A blended model is a system that engages multiple underlying LLMs on each request and synthesizes the result dynamically, presented to the caller as a single model with one API surface. Unlike an aggregator catalog, the caller never selects an underlying model; unlike sending each request to a single model, the blend can combine work rather than only forwarding it.
Why it matters
Model selection is the largest unpriced cost in production LLM use: pay frontier rates on routine traffic and you lose more than any fee; send hard tasks to cheap models and failed runs bill you twice. A blended model moves that decision from a quarterly engineering argument to a per-request mechanism.
Pareto by Unbiased is built this way: it selects frontier and open models for each request and blends their outputs into one answer, so AI providers compete for every request. How a Pareto answer gets made covers its method.
Blended model, model picker or mixture of experts?
All three put more than one model, or more than one expert, behind a single name, which is why they get confused. They combine work at different layers.
| Approach | Where the combining happens | What you call | What comes back |
|---|---|---|---|
| One model per request | Before the call: you, or an automatic picker, choose one model from a catalog | A catalog model name | That one model's answer |
| Mixture of experts (MoE) | Inside one model's weights: a gating network activates a few expert sub-networks for each token | One model | That model's answer |
| Blended model | Across several complete models, on each request | One model name, such as pareto | One answer built from several models' outputs |
The Mixture-of-Agents paper below describes its approach as extending mixture of experts from sub-networks inside a model to whole models. Verified October 25, 2026.
What the research shows about blending
Two public papers measured what happens when several complete LLMs work on the same request. Both are research systems, not Pareto.
- No single model wins every request. In LLM-Blender (Jiang, Ren and Lin, ACL 2023), 11 open-source models answered the same 5,000 instructions. The model that came first most often, Vicuna, was the best answer on only 21.22% of them. Ranking the candidates and fusing the top three into a new answer scored an average GPT-Rank of 3.01, against 3.90 for the best single model, Open Assistant (lower is better).
- Synthesizing beats picking. In Mixture-of-Agents (Wang et al., 2024), layers of open-source models read each other's drafts before a final model wrote the answer. Using open-source models only, it reached a 65.1% length-controlled win rate on AlpacaEval 2.0, against 57.5% for GPT-4o. A variant that only selected the best draft, instead of writing a new answer from all of them, scored significantly lower.
- Different models add more than repeats of one. In the same paper, six different proposer models scored 61.3%, against 56.7% for six drafts from a single model.
What a blended request costs
Pareto bills the request's token counts at published per-token rates, the same way a single model does. A request billed at 3,000 input tokens and 800 output tokens costs $0.0024 for input (3,000 × $0.80 per million) plus $0.00256 for output (800 × $3.20 per million): $0.00496, about half a cent. Cached input bills at $0.03 per million instead of $0.80. Rates are on the pricing page.
How to test a blended model on your traffic
- Collect a sample of real requests that includes your hardest cases, not only typical ones.
- Send each request to the blend and to your current model with the same prompts and settings.
- Grade both sides with the same checks: tests that pass, valid output schema, or a reviewer who cannot see which model wrote which answer.
- Compare cost per successful task, not cost per token. A cheaper answer that fails costs you a retry.
- Before switching, run a copy of live traffic in shadow mode. The evaluation recipe walks through these steps with Pareto.
Measured: what the blend delivered
Preliminary Pareto 26.10 scores and cost per task on four benchmarks are on the model card. These results may change before final publication, and no composite score is published.
To see a blend handle everyday requests, read three real Pareto answers, unedited, each shown with the token counts the API reported.
Common questions
Is a blended model just one model picked per request?
No. Sending each request to one model from a menu gets you that one model's answer. A blend selects several models for a request and blends their outputs, so its output is not reducible to any single underlying model's answer.
How is a blended model priced?
Pareto is billed at published per-token rates: $0.80 per million input tokens, $0.03 cached input and $3.20 output, listed on the pricing page. Aggregators, by contrast, fee the credits or mark up tokens on top of list prices.
How do I verify a blended model's quality claims?
Same-harness benchmarks with both sides published, then your own traffic. Anything less is marketing; this is why the evaluation harness is public.
Is a blended model the same as mixture of experts?
No. Mixture of experts is an architecture inside one model: a gating network activates a few expert sub-networks for each token. A blended model works across several complete models on each request and blends their outputs into one answer.
Do I need a new SDK to call a blended model?
No. Pareto speaks the OpenAI and Anthropic API formats under one model string, pareto. Keep the client you already use, change the base URL, the key and the model string, then check your streaming, tools and error handling. Setup is on the developer docs.
Compare any two models
List rates and dated competitor measurements: prices and measured bills. Preliminary Pareto 26.10 benchmark costs per task from October 1, 2026 are on the model card. Verbosity: Edition 2.
Try it on one part of your codebase
Set Pareto up in the coding agent you already use and give it a week of real work. Pareto starts at $10 a month.
Read https://unbiased.ai/glossary/blended-model/ and tell me what matters for my setup. If Pareto fits, read https://unbiased.ai/works-with/agent.md, find the coding agent I use on this machine, and set it up to use Pareto. Ask before changing any file, and never ask me to paste my API key into chat.
It asks before changing files and never needs your key in chat. Read the agent runbook
