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$10/month
A monthly subscription to get started with Pareto.
- For one person
- 50M tokens a week
- 20 requests per minute
- Billed monthly
New Pareto 26.10 is generally available
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Plans from $10/month · Compare plans ↓Benchmarks
Pareto 26.10 is generally available. The benchmarks below remain preliminary October 1, 2026 preview measurements, not final GA results.
DeepSWE
Cost per task, USD · log scale
Overlapping points. Choose one to inspect it.
| Pareto 26.10 PreviewOct 1, 2026 | 69.9% | $0.24 |
|---|---|---|
| Fable 5Aug 15, 2026 | 70.0% | $13.50 |
| Pareto 26.9Sep 20, 2026 | 70.0% | $0.29 |
Bars use one linear cost scale per benchmark.
See the model card for full results →
Evaluation details
- Preliminary comparison. Pareto 26.10 Preview results are from October 1, 2026 runs and may change before final publication. Other results are transcribed from a September 21, 2026 comparison and are not independently validated. Denominators and cost methodology require confirmation.
- Run dates: October 1, 2026 for Pareto 26.10 Preview; September 20, 2026 for Pareto 26.9; August 15, 2026 for Fable 5. The Pareto 26.9 and Fable 5 DeepSWE results use a 30-task slice, not the full DeepSWE v1.1 set. Pareto 26.9’s HLE result was reported as Humanity’s Last Exam, without a text-only qualifier.
- Each point is one reported result; no performance curves are interpolated. Costs are reported mean USD per task, not token prices. Missing results stay missing and never plot as zero.
- Axes fit the results shown: cost on a log scale, score over at least 30 points. The orange step line shows the best score reached at or below each cost.
- Grey points are other labs’ published results as of October 1, 2026, from the DeepSWE and Terminal-Bench leaderboards, Artificial Analysis, Epoch AI, OpenAI’s GPT-6 Sol and Luna and GPT-6.1 Sol announcements, and Anthropic’s Claude Sonnet 5.5 announcement and system card. They use their own task sets and harnesses, so they are not directly comparable with our runs. Results without a published cost sit on the rail beside the chart, at their score.
- Pareto 26.9 is the previous release, shown for comparison. Fable 5 is a historical comparator, not a model in the published scores. We have not run GPT-6.1 Sol, GPT-6 Luna or Claude Sonnet 5.5 ourselves. Their points are published results from their labs and independent evaluators. Router results from these runs are not shown.
- Published competitor results come from separate evaluators, not our preliminary runs. Each competitor score is paired only with its source’s task cost, never Pareto’s task cost.
PARETO 26.10 PREVIEW / PUBLISHED SCORES
Published individual-model scores
Pareto 26.10 Preview against Fable 5.1, GPT 6 Astra, DeepSeek 4.1 Flash, GPT-6.1 Sol, GPT-6 Luna and Claude Sonnet 5.5. In preliminary October 1, 2026 results, Pareto 26.10 Preview is matching or setting the Pareto frontier on all four benchmarks.
| Benchmark | Pareto 26.10 Preview | Fable 5.1 | GPT 6 Astra | DeepSeek 4.1 Flash | GPT-6.1 Sol | GPT-6 Luna | Claude Sonnet 5.5 |
|---|---|---|---|---|---|---|---|
| DeepSWE v1.1Agentic coding | 69.9 | — | 74.0 | — | 75.2 | 66.6 | 71.0 |
| Terminal-Bench 4.0Agentic terminal use | 50.8 | 55.1 | 59.6 | 26.8 | 56.1 | 12.6 | 70.6 |
| HLE (text-only)Expert reasoning | 49.9 | 59.1 | 54.7 | 39.2 | 52.9 | 38.5 | 55.0 |
| GPQA-DiamondScience reasoning | 92.4 | 93.7 | 96.3 | — | 95.4 | 90.5 | 95.6 |
Task costs: Pareto 26.10 Preview averages $0.24 per task on DeepSWE v1.1, $0.48 on Terminal-Bench 4.0, $0.008 on HLE (text-only) and $0.004 on GPQA-Diamond, the same preliminary runs plotted on the cost chart. These results may change before final publication. Other models’ published costs appear on the cost chart; missing costs are marked Not reported.
Sources: Competitor scores follow the cost chart, each at the source’s highest-scoring published setting. Fable 5.1, GPT 6 Astra and DeepSeek 4.1 Flash use Artificial Analysis where available. We have not run GPT-6.1 Sol, GPT-6 Luna or Claude Sonnet 5.5. DeepSWE v1.1 is from OpenAI (GPT-6.1 Sol, GPT-6 Luna), the Anthropic system card and the DeepSWE leaderboard for GPT 6 Astra. Terminal-Bench 4.0 is from Anthropic for Claude Sonnet 5.5 and from Artificial Analysis for the others. HLE (text-only) is from Artificial Analysis. GPQA-Diamond is from Artificial Analysis for Fable 5.1 and GPT 6 Astra, and Epoch AI for GPT-6.1 Sol, GPT-6 Luna and Claude Sonnet 5.5. The cost chart plots the same results, with a cost where the source publishes one. Different evaluators use different harnesses and settings; these are not a controlled head-to-head. A dash means no sourced score is available here on that benchmark version. Tinted cells mark the best score where more than one is reported, including ties.
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Questions? Contact us
01Why this exists
Nobody's job is to keep you on the most effective model.
A new frontier model ships every few weeks: ahead on one , behind on another, priced differently again. Most teams pick one model and stop checking, because checking is a full-time job nobody was hired to do. And every vendor you’d ask to check for you has a reason to recommend themselves.
Unbiased does that checking for you, continuously, and says so plainly when the honest answer is a model that isn’t ours.
Measured against models from
Anthropic
OpenAI
Google
SpaceXAI
DeepSeek
Moonshot AI
Z.ai
Alibaba Qwen
02How Pareto works
One solution, powered by multiple models.
Unbiased is our platform. Pareto is our own blended AI model, available through the API. One model string, one bill. Under the hood it runs several models on your request and keeps the best answer. Bring-your-own-key traffic splitting is on the roadmap, not sold today.
Pareto runs a mix of and open source models against each other on every request, checks which one earns the answer, and keeps the best result for less.
Your code makes one API call and gets one response back, with no per-model integrations to build or maintain.
Unlike , Pareto never switches models mid-conversation, so your prompt cache, and the savings, stay intact.
03Who's behind this
We believe AI should be accessible to everyone.
Our mission is to make AI accessible to everyone, not just those who can afford frontier prices. Unbiased and Pareto are built and maintained by Circuit & Chisel, a remote-first team across the US and Canada: people making tools for other people. Reach out and talk to us.
Meet the team →04Questions
FAQ
Is Pareto right for every task?
No. The point of Unbiased is to say plainly when Pareto earns the work and when another model is a better fit. Until the public evidence is complete, test Pareto on your own representative workload.
How should I test Pareto?
Start with a real, representative workload and a clear success criterion. Compare quality, reliability, latency, tool behavior, and cost against the model you normally use.
How is Unbiased different from OpenRouter?
OpenRouter offers broad access to third-party models, provider routing, and fallbacks. Unbiased is starting with Pareto and a buyer-side thesis: determine which intelligence earns each workload and be honest when Pareto does not. If you need a large model catalog or provider failover today, OpenRouter may be the right tool.
What happens to my prompts and responses?
Prompt and response content can pass through Circuit & Chisel infrastructure to operate the service. The Data Policy explains current uses, security practices, and open retention questions. Contact the team to confirm which commitments apply to your use before sending sensitive data.
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