Gemini 2.5 Pro vs LFM3-1.7B
Google · US | Liquid AI · US · Updated June 2026
Quick verdict
Pick Gemini 2.5 Pro for 1m context via api or strong multimodal reasoning. 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. Choose LFM3-1.7B if you need self-hosting or data privacy; Gemini 2.5 Pro if you want a managed API.
Gemini 2.5 Pro (Google) and LFM3-1.7B (Liquid AI) are two of the models people most often weigh against each other in 2026. Gemini 2.5 Pro is google's previous-gen 2M flagship — still a strong long-context multimodal option. 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, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Cost model: LFM3-1.7B ships open weights you can self-host (hardware cost only, no per-token fee), while Gemini 2.5 Pro is API-metered at $1.25/$10 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
- ▸Context window: Gemini 2.5 Pro holds 3.9× more — 1M (~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 17 months (released October 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Gemini 2.5 Pro | LFM3-1.7B |
|---|---|---|
| Provider | Google (US) | Liquid AI (US) |
| Released | June 2025 | October 27, 2026 |
| Context window | 1M (~1,500 pages) | 256K tokens (~384 pages) |
| Price (in/out) | $1.25/$10 per 1M tokens | Open weight (self-host / free) |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, audio, video, code | text |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
1M context via API
Gemini 2.5 Pro
Its 1M window holds about 3.9× more than LFM3-1.7B's 256K tokens in a single prompt.
Strong multimodal reasoning
Gemini 2.5 Pro
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
Science and maths benchmarks
Gemini 2.5 Pro
Google's previous-gen 2M flagship — still a strong long-context multimodal option — and it carries the larger 1M context.
Very small open-weight text model - about 1.73B parameters with a 256K-token context for on-device use
LFM3-1.7B
Open weights make this possible at all — Gemini 2.5 Pro is API-only, so it cannot leave the vendor's servers.
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 its weights are open while Gemini 2.5 Pro is API-only.
Part of a six-model LFM3 family covering text, vision, audio, omni and decision tasks
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.
Lowest cost at scale
LFM3-1.7B
Its weights are open, so at volume you pay for your own hardware instead of Gemini 2.5 Pro's $1.25/$10 per 1M tokens.
Largest single-prompt input
Gemini 2.5 Pro
Its 1M 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 Gemini 2.5 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 2.5 Pro
Larger 1M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ LFM3-1.7B
Open weights let you run it on your own hardware; Gemini 2.5 Pro is API-only.
Anyone whose priority is 1m context via api
→ Gemini 2.5 Pro
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.
Gemini 2.5 Pro: where it fits
Google's previous-gen 2M flagship — still a strong long-context multimodal option. Released June 2025 by Google, it is built for 1M context via API, strong multimodal reasoning, science and maths benchmarks, and whole-book and video analysis.
Its trade-offs are real: superseded by 3.x for newest features, and recall degrades on very long inputs. At $1.25 in / $10 out per million tokens, it sits in the mid 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
The defining split here is open vs. closed. LFM3-1.7B gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 2.5 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Gemini 2.5 Pro 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 Gemini 2.5 Pro 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, Gemini 2.5 Pro leans toward 1m context via api 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, Gemini 2.5 Pro or LFM3-1.7B?
LFM3-1.7B is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 2.5 Pro is API-metered at $1.25/$10 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Gemini 2.5 Pro — 1M 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 Gemini 2.5 Pro and LFM3-1.7B together?
Yes — a multi-model platform like LumiChats gives you Gemini 2.5 Pro, 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, Gemini 2.5 Pro or LFM3-1.7B?
LFM3-1.7B — released October 27, 2026, about 17 months after Gemini 2.5 Pro.
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.