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OpenRouter alternative without the markup

A direct OpenAI-compatible endpoint with prepaid billing, per-token prices on the page and no routing fee on top.

2026-11-14

GeniusRelay is an OpenRouter alternative built around the same idea — one OpenAI-compatible API in front of many models — with the rate table published on every model page and prepaid, per-token billing instead of a routing layer on top.

If you are moving off OpenRouter, the useful questions are: which models survive the move, what changes at the call site, and what the new bill looks like.

What you get

• 599 models from 37 providers, including OpenAI, Anthropic, Google, DeepSeek, Z.ai, Qwen, Meta and Mistral
• 43 uncensored and abliterated models
• Up to 2M context on supported models
• Automatic failover between routes
• Prepaid, usage-based pricing with no subscription
• Per-key spend caps and expiry
• OpenAI-compatible chat completions

How it compares

The practical difference is the shape of the catalogue. OpenRouter is broad; GeniusRelay leans into long-tail and uncensored models that most gateways bury. If your workload includes roleplay, Chinese-language traffic or fewer-refusal variants, the difference is not marginal.

Migration at the call site

The format is the same, so the change is three values:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.geniusrelaygpt.com/v1",
    api_key="YOUR_GENIUSRELAY_KEY",
)

Then map model IDs. OpenRouter prefixes IDs with the vendor (deepseek/deepseek-chat); GeniusRelay uses the bare ID (deepseek-v4.1-flash). Keep a translation table in config for the models you actually use:

Anything with a vendor prefix needs the prefix stripped. Validate the ID at boot: an unknown model is a 400, not a fallback.

Keep the routing features you actually use

OpenRouter users often rely on provider preferences or fallback lists. In GeniusRelay terms:

• Fallback: configure a retry to a second model on 5xx/429. Keep the fallback cheaper and similar in capability so a failure does not double the bill.
• Provider pinning: not exposed as a separate field; the model ID is the route. Pick the maker's own model when you need it.
• Caching: put stable prefixes first to hit cached-input pricing where supported.

Do not port fallback lists blindly. A three-deep chain on expensive models turns one outage into a triple charge.

Cost model

Prepaid credits mean you see the deduction per request. Two habits keep it predictable:

1. Cap each key. One key per app or customer, with a spending limit and expiry. A retry storm cannot escape its cap.
2. Read the input/output split. Output is the expensive direction on every model; bound max_tokens.

For volume, tier the models: a cheap model for classification and extraction, a mid-tier for generation, a frontier model only for the hard slice. The cheap LLM API guide has the arithmetic; the pricing page has the live rates.

Reference points:

• DeepSeek V4.1 Flash — $0.0503/M input, $0.520/M output
• Claude Haiku 5.5 — $0.130/M input, $0.650/M output
• GLM 5.3 Flash Uncensored — $0.260/M input, 1M context

What to check before switching

• Model parity. Some OpenRouter-only models have no direct equivalent. List them, decide whether a substitute passes your prompts, and drop the rest.
• Context limits. Do not assume the new model carries the same window; read the model page.
• Tool schemas. Models tagged tools accept the OpenAI tools block; others may ignore it. Filter on the tool-calling page.
• Client compatibility. Cursor, Cline and SillyTavern work through the same endpoint: Cursor, Cline, SillyTavern.

FAQ

Is GeniusRelay OpenAI-compatible?

Yes. Change the base URL to https://api.geniusrelaygpt.com/v1 and use a GeniusRelay key.

Can I use it with Cursor or Cline?

Yes. Both support a custom OpenAI-compatible endpoint; see the guides above.

Does it charge a platform fee?

Billing is prepaid per token. The model page rate is the rate; there is no separate subscription.

Are all OpenRouter models available here?

No. The catalogue overlaps heavily on mainstream models and is stronger on uncensored and long-tail Chinese models, but it is not a mirror.

How do I handle failover?

Retry to a second model on 5xx/429, capped by the key limit. Keep the fallback in the same price class.

Can I keep both services running?

Yes. Nothing about the endpoint is exclusive; split traffic while you benchmark and retire one later.

Start

Open the model list and the comparison page, shortlist two substitutes per OpenRouter model you rely on, and run your own prompts. Then follow the quickstart and set a key cap before moving traffic.

A migration checklist

Work through this before cutting traffic:

1. Export the model IDs you actually called in the last 90 days from your logs.
2. Cross them against the model list; mark parity, substitute or drop.
3. Run your evaluation set against every substitute, twice.
4. Rewrite the ID mapping in config and fail fast on unknown IDs.
5. Create one key per application with a spend cap and expiry.
6. Run both endpoints in shadow for a week; compare failure rates and cost per successful call.
7. Move traffic by percentage, not all at once.

Fallback pattern

Fallbacks should be boring and bounded:

def complete(model, messages):
    try:
        return client.chat.completions.create(model=model, messages=messages)
    except Exception as exc:
        if getattr(exc, "status_code", 0) in (429, 500, 502, 503, 504):
            return client.chat.completions.create(model=FALLBACK, messages=messages)
        raise

One hop only. Keep FALLBACK in the same price class so an outage does not change the monthly budget by an order of magnitude. Log which path was taken; a fallback that fires constantly is a signal to change the primary.

Rollback

Keep the old base URL and key in env for two weeks after the cutover. Rolling back is a one-value change, and having it ready removes the pressure to debug in production. Retire the old key only after the failure rate and cost graphs have been flat for a week.

What to measure

• Cost per successful call, not per token.
• Failure rate by model, split by 429, 5xx, malformed output and refusal.
• P95 latency on the primary and the fallback.
• Share of traffic that hit the fallback.

The first number is the one that decides whether the migration worked. The other three explain it when it does not.

Related reading

• Uncensored AI API for fewer refusals
• OpenAI-compatible API with 599 models
• DeepSeek API pricing, all variants