LFM3-1.7B vs MAI-1-preview

Liquid AI · US  |  Microsoft · US · Updated June 2026

Quick verdict

Pick LFM3-1.7B for very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use or liquid says it can power agentic applications that used to need 9b parameters. Pick MAI-1-preview for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai or ranked in the top 15 on lm arena at launch. Choose LFM3-1.7B if you need self-hosting or data privacy; MAI-1-preview if you want a managed API.

LFM3-1.7B (Liquid AI) and MAI-1-preview (Microsoft) are two of the models people most often weigh against each other in 2026. LFM3-1.7B is liquid AI's October 27, 2026 on-device text model - about 1.73B parameters and 256K context, with open weights under a license capped at $10M annual revenue. MAI-1-preview is microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecLFM3-1.7BMAI-1-preview
ProviderLiquid AI (US) Microsoft (US)
ReleasedOctober 27, 2026 August 28, 2025
Context window256K tokens (~384 pages) 128K (~192 pages)
Price (in/out)Open weight (self-host / free) Not published
Open weight?Yes — self-hostable No — API only
Modalitiestext text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Very small open-weight text model - about 1.73B parameters with a 256K-token context for on-device use

LFM3-1.7B

Its 256K tokens window holds about 2× more than MAI-1-preview's 128K in a single prompt.

Liquid says it can power agentic applications that used to need 9B parameters

LFM3-1.7B

Liquid AI's October 27, 2026 on-device text model - about 1.73B parameters and 256K context, with open weights under a license capped at $10M annual revenue — and it carries the larger 256K tokens context.

Part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks

LFM3-1.7B

MAI-1-preview is comparatively weak here — a 'preview' release, not yet positioned as Microsoft's primary Copilot model

Microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI

MAI-1-preview

MAI-1-preview lists microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI among its strengths; LFM3-1.7B does not.

Ranked in the top 15 on LM Arena at launch

MAI-1-preview

MAI-1-preview lists ranked in the top 15 on LM Arena at launch among its strengths; LFM3-1.7B does not.

Trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment

MAI-1-preview

MAI-1-preview lists trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment among its strengths; LFM3-1.7B does not.

Largest single-prompt input

LFM3-1.7B

Its 256K tokens window is about 2× larger than MAI-1-preview's 128K, fitting roughly 384 pages in one prompt.

Which should you pick?

Someone analysing very long documents or codebases

→ LFM3-1.7B

Larger 256K tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ LFM3-1.7B

Open weights let you run it on your own hardware; MAI-1-preview is API-only.

Anyone whose priority is very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use

→ LFM3-1.7B

It is specifically built for that.

Anyone whose priority is microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai

→ MAI-1-preview

That is its strongest area.

LFM3-1.7B: where it fits

Liquid AI's October 27, 2026 on-device text model - about 1.73B parameters and 256K context, with open weights under a license capped at $10M annual revenue. Released October 27, 2026 by Liquid AI, it is built for very small open-weight text model - about 1.73B parameters with a 256K-token context for on-device use, liquid says it can power agentic applications that used to need 9B parameters, part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks, and supports 16 languages.

Its trade-offs are real: lFM Open License v1.0 covers commercial use only below $10M annual revenue; larger firms need a paid license, liquid says it is not recommended for agentic coding or complex multi-step reasoning because of its size, text-only; vision and audio need other LFM3 checkpoints, and no published per-token API price found. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

MAI-1-preview: where it fits

Microsoft's first fully in-house foundation model - a strategic break from sole reliance on OpenAI, trained on ~15,000 H100 GPUs. Released August 28, 2025 by Microsoft, it is built for microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on OpenAI, ranked in the top 15 on LM Arena at launch, trained on roughly 15,000 NVIDIA H100 GPUs, a genuine internal infrastructure investment, and rolled into Copilot alongside OpenAI models, giving Microsoft a real second option.

Its trade-offs: a 'preview' release, not yet positioned as Microsoft's primary Copilot model, no public per-token API pricing - not sold as a standalone product, and distinct from Microsoft's later MAI-Thinking-1 reasoning model - an earlier, more general-purpose entry.

The bottom line for this matchup

The defining split here is open vs. closed. LFM3-1.7B gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. MAI-1-preview gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both LFM3-1.7B and MAI-1-preview without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is LFM3-1.7B or MAI-1-preview better for coding?

Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, LFM3-1.7B leans toward very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use while MAI-1-preview leans toward microsoft's first fully in-house, end-to-end foundation model - a historic break from relying solely on openai, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, LFM3-1.7B or MAI-1-preview?

LFM3-1.7B is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-1-preview is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

LFM3-1.7B — 256K tokens vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both LFM3-1.7B and MAI-1-preview together?

Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, MAI-1-preview and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, LFM3-1.7B or MAI-1-preview?

LFM3-1.7B — released October 27, 2026, about 14 months after MAI-1-preview.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.