DeepSeek R1 vs LFM3-1.7B
DeepSeek · China | Liquid AI · US · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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.
DeepSeek R1 (DeepSeek, 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. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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
- ▸Context window: LFM3-1.7B holds 2× more — 256K tokens (~384 pages) vs 128K (~192 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.
- ▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | DeepSeek R1 | LFM3-1.7B |
|---|---|---|
| Provider | DeepSeek (China) | Liquid AI (US) |
| Released | January 2025 | October 27, 2026 |
| Context window | 128K (~192 pages) | 256K tokens (~384 pages) |
| Price (in/out) | $0.55/$2.19 per 1M tokens | Open weight (self-host / free) |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, code | text |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Open-weight reasoning model
DeepSeek R1
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
Transparent chain-of-thought
DeepSeek R1
DeepSeek R1 lists transparent chain-of-thought among its strengths; LFM3-1.7B does not.
Low cost
DeepSeek R1
DeepSeek R1 lists low cost 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
Its 256K tokens window holds about 2× more than DeepSeek R1's 128K 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
DeepSeek R1 is comparatively weak here — discontinued - no longer offered as a standalone model via DeepSeek's API as of mid-2026; the legacy alias that used to point to it stopped working July 24, 2026
Lowest cost at scale
LFM3-1.7B
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek R1's $0.55/$2.19 per 1M tokens.
Largest single-prompt input
LFM3-1.7B
Its 256K tokens window is about 2× larger than DeepSeek R1's 128K, 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 DeepSeek R1, 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 open-weight reasoning model
→ DeepSeek R1
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 DeepSeek R1
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: discontinued - no longer offered as a standalone model via DeepSeek's API as of mid-2026; the legacy alias that used to point to it stopped working July 24, 2026, older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget 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." DeepSeek R1 (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 DeepSeek R1 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 pricingFrequently asked questions
Is DeepSeek R1 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, DeepSeek R1 leans toward open-weight reasoning model 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, DeepSeek R1 or LFM3-1.7B?
LFM3-1.7B is cheaper — $0.55/$2.19 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
LFM3-1.7B — 256K tokens vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and LFM3-1.7B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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, DeepSeek R1 or LFM3-1.7B?
LFM3-1.7B — released October 27, 2026, about 22 months after DeepSeek R1.
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.