LFM3-1.7B vs Microsoft Phi-4
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 Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. On a tight budget at scale, LFM3-1.7B is the value pick.
LFM3-1.7B (Liquid AI) and Microsoft Phi-4 (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. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: LFM3-1.7B holds 16× more — 256K tokens (~384 pages) vs 16K (~25 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: LFM3-1.7B is the newer model by about 22 months (released October 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | LFM3-1.7B | Microsoft Phi-4 |
|---|---|---|
| Provider | Liquid AI (US) | Microsoft (US) |
| Released | October 27, 2026 | January 10, 2025 |
| Context window | 256K tokens (~384 pages) | 16K (~25 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.07/$0.14 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| 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
Its 256K tokens window holds about 16× more than Microsoft Phi-4's 16K 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
Microsoft Phi-4 is comparatively weak here — text only — no image, audio or video input
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
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
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
LFM3-1.7B is comparatively weak here — no published per-token API price found
Runs on modest or local hardware
Microsoft Phi-4
Microsoft Phi-4 lists runs on modest or local hardware among its strengths; LFM3-1.7B does not.
Lowest cost at scale
LFM3-1.7B
Its weights are open, so at volume you pay for your own hardware instead of Microsoft Phi-4's $0.07/$0.14 per 1M tokens.
Largest single-prompt input
LFM3-1.7B
Its 256K tokens window is about 16× larger than Microsoft Phi-4's 16K, fitting roughly 384 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ LFM3-1.7B
At Open weight (self-host / free) it undercuts Microsoft Phi-4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ LFM3-1.7B
Larger 256K tokens window fits more in one prompt.
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 strong reasoning for a small 14b open-weight model
→ Microsoft Phi-4
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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
LFM3-1.7B and Microsoft Phi-4 overlap enough that the right pick depends on your specific job. LFM3-1.7B costs less per token; LFM3-1.7B holds the larger context; and each leads in its own area — LFM3-1.7B for very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use, Microsoft Phi-4 for strong reasoning for a small 14b open-weight model. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both LFM3-1.7B and Microsoft Phi-4 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 Microsoft Phi-4 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 Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LFM3-1.7B or Microsoft Phi-4?
LFM3-1.7B is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
LFM3-1.7B — 256K tokens vs 16K, about 16× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both LFM3-1.7B and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, Microsoft Phi-4 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 Microsoft Phi-4?
LFM3-1.7B — released October 27, 2026, about 22 months after Microsoft Phi-4.
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.