Unbiased: Pareto 26.10 Preview
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Pareto 26.10 Preview is a composite AI model from Unbiased AI, built by Circuit & Chisel, a remote-first team in the US and Canada. It runs several models on the same request and returns one answer, and it is aimed at research, coding, and agentic workflows.
On Unbiased's preliminary, vendor-run benchmarks it scores 69.9% on DeepSWE v1.1 at $0.24 per task, 50.8% on Terminal-Bench 4.0, 49.9% on Humanity's Last Exam (text only), and 92.4% on GPQA-Diamond. On DeepSWE it is level with Fable 5 (70.0%, $13.50 per task). On Terminal-Bench, GPT 6 Astra (58) and Claude Fable 5.1 (56) score higher.
It accepts text and image input, supports tool calling, and has a 1,048,576-token context window with up to 131,072 output tokens. It suits engineering teams, platform teams, and agent builders who want a low cost per task.
Context Window 1M
tokens
Max Output 131K
tokens
Input Cost $0.8
per million tokens
Output Cost $3.2
per million tokens
Tool Use Yes
Release Date Oct 1, 2026
Code Example
Add AI to your app with the Puter.js AI API, no API keys or setup required.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
More AI Models From Unbiased
Pareto 26.9
Pareto 26.9 is a blended AI model from Unbiased AI, the platform of Circuit & Chisel, a remote-first team in the US and Canada. It runs several frontier and open models on each request and returns one answer. It does not switch models within a conversation, so prompt caching still applies. On Unbiased's published scores, Pareto 26.9 gets 74 on DeepSWE, 88 on ArXivMath, and 78 on MMMU-Pro. It scores 51 on Terminal-Bench 4.0 and 49 on Humanity's Last Exam without tools, which are below the frontier models. Because the underlying model mix can change between releases, scores cannot be attributed to a single model. It supports tool calling and has a 262,144-token context window with up to 131,072 output tokens. It suits coding and agent workflows that need consistent behavior across a session.
ChatPareto
Pareto is a blended AI model from Unbiased AI, built by Circuit & Chisel, a remote-first team based in the US and Canada. Rather than routing a request to one model, it runs several models against the same request and returns the best answer, without switching mid-conversation, which preserves prompt caching. On Unbiased's published benchmarks, Pareto 26.9 scores 88/100 on ArXivMath and 78/100 on MMMU-Pro, and ties GPT 6 Astra and DeepSeek 4.1 Flash at 74/100 on the DeepSWE coding benchmark. It trails Fable 5.1 and GPT 6 Astra on Terminal-Bench 4.0 and HLE, and Unbiased publishes those losses alongside its wins rather than only citing favorable results. It accepts text and image input and supports tool calling. It targets engineering teams evaluating model performance, platform teams looking for a cost-effective general-purpose option, and developers building agents that need consistent responses across a session.
Frequently Asked Questions
Pareto 26.10 Preview is no longer available through Puter.js. Explore other AI models for alternatives.
| Price per 1M tokens | |
|---|---|
| Input | $0.8 |
| Output | $3.2 |
Pareto 26.10 Preview was created by Unbiased and released on Oct 1, 2026.
Pareto 26.10 Preview supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
Pareto 26.10 Preview can generate up to 131K tokens in a single response.
Yes, Pareto 26.10 Preview supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Pareto 26.10 Preview API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
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