Hunyuan Hy3 vs LFM3-1.7B
Tencent · China | Liquid AI · US · Updated June 2026
Quick verdict
Pick Hunyuan Hy3 for frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost or runs a 295b model at the cost of a 21b — only 21b parameters active per token. 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.
Hunyuan Hy3 (Tencent, 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. Hunyuan Hy3 is a 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. 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. The breakdown below works through their capabilities and ideal use cases so you can match one to your task.
Key differences at a glance
- ▸Context window: both advertise 256K (~384 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: LFM3-1.7B is the newer model by about 4 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 | Hunyuan Hy3 | LFM3-1.7B |
|---|---|---|
| Provider | Tencent (China) | Liquid AI (US) |
| Released | July 6, 2026 | October 27, 2026 |
| Context window | 256K (~384 pages) | 256K tokens (~384 pages) |
| Price (in/out) | Open weight (self-host / free) | 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
Frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost
Hunyuan Hy3
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
Runs a 295B model at the cost of a 21B — only 21B parameters active per token
Hunyuan Hy3
LFM3-1.7B is comparatively weak here — no published per-token API price found
Clean, unrestricted Apache-2.0 license with no geographic carve-out
Hunyuan Hy3
LFM3-1.7B is comparatively weak here — lFM Open License v1.0 covers commercial use only below $10M annual revenue; larger firms need a paid license
Very small open-weight text model - about 1.73B parameters with a 256K-token context for on-device use
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.
Liquid says it can power agentic applications that used to need 9B parameters
LFM3-1.7B
LFM3-1.7B lists liquid says it can power agentic applications that used to need 9B parameters among its strengths; Hunyuan Hy3 does not.
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; Hunyuan Hy3 does not.
Which should you pick?
Anyone whose priority is frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost
→ Hunyuan Hy3
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 Hunyuan Hy3
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Hunyuan Hy3: where it fits
A 295B Apache-2.0 open MoE that reaches frontier reasoning quality while running at roughly 21B active-parameter cost. Released July 6, 2026 by Tencent, it is built for frontier-level reported reasoning and science (GPQA Diamond 90.4) at low active-parameter cost, runs a 295B model at the cost of a 21B — only 21B parameters active per token, clean, unrestricted Apache-2.0 license with no geographic carve-out, and broad day-one ecosystem support plus an FP8 checkpoint.
Its trade-offs are real: benchmarks are largely self-reported, and the ultra-low hosted pricing is a limited promotion, and the hosted API is China-jurisdiction, and self-hosting a 295B MoE still needs serious hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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." Hunyuan Hy3 (China) and LFM3-1.7B (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. 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 Hunyuan Hy3 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 Hunyuan Hy3 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, Hunyuan Hy3 leans toward frontier-level reported reasoning and science (gpqa diamond 90.4) at low active-parameter cost 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, Hunyuan Hy3 or LFM3-1.7B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 256K (~384 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Hunyuan Hy3 and LFM3-1.7B together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy3, 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, Hunyuan Hy3 or LFM3-1.7B?
LFM3-1.7B — released October 27, 2026, about 4 months after Hunyuan Hy3.
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.