LFM3-1.7B vs MiniMax M3

Liquid AI · US  |  MiniMax · China · 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 MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. On a tight budget at scale, LFM3-1.7B is the value pick.

LFM3-1.7B (Liquid AI, US) and MiniMax M3 (MiniMax, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecLFM3-1.7BMiniMax M3
ProviderLiquid AI (US) MiniMax (China)
ReleasedOctober 27, 2026 May 31, 2026
Context window256K tokens (~384 pages) 1M (~1,573 pages)
Price (in/out)Open weight (self-host / free) $0.23/$0.96 per 1M tokens
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext text, image, video, 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

MiniMax M3 is comparatively weak here — price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M

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 is the newer of the two.

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

LFM3-1.7B

LFM3-1.7B lists part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks among its strengths; MiniMax M3 does not.

Open-weight 428B MoE (~23B active per token) with a 1M-token context

MiniMax M3

Its 1M window holds about 4.1× more than LFM3-1.7B's 256K tokens in a single prompt.

Native multimodal input — text, image and video

MiniMax M3

MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it carries the larger 1M context.

Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5

MiniMax M3

MiniMax M3 lists reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5 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 MiniMax M3's $0.23/$0.96 per 1M tokens.

Largest single-prompt input

MiniMax M3

Its 1M window is about 4.1× larger than LFM3-1.7B's 256K tokens, fitting roughly 1,573 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 MiniMax M3, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ MiniMax M3

Larger 1M 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 open-weight 428b moe (~23b active per token) with a 1m-token context

→ MiniMax M3

That is its strongest area.

An enterprise with regional data-residency rules

→ LFM3-1.7B or MiniMax M3

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

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.

MiniMax M3: where it fits

MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released May 31, 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.

Its trade-offs: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.23 in / $0.96 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." LFM3-1.7B (US) and MiniMax M3 (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. LFM3-1.7B is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both LFM3-1.7B and MiniMax M3 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 MiniMax M3 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 MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, LFM3-1.7B or MiniMax M3?

LFM3-1.7B is cheaper — Open weight (self-host / free) vs $0.23/$0.96 per 1M tokens.

Which has the bigger context window?

MiniMax M3 — 1M vs 256K tokens, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both LFM3-1.7B and MiniMax M3 together?

Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, MiniMax M3 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 MiniMax M3?

LFM3-1.7B — released October 27, 2026, about 5 months after MiniMax M3.

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.