Hunyuan Hy3 vs Muse Spark Open
Tencent · China | Meta · 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 Muse Spark Open for meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input or artificial analysis intelligence index of 39, first among us open-weight models.
Hunyuan Hy3 (Tencent, China) and Muse Spark Open (Meta, 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. Muse Spark Open is meta's October 15, 2026 open-weight Muse Spark - an 816B/42B-active multimodal MoE with a 1M context under a gated Muse Model License. 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: Muse Spark Open holds 3.9× more — 1M tokens (~1,500 pages) vs 256K (~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: Muse Spark Open is the newer model by about 3 months (released October 15, 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 | Muse Spark Open |
|---|---|---|
| Provider | Tencent (China) | Meta (US) |
| Released | July 6, 2026 | October 15, 2026 |
| Context window | 256K (~384 pages) | 1M tokens (~1,500 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, image, video, audio, code |
| 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
Muse Spark Open is comparatively weak here — muse Model License: attribution required and a revenue threshold (reported as about $20M; check the license text) before a separate license is needed
Runs a 295B model at the cost of a 21B — only 21B parameters active per token
Hunyuan Hy3
Muse Spark Open is comparatively weak here — weakest of the three Muse models (Wonder Full scores 56, Wonder 52)
Clean, unrestricted Apache-2.0 license with no geographic carve-out
Hunyuan Hy3
Hunyuan Hy3 lists clean, unrestricted Apache-2.0 license with no geographic carve-out among its strengths; Muse Spark Open does not.
Meta's first open-weight Muse model - 816B-parameter MoE with 42B active, accepting text, image, video and audio input
Muse Spark Open
Meta's October 15, 2026 open-weight Muse Spark - an 816B/42B-active multimodal MoE with a 1M context under a gated Muse Model License — and it carries the larger 1M tokens context.
Artificial Analysis Intelligence Index of 39, first among US open-weight models
Muse Spark Open
Meta's October 15, 2026 open-weight Muse Spark - an 816B/42B-active multimodal MoE with a 1M context under a gated Muse Model License — and it is the newer of the two.
About 143 output tokens/sec; up to 128K output tokens
Muse Spark Open
Its 1M tokens window holds about 3.9× more than Hunyuan Hy3's 256K in a single prompt.
Largest single-prompt input
Muse Spark Open
Its 1M tokens window is about 3.9× larger than Hunyuan Hy3's 256K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Muse Spark Open
Larger 1M tokens window fits more in one prompt.
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 meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input
→ Muse Spark Open
That is its strongest area.
An enterprise with regional data-residency rules
→ Muse Spark Open 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.
Muse Spark Open: where it fits
Meta's October 15, 2026 open-weight Muse Spark - an 816B/42B-active multimodal MoE with a 1M context under a gated Muse Model License. Released October 15, 2026 by Meta, it is built for meta's first open-weight Muse model - 816B-parameter MoE with 42B active, accepting text, image, video and audio input, artificial Analysis Intelligence Index of 39, first among US open-weight models, and about 143 output tokens/sec; up to 128K output tokens.
Its trade-offs: weakest of the three Muse models (Wonder Full scores 56, Wonder 52), muse Model License: attribution required and a revenue threshold (reported as about $20M; check the license text) before a separate license is needed, and needs about 1 TB of memory at 4-bit; no first-party per-token price confirmed. 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 Muse Spark Open (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 Muse Spark Open 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 Muse Spark Open 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 Muse Spark Open leans toward meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Hunyuan Hy3 or Muse Spark Open?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Muse Spark Open — 1M tokens vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Hunyuan Hy3 and Muse Spark Open together?
Yes — a multi-model platform like LumiChats gives you Hunyuan Hy3, Muse Spark Open 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 Muse Spark Open?
Muse Spark Open — released October 15, 2026, about 3 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.