or integrate the execution, credentials, billing, and observability layer yourself.

   per Claude Niche traction and a black-box router you must trust with eval data; it is not a full gateway (no key management, budgets, or observability), so most teams pair it with one of the options above.

   per Gemini It introduces latency overhead from classifier runs and requires sending prompt data to a third-party hosted service, raising compliance concerns for sensitive data.

3. 3
   LiteLLM—8 pts

   GPT #3Claude #2Gemini #5

   The de facto open-source standard: an OpenAI-compatible self-hosted proxy across 100+ providers with load balancing, fallbacks, budgets, virtual keys, and cost tracking; no per-token tax and traffic stays in your VPC, which is why it's the gateway most companies actually run internally.

   GPT Best open-source value and near-tied with Portkey for production teams: self-hosted or embedded operation, very broad provider support, virtual keys, budgets, fallbacks, load balancing, semantic intent routing, and a sub-millisecond complexity router, with no gateway markup when using your own provider accounts.

   Gemini The most popular developer-first open-source proxy gateway supporting 100+ model APIs, giving teams total control over load balancing, fallbacks, retries, and manual routing rules.

   Where it falls short

   per GPT Its flexibility transfers routing calibration and operational reliability to you, so it is not for teams wanting a trustworthy zero-configuration “best model” decision.

   per Claude You operate it yourself — the Python proxy adds ops burden and measurable overhead at high RPS (the gap that Rust/Go gateways like Bifrost and Helicone's target), config sprawls as teams grow, and SSO/enterprise features sit behind a paid tier.

   per Gemini It lacks out-of-the-box ML-based dynamic semantic classification to assess prompt complexity, requiring developers to write custom routing heuristics manually.

4. 4
   Portkey—4 pts

   GPT #4Claude #4Gemini —

   Near-tied with LiteLLM and stronger for governed enterprise deployments: polished observability, guardrails, caching, budgets, conditional routing, nested load balancing and fallbacks, circuit breakers, canaries, and managed or self-hosted options across a large model catalog.

   Claude The most complete production gateway around routing: config-as-code fallbacks, retries, load balancing and canary splits, plus semantic caching, guardrails, and deep observability in one product, with an open-source gateway core — suited to teams that need governance and reliability, not just model access.

   Where it falls short

   per GPT Its core routing is principally policy- and metadata-driven rather than a learned predictor of which model will answer each prompt best.

   per Claude Per-request pricing gets expensive at scale, and its routing is rule-driven — it won't decide which model is best for a given prompt on its own.

5. 5
   RRouteLLM—4 pts

   GPT —Claude —Gemini #2

   The standard open-source framework for training and serving local routing classifiers using human preference data (like Chatbot Arena), avoiding commercial vendor lock-in and keeping prompt data fully within local infrastructure.

   Where it falls short

   per Gemini It demands significant engineering overhead to host, manage, and continuously retrain the routing models as newer LLMs are released.

6. 6
   MMartian—3 pts

   GPT —Claude —Gemini #3

   Highly effective commercial router using proprietary model mapping technology to forecast target model performance on-the-fly, enabling zero-ops dynamic optimization.

   Where it falls short

   per Gemini It operates as a proprietary, closed-source black box with markup fees, preventing developers from auditing why specific routing decisions were made.

7. 7
   Vercel AI Gatewayincumbent—3 pts

   GPT —Claude #3Gemini —

   0% markup on tokens with bring-your-own-key, a unified API with automatic failover across providers, spend monitoring, and tight AI SDK integration — the best price-to-simplicity ratio for teams already in the Vercel/Next.js orbit; near-tie with LiteLLM, the split being hosted convenience versus self-hosted control.

   Where it falls short

   per Claude Smaller catalog and fewer routing knobs than OpenRouter, routing is failover-grade rather than learned per-prompt selection, and it deepens dependence on the Vercel ecosystem.

8. 8
   RRequesty—1 pts

   GPT #5Claude —Gemini —

   A practical hosted alternative with one OpenAI-compatible endpoint, hundreds of models, automatic failover, geographic and data-residency routing, organizational controls, caching, and usable smart-routing defaults without infrastructure work.

   Where it falls short

   per GPT Its proprietary quality-selection logic has less independent validation and transparency than the leaders, making it a weaker choice for high-stakes routing decisions.

Just missed the top 5

By model

Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled continuously