The real pain: three providers, three bills, and a markup you keep paying forever

If you're running GPT-6, Claude, and Gemini in production, the messy part isn't "how do I call three APIs." You can wrap that in an afternoon.

The pain is operational and financial:

• Three separate API accounts.
• Three billing relationships you have to reconcile when the bill spikes.
• Three sets of rate limits and weird edge cases.
• And if you don't centralize telemetry yourself, three observability blind spots where spend creeps in and nobody notices until month-end.

Most teams throw a router/gateway in front of it. One endpoint. One auth story. One place for retries and logging.

Then they miss the part that matters: many multi-provider routers charge a percentage markup on tokens. That means you're not just paying OpenAI/Anthropic/Google. You're paying a permanent extra tax layered on top — on every request, forever.

The cheapest way to access all three providers through one API isn't "use a router." It's: use a router that doesn't take a cut of the tokens you already pay the provider for. If you get that wrong, everything else you do to "optimize" is fighting uphill.

Current list pricing (per 1M tokens): the spread is the whole story

Here's the pricing reality you're routing across.

Now interpret the numbers like someone who has to defend a cloud bill.

• GPT-6 Astra is expensive on output. $50.00 per 1M output tokens means verbose generations hurt. This is the model you want when you actually need it — and the model you absolutely don't want as your "default because it worked in staging."
• Claude has clear tiers you can use intentionally. Opus 5 at $25.00 output is not the same decision as Haiku 4.5 at $5.00 output, and Sonnet 5 now sits between them at $10.00 output. If your system doesn't make it easy to pick per task, you'll end up paying Opus/Sonnet rates for Haiku-shaped work.
• Gemini has both cheap throughput models and a context-priced Pro tier. Gemini 3.1 Flash-Lite at $0.25 input / $1.50 output is where a lot of boring production traffic should live if quality holds. Gemini 3.1 Pro Preview has a pricing split at ≤200K context vs >200K context — and that kind of threshold is exactly the sort of detail that quietly changes unit economics if you're not watching it.

And the spread is brutal:

GPT-6 Astra's output rate ($50/1M) is roughly 33x Gemini 3.1 Flash-Lite's ($1.50/1M).

That one line is why "we'll optimize later" fails in production. If you're routing incorrectly, your AI bill scales faster than your product because you've attached a high-cost generator to normal traffic.

Also: this spread is why percentage markups hurt more than teams expect. Markup doesn't care whether you're already paying $1.50/1M output or $50/1M output. It just stacks on top.

The hidden tax: what a markup-charging router costs in real life

A percentage-cut gateway isn't a one-time integration expense. It's not "we paid for the tool, move on."

It's a permanent skim applied on top of every number in the table. On every request. Forever.

A useful way to sanity-check it: a team spending $30,000/month across GPT-6, Claude, and Gemini traffic pays an extra $1,500-$1,650/month at a typical 5-5.5% markup.

That is money you are paying that does not buy you better answers from the models. It doesn't buy you extra context. It doesn't buy you more uptime from the provider. It's purely the cost of having a middle layer take a cut of your token spend.

And it scales perfectly with your success. The better your product does, the more you pay the router. That's the worst kind of cost in an LLM app because token spend already grows with usage; attaching another percentage-based line item just amplifies it.

This matters most on frontier-model output, because output is where costs blow up fastest — long streamed answers, verbose or reasoning-heavy generations, "helpful" extra explanation the user didn't ask for. If output tokens are already your primary cost driver, paying an additional percentage on top of them is self-inflicted damage.

"Cheapest" starts with zero markup, not fancy routing

Routing is useful. Evaluation is useful. Observability is useful.

But if you're paying a percentage markup, you've already lost the cheapest-possible game before any of those features matter.

Why? Because markup applies even if you route perfectly. Even if you never send a single request "wrong." Even if your model selection is flawless.

Zero markup is the structural fix. It stops the bleed immediately.

Only after that does routing become the lever to exploit the pricing spread. If you're serious about not overpaying, the decision order should be:

• Don't pay a percentage tax.
• Then implement model selection so expensive models are reserved for the work that truly needs them.

FastRouter: one API across GPT-6, Claude, and Gemini — with $0 markup on tokens

FastRouter's claim (and the only one that matters in a cost-focused comparison) is simple:

• A single OpenAI-compatible endpoint across GPT-6, every current Claude model, every current Gemini model, and 200+ others.
• $0 markup on every token, regardless of provider or model.
• You pay OpenAI's, Anthropic's, or Google's listed rate, exactly as shown in the pricing table.
• FastRouter charges a flat monthly platform fee instead of a percentage of model spend.

That pricing structure is what makes it "cheapest" in the specific way engineers mean cheapest: lowest long-run marginal cost at a given quality bar.

A percentage router is misaligned with you. It makes more money when you spend more — especially when you spend more on the most expensive models.

A flat-fee layer is at least directionally aligned. Your spend can go down without the platform fighting you.

And the point about where this matters most holds up:

• A 5% fee on Gemini Flash-Lite traffic is nearly invisible.
• A 5% fee on GPT-6 Astra output traffic is not.

In other words: the router tax hits hardest exactly where you're already exposed.

How you actually capture the savings (after you stop paying markup)

Zero markup gets you to "not overpaying for plumbing."

Then you still have to do the production work that makes the table real: systematically preventing expensive models from handling cheap tasks.

Three mechanisms matter here. None of them are magic. They're the boring controls you end up building yourself if you DIY multi-provider.

1) Task-level routing: keep GPT-6 Astra off workloads that don't deserve it

Not every request needs GPT-6 Astra. Not even close. But if you don't enforce that in the platform layer, it will happen anyway.

What happens in real systems is simple: teams start with one default model (often the best one), ship features, and then never come back. The codepath that "temporarily" used the frontier model becomes permanent. Six months later, the bill is a crime scene and nobody wants to touch the prompts because they're tied to product behavior.

Task-level routing is how you prevent that drift: FastRouter's routing can send lighter tasks to Gemini Flash-Lite or Claude Haiku automatically, reserving the expensive frontier models for the requests that actually need that capability — the same principle behind the Architect/Editor pattern, where one capable model plans and cheaper models execute the bulk of the work.

That's the mechanic. The headline isn't "automatic routing." The headline is: you cannot afford to let your default model be your most expensive model when the output spread is ~33x.

So the practical goal of routing isn't "multi-provider access." It's cost containment through defaults and policy. You want a world where cheap, high-volume traffic lands on cheap models by default, and reaching for an expensive model is a deliberate choice — not an accident inherited from whatever the default happened to be when the feature shipped.

2) Evaluation-verified switching: because "cheaper" that breaks quality is not cheaper

Switching to cheaper models only saves money if the output still meets your bar.

Otherwise you pay later — support tickets, retries, escalations, longer prompts to compensate, and engineers spending time patching regressions. That time isn't free, and it tends to show up as "why is on-call awful now" instead of as a clean line item.

FastRouter checks a proposed switch against your own quality bar before recommending it — so "cheapest" doesn't quietly become "cheapest and worse."

That's the right philosophy. Any routing system that treats "cheapest model" as the primary objective without a quality gate is eventually going to ship a regression you only catch through customer complaints.

Cross-provider switching makes this even more important, because each model family has different failure modes. You need your own evaluations to tell you what breaks for your specific prompts and distributions, not a generic benchmark.

3) Unified observability across providers: you can't cut what you can't attribute

The fastest way to fail at cost optimization is fragmented visibility.

With three providers, you can always find a story that explains a bill spike: "OpenAI costs went up because usage went up." "Anthropic got cheaper last month so routing must be working." "Google spend is fine, it's the cheap provider."

That's how teams lull themselves into not investigating.

One dashboard showing spend by model and by provider is what makes the pricing table above actionable instead of theoretical — you can see, in your own traffic, exactly where GPT-6 Astra calls are happening that could have run on something a fraction of the cost.

If you can't slice spend by model and provider in one place, you can't answer basic production questions quickly: which endpoints are actually generating expensive output, which deployments changed token usage patterns, or whether long-context traffic is accidentally landing in the wrong tier (a real risk when a model has a pricing split like Gemini 3.1 Pro Preview's ≤200K vs >200K context rates).

And if you can't answer those questions, you're not managing spend. You're just paying it.

Bottom line

If you're accessing GPT-6 Astra, Claude (Opus 5 / Sonnet 5 / Haiku 4.5), and Gemini (3.1 Pro Preview / 3.5 Flash / 3.1 Flash-Lite) in production, you're already playing on a price surface where output ranges from $50.00/1M down to $1.50/1M — a ~33x spread. That spread is big enough that "default model selection" and "oops we left it on the expensive one" become existential cost problems, not minor inefficiencies.

So the cheapest way to access all three through one API starts with a non-negotiable: don't pay a percentage markup on tokens. At $30,000/month in combined traffic, a typical 5-5.5% markup is $1,500-$1,650/month of permanent tax — money that could have gone to more evals, more caching, or just stayed unspent.

FastRouter's zero-markup structure (provider list rates pass-through, flat monthly platform fee) is the structural win. Routing, evaluation-verified switching, and unified observability are how you cash in on it — by keeping high-cost models like GPT-6 Astra where they belong: on the requests that actually need them, not on the ones that just happened to hit the default.