LFM3-1.7B vs MiMo-V2.5-Pro
Liquid AI · US | Xiaomi · 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 MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro) or long-horizon autonomous tasks (1,000+ tool calls). On a tight budget at scale, LFM3-1.7B is the value pick.
LFM3-1.7B (Liquid AI, US) and MiMo-V2.5-Pro (Xiaomi, 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. MiMo-V2.5-Pro is xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: MiMo-V2.5-Pro holds 3.9× more — 1M (~1,500 pages) vs 256K tokens (~384 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 6 months (released October 27, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | LFM3-1.7B | MiMo-V2.5-Pro |
|---|---|---|
| Provider | Liquid AI (US) | Xiaomi (China) |
| Released | October 27, 2026 | April 22, 2026 |
| Context window | 256K tokens (~384 pages) | 1M (~1,500 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.435/$0.87 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text | text, image, video, 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
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.
Liquid says it can power agentic applications that used to need 9B parameters
LFM3-1.7B
LFM3-1.7B lists liquid says it can power agentic applications that used to need 9B parameters among its strengths; MiMo-V2.5-Pro does not.
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; MiMo-V2.5-Pro does not.
Complex software engineering (top-ranked on SWE-bench Pro)
MiMo-V2.5-Pro
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
Long-horizon autonomous tasks (1,000+ tool calls)
MiMo-V2.5-Pro
Its 1M window holds about 3.9× more than LFM3-1.7B's 256K tokens in a single prompt.
Strong on GDPVal and ClawEval
MiMo-V2.5-Pro
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost — and it carries the larger 1M context.
Lowest cost at scale
LFM3-1.7B
Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.5-Pro's $0.435/$0.87 per 1M tokens.
Largest single-prompt input
MiMo-V2.5-Pro
Its 1M window is about 3.9× larger than LFM3-1.7B's 256K tokens, fitting roughly 1,500 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 MiMo-V2.5-Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiMo-V2.5-Pro
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 complex software engineering (top-ranked on swe-bench pro)
→ MiMo-V2.5-Pro
That is its strongest area.
An enterprise with regional data-residency rules
→ LFM3-1.7B or MiMo-V2.5-Pro
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.
MiMo-V2.5-Pro: where it fits
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. Released April 22, 2026 by Xiaomi, it is built for complex software engineering (top-ranked on SWE-bench Pro), long-horizon autonomous tasks (1,000+ tool calls), strong on GDPVal and ClawEval, and agent-framework integration.
Its trade-offs: benchmark rankings are largely vendor-stated, and limited Western adoption and tooling. At $0.435 in / $0.87 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 MiMo-V2.5-Pro (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 MiMo-V2.5-Pro 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 MiMo-V2.5-Pro 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 MiMo-V2.5-Pro leans toward complex software engineering (top-ranked on swe-bench pro), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LFM3-1.7B or MiMo-V2.5-Pro?
LFM3-1.7B is cheaper — Open weight (self-host / free) vs $0.435/$0.87 per 1M tokens.
Which has the bigger context window?
MiMo-V2.5-Pro — 1M vs 256K tokens, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both LFM3-1.7B and MiMo-V2.5-Pro together?
Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, MiMo-V2.5-Pro 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 MiMo-V2.5-Pro?
LFM3-1.7B — released October 27, 2026, about 6 months after MiMo-V2.5-Pro.
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.