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TokenRoute

FreemiumAPI

Hardcoding a model into your workflow is fine until the model drifts, a cheaper alternative ships, or a client asks why you chose it — and you have nothing but a demo to point to. TokenRoute exists to replace that guess with repeatable benchmark evidence tied to your actual prompts.

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$7.50/mo · Free tier: 10 benchmark packs / month, 1,000 route API calls / month, 1 project and 1 user

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Summary

The core loop is: feed your real prompt contracts into benchmark packs, run them against candidate models (local Ollama, private endpoints, OpenRouter, OpenAI, Anthropic), score the results on quality, latency, cost, and failure rate, then export a route-decision policy your system can call at runtime. Every recommendation traces back to stored judge provenance, so you can defend the choice when someone asks. The route-decision API means agents stop hardcoding a model name and start querying what the evidence says. The ceiling appears when your routing logic needs to adapt mid-conversation or chain decisions across multiple steps — TokenRoute does benchmarking and policy export, not autonomous task execution. Teams that need live, self-correcting routing use this as the evidence layer feeding a separate gateway.

Bottom line: Pick TokenRoute when you need to justify a model swap with data your team can audit; plan a separate runtime layer when you need routing decisions that react to what happened three steps ago in the same session.

Pricing Plans

Subscription
Free Tier
10 benchmark packs / month, 1,000 route API calls / month, 1 project and 1 user

Free

Free

Useful trial for validating a real workload

  • 10 benchmark packs / month
  • 1,000 route API calls / month
  • 1 project and 1 user
  • BYOK and local/private models

Builder

$7.50per month
$90/yr

Low-friction plan for builders, small apps, and client experiments

  • 75 benchmark packs / month
  • 10,000 route API calls / month
  • 3 projects and 2 users
  • Exports, cost caps, and provider health

Team

$24.17per month
$290/yr

Shared benchmark evidence for early AI product and platform teams

  • 300 benchmark packs / month
  • 50,000 route API calls / month
  • 10 projects and 5 users
  • Route-decision API and priority support

View full pricing on tokenroute.ai →

Pricing may have changed since last verified. Check the official site for current plans.

Find your fit

Before you choose TokenRoute

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Pros

  • Provider-agnostic BYOK design supports local Ollama, private endpoints, OpenRouter, OpenAI, and Anthropic from the same benchmark run, so swapping providers when costs spike doesn't require re-architecting the evaluation setup.
  • Stored judge provenance ties every quality score to the specific method and benchmark pack that generated it, which means when a model's score drops you can tell whether the provider changed behavior or your prompt contract drifted — without that, you're re-running tests from scratch to find the cause.
  • Route-decision API lets agent workflows query the current evidence-backed model recommendation at runtime instead of hardcoding a model name, so a model deprecation or a cost-driven swap doesn't require touching every workflow that calls a model.
  • Aggregate model trends persist without exposing raw prompts across tenants, so platform teams serving multiple clients can build a shared benchmark library without commingling client data.
  • Deterministic output contract checks run alongside optional LLM judges, which means extraction accuracy and format compliance failures surface even when an LLM judge would score the response as acceptable prose.

Cons

  • TokenRoute benchmarks against historical prompt packs and exports static policies — it does not observe live session context. Routing decisions that need to branch based on what the previous agent step returned require a separate runtime layer; teams that conflate benchmark-time policy with inference-time routing discover this gap when their first multi-step agent ignores the policy because the policy has no way to see mid-session state.
  • The benchmark pack and route API call limits on the free tier are fixed at volumes the vendor positions as a validation trial, not ongoing production use. Teams running continuous benchmark sweeps across a growing model library hit those limits and move to paid tiers; teams whose benchmark volume scales with client count find the per-project and per-user caps on lower paid tiers constraining before the API call cap does.
  • There is no self-hosted deployment option. Teams operating under data residency requirements or security policies that prohibit sending prompt contracts to a third-party SaaS — even with BYOK keys — cannot use TokenRoute and switch to building internal evaluation harnesses instead, typically on open-source eval frameworks where provenance tracking and route-policy export require custom engineering work.

About

Platforms
Web SaaS
API Available
Yes
Self-Hosted
No
Last Updated
September 27, 2026

Best For

Who it's for

  • App teams validating model changes
  • Agent workflows needing dynamic routing
  • Platform teams maintaining benchmark libraries
  • Cost control on model selection

What it does well

  • Select model for real workload using quality, latency, and cost evidence
  • Call route-decision API instead of hardcoding models in workflows
  • Track provider drift and failure rates over time
  • Compare cheaper candidates against quality gates before routing to expensive models

Integrations

Ollamaprivate endpointsOpenRouterOpenAIAnthropicLiteLLM

Frequently Asked Questions

Is TokenRoute free?
TokenRoute offers a free option alongside paid upgrades. Check the current allowances and terms before choosing a plan.
Does TokenRoute have an API?
Yes. TokenRoute exposes a developer API. See the official documentation at https://tokenroute.ai for details.
What platforms does TokenRoute support?
TokenRoute is available on: Web SaaS.

Curated lists that include this category

Hardcoding a model breaks when things change

Hardcoding a model into your workflow is fine until the model drifts, a cheaper alternative ships, or a client asks why you chose it — and you have nothing but a demo to point to. TokenRoute replaces that guess with repeatable benchmark evidence tied to your actual prompts.

How it works

The core loop is: feed your real prompt contracts into benchmark packs, run them against candidate models (local Ollama, private endpoints, OpenRouter, OpenAI, Anthropic), score the results on quality, latency, cost, and failure rate, then export a route-decision policy your system can call at runtime. Every recommendation traces back to stored judge provenance, so you can defend the choice when someone asks. The route-decision API means agents stop hardcoding a model name and start querying what the evidence says.

Key limits

TokenRoute benchmarks against historical prompt packs and exports static policies — it does not observe live session context. Routing decisions that need to branch based on what the previous agent step returned require a separate runtime layer.

Who it is for / who should skip it

Best for app teams validating model changes, agent workflows needing dynamic routing, platform teams maintaining benchmark libraries, and cost control on model selection. It offers provider-agnostic BYOK design and stored judge provenance, but teams needing mid-conversation adaptation or high-volume production use on the free tier should plan for paid limits or additional layers.

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