Published rates
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.
- Input
- $0.80/Mtok
- Cached input
- $0.03/Mtok
- Output
- $3.20/Mtok
Published benchmark scores
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. The two agentic benchmarks below plot each homepage-table model that has both a published score and a published cost per task.
Terminal-Bench 4.0 Agentic terminal use
% solved against $/task. Up and left is better.
Show the numbers
| Model | % solved | $/task | Source |
|---|---|---|---|
| Claude Sonnet 5.5 | 70.6% | $12.54 | Anthropic |
| GPT 6 Astra | 59.6% | $5.86 | Artificial Analysis |
| GPT-6.1 Sol | 56.1% | $1.82 | Artificial Analysis |
| Fable 5.1 | 55.1% | $15.78 | Artificial Analysis |
| Pareto 26.10 | 50.8% | $0.48 | Unbiased, preliminary October 1, 2026 run |
| DeepSeek 4.1 Flash | 26.8% | $1.16 | Artificial Analysis |
| GPT-6 Luna | 12.6% | $0.24 | Artificial Analysis |
DeepSWE v1.1 Agentic coding
% solved against $/task. Up and left is better.
Show the numbers
| Model | % solved | $/task | Source |
|---|---|---|---|
| GPT-6.1 Sol | 75.2% | $0.65 | OpenAI |
| GPT 6 Astra | 74.0% | $4.43 | DeepSWE leaderboard |
| Pareto 26.10 | 69.9% | $0.24 | Unbiased, preliminary October 1, 2026 run |
| GPT-6 Luna | 66.6% | $0.22 | OpenAI |
The mechanism
Pareto is Unbiased’s blended AI model: it selects frontier and open models for each request and blends their outputs into one answer. You send one request to one model string, pareto, and get one answer back.
- Your request
- One model string, pareto, over the documented OpenAI and Anthropic API formats
- ParetoSelects frontier and open models for each requestBlends their outputs into one answer
- One answer
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
On each of the four published benchmarks, at least one model scores higher than Pareto 26.10. GPT 6 Astra, GPT-6.1 Sol and Claude Sonnet 5.5 score higher on all four: DeepSWE v1.1, Terminal-Bench 4.0, HLE (text-only) and GPQA-Diamond. Fable 5.1 scores higher on Terminal-Bench 4.0, HLE (text-only) and GPQA-Diamond, and has no reported DeepSWE v1.1 score. Pareto 26.10 results are preliminary. The charts above are a snapshot of published results as of October 1, 2026, not a leaderboard. We have not run GPT-6.1 Sol or Claude Sonnet 5.5 ourselves; their scores are published by their labs and independent evaluators. See the complete published table and its sources.
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. Test it on your own prompts: 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 info@unbiased.ai; a person reads every one.