LFM3-1.7B vs MAI-Thinking-1
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-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. Choose LFM3-1.7B if you need self-hosting or data privacy; MAI-Thinking-1 if you want a managed API.
LFM3-1.7B (Liquid AI) and MAI-Thinking-1 (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-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Their biggest split is open vs. closed weights, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Cost model: LFM3-1.7B ships open weights you can self-host (hardware cost only, no per-token fee), while MAI-Thinking-1 is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: both advertise 256K tokens (~384 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: LFM3-1.7B is the newer model by about 3 months (released October 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | LFM3-1.7B | MAI-Thinking-1 |
|---|---|---|
| Provider | Liquid AI (US) | Microsoft (US) |
| Released | October 27, 2026 | August 12, 2026 |
| Context window | 256K tokens (~384 pages) | 256K (~384 pages) |
| Price (in/out) | Open weight (self-host / free) | Not published |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text | text, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not 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
Open weights make this possible at all — MAI-Thinking-1 is API-only, so it cannot leave the vendor's servers.
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 its weights are open while MAI-Thinking-1 is API-only.
Part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks
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 is the newer of the two.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
LFM3-1.7B is comparatively weak here — liquid says it is not recommended for agentic coding or complex multi-step reasoning because of its size
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
MAI-Thinking-1 lists microsoft's first in-house flagship reasoner, trained without OpenAI distillation among its strengths; LFM3-1.7B does not.
Efficient reasoning at low token cost for its class
MAI-Thinking-1
MAI-Thinking-1 lists efficient reasoning at low token cost for its class among its strengths; LFM3-1.7B does not.
Which should you pick?
A team with data-privacy or self-hosting needs
→ LFM3-1.7B
Open weights let you run it on your own hardware; MAI-Thinking-1 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 very strong math reasoning (aime 2025 97%, aime 2026 94.5%)
→ MAI-Thinking-1
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-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released August 12, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
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-Thinking-1 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-Thinking-1 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 pricingFrequently asked questions
Is LFM3-1.7B or MAI-Thinking-1 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-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LFM3-1.7B or MAI-Thinking-1?
LFM3-1.7B is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while MAI-Thinking-1 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?
Both advertise 256K tokens (~384 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both LFM3-1.7B and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, MAI-Thinking-1 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-Thinking-1?
LFM3-1.7B — released October 27, 2026, about 3 months after MAI-Thinking-1.
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.