Published rates
Preliminary Pareto 26.10 Preview 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.
- Input
- $0.80/Mtok
- Cached input
- $0.03/Mtok
- Output
- $3.20/Mtok
Published benchmark scores
Preliminary Pareto 26.10 Preview 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.
The mechanism
Pareto is our blended AI model, available through the Unbiased AI platform. Multiple LLMs are engaged in parallel on every request, and their results are synthesized dynamically based on the task. You send one request and get one answer. The blend includes frontier and open models.
It is worth being precise about what it is not. Pareto is not a “model router.” Routers read the prompt, estimate its complexity, and pick a single model. If a reliable way to do that existed, we'd use it. Routers also break prompt caching when they switch models mid-conversation, and at scale the cache misses erase the savings. Pareto works the same way on easy prompts and hard ones, and the savings hold across long trajectories.
Model composition can change. See the published release notes and current model card for available release information. The Terms govern service changes.
Latency varies by workload. Current-release latency comparisons have not been published.
Estimating your bill
Estimate spend using input, cached-input, and output tokens at the published rates. A token estimate is not a measured task bill. Use the cost calculator.
Where other models score higher
GPT 6 Astra scores higher than Pareto on Terminal-Bench 4.0, MMMU-Pro, HLE (no tools), and ArXivMath. Fable 5.1 also scores higher on Terminal-Bench 4.0, MMMU-Pro, and HLE (no tools). DeepSWE is a three-way tie. See the complete published table.
Check it yourself
Nothing above requires trust. Here are four ways to evaluate Pareto on your workload:
- Rerun ours. The harness is public at pareto-evals: reproducible head-to-head runs, accuracy and cost per task, with cost read from the API's own response where it is reported, against contamination-checked datasets. It points at any OpenAI-compatible endpoint, including the one you use today.
- Run yours. Bring your heaviest workload and compare Pareto with your current model. Measure the quality delta and the bill. Apply; a person replies within 24 hours.
- Start small: Purchase pay-as-you-go credits. We’re still in beta, so every signup is reviewed and approved by hand before your API access turns on.
- Get an hour of help. A free live onboarding hour with the team comes with any purchase: a real workload, tuned live. Optional.
If the numbers hold on your workload, you will know exactly what you are paying for and why it costs what it does. If they don't, you should stay where you are. Either way, the question “am I overpaying?” stops being unanswerable. Questions to [email protected]; a person reads every one.