NOLO
Honest comparison

NOLO next to the other private ones.

Not against the giants that read everything, against the assistants that also take privacy seriously: DuckDuckGo's Duck.ai, Brave Leo, Venice, Proton Lumo and Mistral's Le Chat. Then the real benchmark scores of the models NOLO actually runs, with sources.

Privacy & features

Where each one stands.

Based on each company's own public documentation as of June 2026. Their terms can change, so check the source if a detail matters to you.

NOLO Duck.ai Brave Leo Venice Proton Lumo Le Chat
No account or email Yes Yes Yes Noemail Noaccount Noaccount
History stays on your device Yes Yes Yes Yes Serverencrypted Server
Your raw IP never reaches the model Yes Yes Yes Partial Yes Partial
Tells you which model answers Yesopen-weights Yes Yes Yes Partial Partial
Real-time web search Yes Yes Yes Yes Yes Yes
Image generation Yes Yes No Yes No Yes
Voice input Yes Yes Partial Partial No Yes
Full interface in 30 languages Yes Partial Partial Partial Partial Partial
Long context (up to ~1M tokens) YesPro Varies Varies Varies Varies Varies
Android app YesAPK Yes Yesbrowser Yes Yes Yes
Free tier Yes€0 Yes Yes Yes Yes Yes

Legend: Yes = supported · Partial / Varies = limited or conditional · No = not offered. Sources: Duck.ai, Brave Leo, Venice, Proton Lumo, Le Chat.

The honest read: several of these are genuinely good, and we're not here to pretend otherwise. Duck.ai and Brave Leo are excellent if you live in their browser or want a quick anonymous question. Proton Lumo encrypts your history on its servers. What's specific to NOLO is the combination: no account at all, history that never leaves your device by default, open-weights models we'll name, and the full toolset (search, files, images, voice, live data, exports) in one private workspace, in 30 languages.

Real benchmarks

The models NOLO runs, by the numbers.

We don't claim to be the smartest AI on earth, that's not the point of NOLO. But you deserve to know exactly what's under the hood. These are the providers' own published scores on standard public tests, not numbers we made up.

gpt-oss-20b

Free · Fast

OpenAI's open-weights model (Apache 2.0), same family as gpt-oss-120b. Powers the free plan. Fast, and reasons when asked. Image understanding for uploads runs on a separate shared vision model used across every plan.

MMLU-Pro (knowledge)85.3
GPQA Diamond (science)71.5
AIME 2025 (math, with tools)98.7

Source: OpenAI, gpt-oss model card (Aug 2025). openai.com

gpt-oss-120b

Plus · Reasoning

OpenAI's open-weights reasoning model (Apache 2.0). Powers the Plus plan. Built for analysis, planning and harder questions.

MMLU-Pro (knowledge)90.0
GPQA (science)80.1
AIME 2025 (math, with tools)97.9

Source: OpenAI, gpt-oss model card (Aug 2025). openai.com

DeepSeek V4 Flash

Pro · Long context

An efficient Mixture-of-Experts model (284B total, ~13B active per token) built for huge documents. Powers the Pro plan. We show its verified architecture rather than borrow the larger V4-Pro's headline scores, because that wouldn't be the model you're actually using.

Context window1M
Max output tokens384K
Active params per token~13B

Source: DeepSeek V4 family specifications (released Apr 2026). Headline reasoning scores like SWE-bench Verified ~80% belong to the larger V4-Pro variant, not Flash.

Same test: GPQA Diamond

Science reasoning

Side by side with the open models the other privacy-first assistants actually run. One public test, each maker's own figure. NOLO's models in bright purple.

gpt-oss-120b · NOLO Plus80.1
gpt-oss-20b · NOLO Free71.5
Llama 3.3 70B50.5
Claude 3.5 Haiku40.8
Mistral Small 336.9

Those three are the open models behind Duck.ai, Brave Leo, Venice, Le Chat and Proton Lumo. Sources: Meta, Anthropic and Mistral model cards.

Same test: MMLU-Pro

Broad knowledge

Professional-level knowledge across subjects. Same public test, makers' own published figures.

gpt-oss-120b · NOLO Plus90.0
gpt-oss-20b · NOLO Free85.3
Llama 3.3 70B68.9
Claude 3.5 Haiku63.4

Mistral Small 3 reports MMLU (81.0), not MMLU-Pro, so we leave it out here to keep the test identical. Sources: Meta and Anthropic model cards.

What these mean: MMLU-Pro tests broad knowledge across professional subjects; GPQA is graduate-level science; AIME is competition math. Different labs run tests slightly differently, so treat cross-model comparisons as a guide, not a photo finish. Every number above is the model maker's own published figure, linked at the source.

Good models. No account. Honest about both.

Try it and see where it lands for your own work.