import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "unbiased/pareto-26.10-preview", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "unbiased/pareto-26.10-preview", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"unbiased/pareto-26.10-preview","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
Preview release — Unbiased states behaviour may change without notice. Cached input is billed separately.
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| DeepSWE | 69.9% | Agentic software engineering: resolving real repository tasks end to end | Source | October 1, 2026 |
| Terminal-Bench | 50.8% | Autonomous shell/terminal task completion | Source | October 1, 2026 |
| Humanity's Last Exam | 49.9% | Expert-level questions across many domains | Source | October 1, 2026 |
| GPQA Diamond | 92.4% | Google-proof graduate science questions (hardest subset) | Source | October 1, 2026 |
Published by Unbiased from runs dated 1 October 2026 and marked preliminary; Terminal-Bench 4.0 and DeepSWE v1.1. Not independently reproduced.
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Pareto 26.10 Preview This page | Long-context research, coding & agentic workflows | |||
| Research, coding & agentic workflows |
It is a preview release of Unbiased's composite model. Rather than one set of weights, Pareto runs a mix of frontier and open-source models against the same request and returns the best result. The 26.10 preview raises the context window to 1,048,576 tokens, four times the 262,144 of the September release.
Two things: the context window grows from 262,144 to 1,048,576 tokens, and the price drops — $1.10032 per 1M input tokens against $3.4385 for the September release. It is a preview, so behaviour can change; the stable Pareto card remains the one to build against if you need that.
Unbiased marks this release as a preview that may change without notice, and points to the stable Pareto release for behaviour you can pin. Use the preview to evaluate the larger context window and the lower price; keep production on the stable model until Unbiased promotes this one.
On AI/ML API it is $1.10032 per 1M input tokens and $4.40128 per 1M output tokens, with cached input at $0.041262 per 1M — about a twenty-sixth of the input rate, which makes repeated context cheap to re-send.
Unbiased reports 69.9% on DeepSWE v1.1 (agentic coding), 50.8% on Terminal-Bench 4.0 (agentic terminal use), 49.9% on Humanity's Last Exam text-only, and 92.4% on GPQA-Diamond. The runs are dated 1 October 2026 and Unbiased marks them preliminary — they may change before final publication, and they have not been independently reproduced.
Text and images in, text out. Screenshots, diagrams and scanned pages can go straight into the prompt alongside the surrounding text. It supports streaming, tool calling and vision.
No. Pareto picks a model per request and does not switch mid-conversation, which is what lets prompt caching keep working across turns. Cached input is billed separately at $0.041262 per 1M tokens.
Through the standard chat completions endpoint at https://api.aimlapi.com/v1/chat/completions with the model id unbiased/pareto-26.10-preview. Any OpenAI-compatible client works — point the base URL at AI/ML API and use your AI/ML API key.
