Muse Spark Open vs Qwen3.6 35B A3B

Meta · US  |  Alibaba · China · Updated June 2026

Quick verdict

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. Pick Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu.

Muse Spark Open (Meta, US) and Qwen3.6 35B A3B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.

Key differences at a glance

Side-by-side specs

SpecMuse Spark OpenQwen3.6 35B A3B
ProviderMeta (US) Alibaba (China)
ReleasedOctober 15, 2026 April 16, 2026
Context window1M tokens (~1,500 pages) 256K (~393 pages)
Price (in/out)Open weight (self-host / free) Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, video, audio, code text, image, code
SWE-Bench VerifiedNot published 73.4%
MRCR v2 @ 1MNot published Not published

Who wins what

Meta's first open-weight Muse model - 816B-parameter MoE with 42B active, accepting text, image, video and audio input

Muse Spark Open

Qwen3.6 35B A3B is comparatively weak here — loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters

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 carries the larger 1M tokens context.

About 143 output tokens/sec; up to 128K output tokens

Muse Spark Open

Its 1M tokens window holds about 3.8× more than Qwen3.6 35B A3B's 256K in a single prompt.

Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost

Qwen3.6 35B A3B

Muse Spark Open is comparatively weak here — needs about 1 TB of memory at 4-bit; no first-party per-token price confirmed

Runs at roughly 120 tokens per second on a single 24GB consumer GPU

Qwen3.6 35B A3B

Qwen3.6 35B A3B lists runs at roughly 120 tokens per second on a single 24GB consumer GPU among its strengths; Muse Spark Open does not.

Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN

Qwen3.6 35B A3B

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

Largest single-prompt input

Muse Spark Open

Its 1M tokens window is about 3.8× larger than Qwen3.6 35B A3B'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 meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input

→ Muse Spark Open

It is specifically built for that.

Anyone whose priority is extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost

→ Qwen3.6 35B A3B

That is its strongest area.

An enterprise with regional data-residency rules

→ Muse Spark Open or Qwen3.6 35B A3B

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

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 are real: 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.

Qwen3.6 35B A3B: where it fits

A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.

Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. 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." Muse Spark Open (US) and Qwen3.6 35B A3B (China) 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 Muse Spark Open and Qwen3.6 35B A3B 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 pricing

Frequently asked questions

Is Muse Spark Open or Qwen3.6 35B A3B better for coding?

Public SWE-Bench figures are not available for Muse Spark Open, so the honest test is your own repository — run an identical real bug through both. By design, 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 while Qwen3.6 35B A3B leans toward extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Muse Spark Open or Qwen3.6 35B A3B?

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 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Muse Spark Open and Qwen3.6 35B A3B together?

Yes — a multi-model platform like LumiChats gives you Muse Spark Open, Qwen3.6 35B A3B 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, Muse Spark Open or Qwen3.6 35B A3B?

Muse Spark Open — released October 15, 2026, about 6 months after Qwen3.6 35B A3B.

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.