LFM3-1.7B vs Mistral Large 4
Liquid AI · US | Mistral AI · 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 Large 4 for mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder or trained from scratch on 3,800 nvidia grace blackwell gpus in mistral's own european data centers, across 160+ languages. On a tight budget at scale, LFM3-1.7B is the value pick.
LFM3-1.7B (Liquid AI, US) and Mistral Large 4 (Mistral AI, 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 Large 4 is mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Context window: Mistral Large 4 holds 3.9× more — 1M tokens (~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 21 days (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 Large 4 |
|---|---|---|
| Provider | Liquid AI (US) | Mistral AI (France) |
| Released | October 27, 2026 | October 6, 2026 |
| Context window | 256K tokens (~384 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | Open weight (self-host / free) | $0.7/$2.1 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text | text, image, 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
Mistral Large 4 is comparatively weak here — launched as a public preview — Mistral's launch post said the model was still improving
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
LFM3-1.7B lists part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks among its strengths; Mistral Large 4 does not.
Mistral's largest model to date — about 1T total parameters, 49B active per token (52B with embeddings and output layers), plus a 1.6B-parameter vision encoder
Mistral Large 4
Its 1M tokens window holds about 3.9× more than LFM3-1.7B's 256K tokens in a single prompt.
Trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages
Mistral Large 4
Mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10 — and it carries the larger 1M tokens context.
Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning
Mistral Large 4
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
Lowest cost at scale
LFM3-1.7B
Its weights are open, so at volume you pay for your own hardware instead of Mistral Large 4's $0.7/$2.1 per 1M tokens.
Largest single-prompt input
Mistral Large 4
Its 1M tokens 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 Mistral Large 4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Mistral Large 4
Larger 1M 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 mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder
→ Mistral Large 4
That is its strongest area.
An enterprise with regional data-residency rules
→ LFM3-1.7B or Mistral Large 4
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 Large 4: where it fits
Mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10. Released October 6, 2026 by Mistral AI, it is built for mistral's largest model to date — about 1T total parameters, 49B active per token (52B with embeddings and output layers), plus a 1.6B-parameter vision encoder, trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages, hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning, and open weights on Hugging Face since October 29, 2026, with an NVFP4 checkpoint for Blackwell GPUs, making it among the largest open-weight models available.
Its trade-offs: launched as a public preview — Mistral's launch post said the model was still improving, mistral Ecosystem Open-Weight License is a modified MIT license: selling hosted access to third parties needs a separate commercial license, self-hosting a 1T-parameter MoE needs datacenter-class GPUs, and scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark. At $0.7 in / $2.1 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 Large 4 (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 Large 4 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 Large 4 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 Large 4 leans toward mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LFM3-1.7B or Mistral Large 4?
LFM3-1.7B is cheaper — Open weight (self-host / free) vs $0.7/$2.1 per 1M tokens.
Which has the bigger context window?
Mistral Large 4 — 1M tokens 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 Mistral Large 4 together?
Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, Mistral Large 4 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 Large 4?
LFM3-1.7B — released October 27, 2026, about 21 days after Mistral Large 4.
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.