LFM3-1.7B vs Qwen3 235B A22B (2507)
Liquid AI · US | Alibaba · 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 Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench).
LFM3-1.7B (Liquid AI, US) and Qwen3 235B A22B (2507) (Alibaba, 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. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
- ▸Context window: 256K tokens vs 256K — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
- ▸Recency: LFM3-1.7B is the newer model by about 15 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 | Qwen3 235B A22B (2507) |
|---|---|---|
| Provider | Liquid AI (US) | Alibaba (China) |
| Released | October 27, 2026 | July 21, 2025 |
| Context window | 256K tokens (~384 pages) | 256K (~393 pages) |
| Price (in/out) | Open weight (self-host / free) | Open weight (self-host / free) |
| 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
Qwen3 235B A22B (2507) is comparatively weak here — its 235B weights need roughly 438GB in BF16, far beyond consumer hardware
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
Qwen3 235B A22B (2507) is comparatively weak here — text-only with no vision, and the absence of a thinking mode caps its hardest reasoning
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; LFM3-1.7B does not.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; LFM3-1.7B does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B (2507)
Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; LFM3-1.7B does not.
Which should you pick?
Someone analysing very long documents or codebases
→ Qwen3 235B A22B (2507)
Larger 256K 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 deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B (2507)
That is its strongest area.
An enterprise with regional data-residency rules
→ LFM3-1.7B or Qwen3 235B A22B (2507)
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.
Qwen3 235B A22B (2507): where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. 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." LFM3-1.7B (US) and Qwen3 235B A22B (2507) (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. 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 Qwen3 235B A22B (2507) 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 Qwen3 235B A22B (2507) 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 Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LFM3-1.7B or Qwen3 235B A22B (2507)?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Effectively neither — 256K tokens vs 256K is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both LFM3-1.7B and Qwen3 235B A22B (2507) together?
Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, Qwen3 235B A22B (2507) 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 Qwen3 235B A22B (2507)?
LFM3-1.7B — released October 27, 2026, about 15 months after Qwen3 235B A22B (2507).
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.