LFM3-1.7B vs OLMo 3 32B Think
Liquid AI · US | Allen Institute for AI · US · 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 OLMo 3 32B Think for the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights or a genuine 32b reasoning ('think') model that generates explicit chain-of-thought.
LFM3-1.7B (Liquid AI) and OLMo 3 32B Think (Allen Institute for AI) are two of the models people most often weigh against each other in 2026. 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. OLMo 3 32B Think is allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. 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: LFM3-1.7B holds 3.9× more — 256K tokens (~384 pages) vs 65K (~98 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 11 months (released October 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | LFM3-1.7B | OLMo 3 32B Think |
|---|---|---|
| Provider | Liquid AI (US) | Allen Institute for AI (US) |
| Released | October 27, 2026 | November 20, 2025 |
| Context window | 256K tokens (~384 pages) | 65K (~98 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
Its 256K tokens window holds about 3.9× more than OLMo 3 32B Think's 65K 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
OLMo 3 32B Think is comparatively weak here — a 65K context window is modest next to million-token frontier models
The most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights
OLMo 3 32B Think
LFM3-1.7B is comparatively weak here — text-only; vision and audio need other LFM3 checkpoints
A genuine 32B reasoning ('Think') model that generates explicit chain-of-thought
OLMo 3 32B Think
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
Fully open under Apache 2.0 - free to self-host
OLMo 3 32B Think
OLMo 3 32B Think lists fully open under Apache 2.0 - free to self-host among its strengths; LFM3-1.7B does not.
Largest single-prompt input
LFM3-1.7B
Its 256K tokens window is about 3.9× larger than OLMo 3 32B Think's 65K, fitting roughly 384 pages in one prompt.
Which should you pick?
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 the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights
→ OLMo 3 32B Think
That is its strongest area.
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.
OLMo 3 32B Think: where it fits
Allen Institute for AI's fully-open 32B reasoning model - the rare release that ships the complete training data and process, not just the weights. Released November 20, 2025 by Allen Institute for AI, it is built for the most fully transparent open release available - Ai2 publishes the complete weights, full Dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/RL code, not just final weights, a genuine 32B reasoning ('Think') model that generates explicit chain-of-thought, fully open under Apache 2.0 - free to self-host, and built specifically to let researchers reproduce and audit exactly how the model was trained.
Its trade-offs: a 65K context window is modest next to million-token frontier models, prioritizes full openness and reproducibility over topping raw capability leaderboards, and 32B scale trails much larger frontier and open MoE models on general benchmarks. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
LFM3-1.7B and OLMo 3 32B Think overlap enough that the right pick depends on your specific job. LFM3-1.7B holds the larger context; and each leads in its own area — LFM3-1.7B for very small open-weight text model - about 1.73b parameters with a 256k-token context for on-device use, OLMo 3 32B Think for the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both LFM3-1.7B and OLMo 3 32B Think 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 OLMo 3 32B Think 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 OLMo 3 32B Think leans toward the most fully transparent open release available - ai2 publishes the complete weights, full dolma training dataset, every training checkpoint, training logs, and pretraining/instruction-tuning/rl code, not just final weights, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LFM3-1.7B or OLMo 3 32B Think?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
LFM3-1.7B — 256K tokens vs 65K, 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 OLMo 3 32B Think together?
Yes — a multi-model platform like LumiChats gives you LFM3-1.7B, OLMo 3 32B Think 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 OLMo 3 32B Think?
LFM3-1.7B — released October 27, 2026, about 11 months after OLMo 3 32B Think.
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.