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# TrustedRouter launches three new encrypted 'fusion' models that combine multiple LLMs, including one exclusively with U.S. model providers
[Carl Franzen](https://venturebeat.com/author/carlfranzen)
6:39 am, PT, November 11, 2026
Scientists in lower foreground look up at a particle accelerator collision releasing colorful rays and code
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Choosing an AI model for your company is hard enough. Choosing which one to use for every job is harder. Should you send a tricky code review to [Anthropic’s Claude Fable 5.2](https://www.anthropic.com/claude-fable-and-mythos-5-2), try an open-weight model such as [Meituan’s LongCat-2.5](https://venturebeat.com/technology/meituan-releases-the-open-weights-of-its-longcat-2-5-llm-with-vision-under-a-permissive-mit-license), or use something else? The answer can change from one task to the next, even inside the same application.
Fortunately, a number of firms have sprung up to try and automatically answer this question for you by intelligently touting different tasks or parts of tasks to the model best suited to get them done most cost-effectively.
One of the latest is the startup [TrustedRouter](https://trustedrouter.com/), which also seeks to provide enterprises and developers the added benefit of keeping their data private via [eligible confidential routes](https://nytokenexchange.com/confidential), which keep user prompts encrypted until they reach verified hardware at the provider running the model. Instead of sending every request directly to OpenAI, Anthropic, or another model developer, businesses can use TrustedRouter to direct their workloads to different providers based on cost, availability, model capabilities, and privacy requirements.
The prompt is decrypted inside that protected environment, which is designed to keep it out of the provider’s operators’ hands. The protection depends on the route and the caller’s privacy settings.
Now, TrustedRouter is launching three encrypted “combo” models based around this idea through its existing API. Developers can access them through the [New York Token Exchange](https://nytokenexchange.com/) using model IDs that start with `nyte/`.
- **Prometheus 4.0** combines independent answers from a panel of six open-weight models into a single response.
- **Liberty 4.0** also combines multiple answers, drawing on models from NVIDIA, Reflection AI and Meta — three U.S. AI model providers.
- **Polyphemus 1.0** picks a single model to answer each request. TrustedRouter [documents it for standard privacy](https://trustedrouter.com/models/trustedrouter/polyphemus-1.0)
All three use an encrypted connection to TrustedRouter’s gateway. Provider-side confidential processing is opt-in for Liberty 4.0 and Prometheus 4.0. Polyphemus 1.0 uses standard privacy.
In an exclusive video interview with VentureBeat, founder and CEO Joseph Perla explained how the models work and why the company wants to give developers more ways to match models to the work they’re doing.
"Everyone should use a router because no provider, no one provider is 100% reliable," Perla said. "Even Claude goes down sometimes, so you've got to use a router."
Each of the new three models decides where and how to route its requests to the underlying models. A difficult research question might benefit from several independent answers, while routine classification may only need one model. Each preset has its own [routing and privacy characteristics](https://trustedrouter.com/models).
According to Perla, the system uses confidential computing technology supported by NVIDIA GPUs to encrypt AI requests while they're being processed, preventing infrastructure providers and the exchange itself from reading customers' sensitive information.
It also aims to verify which AI model actually processes each request, reducing the risk of customers paying for an expensive model but unknowingly receiving results from a cheaper alternative. These capabilities are central to Perla's argument that businesses can safely purchase AI services from unfamiliar infrastructure providers through a shared marketplace.
Perla said that the three offerings fit into a larger plan for the [New York Token Exchange](https://nytokenexchange.com/media-kit), or NYTE. Lore Hex Corp operates both services.
TrustedRouter is the gateway that connects applications to models and hosting providers. NYTE uses that gateway to sell AI processing on demand and offer advance capacity reservations.
The [two API addresses](https://trustedrouter.com/docs) share keys, account balances and routing behavior, and developers can call the same preset using either the `nyte/` or `trustedrouter/` prefix.
## **What happens after you send a prompt?**
Prometheus and Liberty ask several models to answer a prompt independently and in parallel. A separate judge call evaluates their answers, and a synthesizer call produces the final response, according to TrustedRouter. A full Prometheus request uses eight model calls, and a full Liberty request uses five.
A model that misses a source or takes a wrong turn in a calculation can be checked against the others. The synthesizer has to recognize which answer is better supported.
If several panelists repeat the same mistake, agreement alone gives it little reason to correct them. The cost of running the panel and the time spent on synthesis are part of the trade-off.
Prometheus 4.0 is the open-weight panel. The [published lineup](https://trustedrouter.com/models/trustedrouter/prometheus-4.0) comprises Xiaomi’s MiMo-V2.6-Pro, Z.ai’s GLM 5.3, Moonshot AI’s Kimi K3, DeepSeek V4.1 Flash, MiniMax M3 and Alibaba’s Qwen 3.8. TrustedRouter lists a one-million-token context window for the preset. Its rolling `nyte/prometheus` alias follows the latest version, while `nyte/prometheus-4.0` selects this release.
TrustedRouter has yet to specify which model synthesizes the production answers, how many panelists must finish before synthesis begins or how the advertised context limit applies when component models have different limits.
Liberty 4.0 brings together models from NVIDIA, Reflection AI and Meta, three U.S. developers. The [component catalog](https://azure.trustedrouter.com/models/trustedrouter/liberty-4.0) lists NVIDIA’s Nemotron 3 Ultra, Reflection’s Beam and Meta’s Muse Spark Open.
TrustedRouter says each panelist answers independently and the Meta model combines the responses. Beam takes over synthesis if the Meta model is unavailable. If a panelist fails to answer, Liberty can work with the responses it receives.
Polyphemus 1.0 makes its choice before generating an answer. The selector examines the request and chooses a model, which answers in a single call. Its [model documentation](https://trustedrouter.com/models/trustedrouter/polyphemus-1.0) specifies the Responses API and a 65,536-token context window.
It charges $0.05 per million estimated selector input tokens, plus the chosen model’s normal charges. If the selector fails, the general TrustedRouter auto route handles the request and the selector fee drops away. Developers can reuse a session ID and send the conversation history on each turn to improve the chances of reusing cached input.
The session expires after an hour of inactivity. The documentation lists standard privacy for this preset. The selector’s eligible model pool, decision criteria and performance against a fixed-model baseline remain open questions.
## **How much does an answer cost?**
TrustedRouter’s [model catalog](https://trustedrouter.com/models) displays component-route rates for Prometheus 4.0 of roughly $0.01 to $1.48 per million input tokens and $0.13 to $7.39 per million output tokens. It lists Liberty 4.0 at roughly $0.42 to $1.32 for input and $1.27 to $4.48 for output, also per million tokens. Those rates describe parts of a request.
A buyer evaluating the panel needs the total bill, including the work done by each model as well as the judge and synthesizer. TrustedRouter’s [standard markup for prepaid text](https://trustedrouter.com/pricing) is 5.5%, with no subscription. The company documents a 12% total service fee for managed prepaid requests that opt into a [signed inference receipt](https://trustedrouter.com/docs/receipts).
TrustedRouter estimates how much each preset costs per request compared with Claude Fable 5.1. The panel estimates cover all of the calls in a full request. Polyphemus’s estimated savings depend on which model it selects.
- **Liberty 4.0:** TrustedRouter puts Fable 5.1 at 2.3 to 2.9 times Liberty’s cost per request, or roughly 60% savings for Liberty. The full run makes five calls, with three open-weight U.S. models answering, one call to judge their responses and one to combine them.
- **Prometheus 4.0:** TrustedRouter puts Fable 5.1 at 2.5 to 3 times Prometheus’s cost per request, or roughly 60% to 65% savings for Prometheus. The full run makes eight calls, with six open-weight models answering and two more calls for judging and synthesis.
- **Polyphemus 1.0:** TrustedRouter estimates about 60% lower cost when the router selects Claude Opus 5.5 and about 80% lower when it selects GPT-6.1 Sol.
TrustedRouter provided relative savings figures. The underlying dollar costs, tasks, quality and latency measurements, and whether the Polyphemus figures include the selector fee remain open.
The most concrete benchmark result TrustedRouter has published for the Prometheus line comes from an earlier version.
In [its July test results](https://trustedrouter.com/blog/prometheus-2-new-draco-state-of-the-art), TrustedRouter reports that Prometheus 2.0 scored 77.4 across all 100 tasks in [Perplexity’s DRACO deep-research benchmark](https://www.perplexity.ai/hub/blog/evaluating-deep-research-performance-in-the-wild-with-the-draco-benchmark). Its write-up lists 65.3 for Claude Fable 5, 63.3 for GPT-5.5 and 60.3 for Claude Opus 4.8 as single-model reference scores.
Perla pointed to those research tests in explaining why he expects panels to outperform individual models on difficult queries. The result applies to the earlier configuration on a research benchmark. A comparison on matched tasks, with cost, quality and latency reported together, would show whether the savings hold for buyers’ workloads.
In a VentureBeat test, Claude Code called DeepSeek Flash through TrustedRouter. It correctly returned Paris as the capital of France. Claude Code then estimated that Fable 5.1 would cost about 70 times as much for the same question. That estimate illustrates the price difference for one simple request.
A harder test would measure how often a panel gets a usable answer that a cheaper single model misses, and how much the extra work costs.
## **How TrustedRouter compares with GitHub, Sakana, OpenRouter and Arcade**
[GitHub Copilot Fusion](https://docs.github.com/en/copilot/concepts/models/copilot-fusion) makes its choice inside the coding agent. Depending on the task and effort setting, it can use one model, start with an efficient model and escalate after a quality check, or have one model review another’s draft. GitHub gives enterprise administrators control over eligible models.
In [Artificial Analysis’s independent test](https://artificialanalysis.ai/articles/github-copilot-fusion-orchestrates-anthropic-and-openai-models-to-balance-performance-and-cost), Fusion Max averaged $5.59 per coding task, against $11.42 for Claude Code running Claude Opus 5.5 at its Max setting, with a two-point gap on the Coding Agent Index.
As [VentureBeat previously reported](https://venturebeat.com/orchestration/github-copilot-fusion-mixes-models-to-achieve-near-opus-5-5-coding-performance-at-half-the-per-task-cost), that comparison measures the complete coding agents on the same test. TrustedRouter’s published DRACO scores measure deep research, so they answer a different question.
Sakana AI’s [Fugu Max and Fugu Ultra v2](https://sakana.ai/fugu-max-re