Kimi K3 vs LFM3-1.7B

Moonshot AI · China  |  Liquid AI · US · Updated June 2026

Quick verdict

Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). 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. On a tight budget at scale, LFM3-1.7B is the value pick.

Kimi K3 (Moonshot AI, China) and LFM3-1.7B (Liquid AI, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Kimi K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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. 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

SpecKimi K3LFM3-1.7B
ProviderMoonshot AI (China) Liquid AI (US)
ReleasedJuly 27, 2026 October 27, 2026
Context window1M (~1,573 pages) 256K tokens (~384 pages)
Price (in/out)$3/$15 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, video, code text
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Largest open-weight model at release — 2.8T sparse MoE, self-hostable

Kimi K3

Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.

1M-token context with native vision (text, image and video)

Kimi K3

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

Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness

Kimi K3

Kimi K3 lists vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness among its strengths; LFM3-1.7B does not.

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

LFM3-1.7B

Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not

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

Kimi K3 is comparatively weak here — image input but no audio or video

Lowest cost at scale

LFM3-1.7B

Its weights are open, so at volume you pay for your own hardware instead of Kimi K3's $3/$15 per 1M tokens.

Largest single-prompt input

Kimi K3

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 Kimi K3, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Kimi K3

Larger 1M window fits more in one prompt.

Anyone whose priority is largest open-weight model at release — 2.8t sparse moe, self-hostable

→ Kimi K3

It is specifically built for that.

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

That is its strongest area.

An enterprise with regional data-residency rules

→ LFM3-1.7B or Kimi K3

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

Kimi K3: where it fits

Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.

Its trade-offs are real: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.

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: 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.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." Kimi K3 (China) and LFM3-1.7B (US) 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 Kimi K3 and LFM3-1.7B 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 Kimi K3 or LFM3-1.7B 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, Kimi K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable while LFM3-1.7B leans toward very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Kimi K3 or LFM3-1.7B?

LFM3-1.7B is cheaper — $3/$15 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

Kimi K3 — 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 Kimi K3 and LFM3-1.7B together?

Yes — a multi-model platform like LumiChats gives you Kimi K3, LFM3-1.7B 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, Kimi K3 or LFM3-1.7B?

LFM3-1.7B — released October 27, 2026, about 3 months after Kimi K3.

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.