LFM3-1.7B vs Mistral NeMo
Liquid AI · US | Mistral · France · 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 Mistral NeMo for multilingual understanding across 11+ languages or runs on a single gpu with fp8 quantization-aware training. On a tight budget at scale, LFM3-1.7B is the value pick.
LFM3-1.7B (Liquid AI, US) and Mistral NeMo (Mistral, France) 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. Mistral NeMo is a 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. 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 (~197 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 28 months (released October 27, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a US-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | LFM3-1.7B | Mistral NeMo |
|---|---|---|
| Provider | Liquid AI (US) | Mistral (France) |
| Released | October 27, 2026 | July 18, 2024 |
| Context window | 256K tokens (~384 pages) | 128K (~197 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.02/$0.03 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text | text |
| 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
Its 256K tokens window holds about 2× more than Mistral NeMo'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
Mistral NeMo is comparatively weak here — 12B scale trails larger frontier models on complex reasoning and coding
Multilingual understanding across 11+ languages
Mistral NeMo
Mistral NeMo lists multilingual understanding across 11+ languages among its strengths; LFM3-1.7B does not.
Runs on a single GPU with FP8 quantization-aware training
Mistral NeMo
Mistral NeMo lists runs on a single GPU with FP8 quantization-aware training among its strengths; LFM3-1.7B does not.
128K-token context for long documents
Mistral NeMo
LFM3-1.7B is comparatively weak here — no published per-token API price found
Lowest cost at scale
LFM3-1.7B
Its weights are open, so at volume you pay for your own hardware instead of Mistral NeMo's $0.02/$0.03 per 1M tokens.
Largest single-prompt input
LFM3-1.7B
Its 256K tokens window is about 2× larger than Mistral NeMo'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 Mistral NeMo, 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 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 multilingual understanding across 11+ languages
→ Mistral NeMo
That is its strongest area.
An enterprise with regional data-residency rules
→ LFM3-1.7B or Mistral NeMo
Origin (US vs France) 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.
Mistral NeMo: where it fits
A 12B Apache-2.0 open-weight model co-developed by Mistral and NVIDIA, pairing a 128K context and strong multilingual performance with efficiency that fits on a single GPU. Released July 18, 2024 by Mistral, it is built for multilingual understanding across 11+ languages, runs on a single GPU with FP8 quantization-aware training, 128K-token context for long documents, and function calling and structured tool use.
Its trade-offs: discontinued - deprecated May 22, 2026 and fully retired July 31, 2026; replaced by Ministral 3 8B, 12B scale trails larger frontier models on complex reasoning and coding, and text-only; no vision or audio input. At $0.02 in / $0.03 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 Mistral NeMo (France) 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 Mistral NeMo 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 Mistral NeMo 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 Mistral NeMo leans toward multilingual understanding across 11+ languages, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LFM3-1.7B or Mistral NeMo?
LFM3-1.7B is cheaper — Open weight (self-host / free) vs $0.02/$0.03 per 1M tokens.
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 LFM3-1.7B and Mistral NeMo together?
Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, Mistral NeMo 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 Mistral NeMo?
LFM3-1.7B — released October 27, 2026, about 28 months after Mistral NeMo.
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.