Gemma 4 26B A4B vs Muse Spark Open

Google · US  |  Meta · US · Updated June 2026

Quick verdict

Pick Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total) or near-31b-dense quality at a fraction of the compute and memory-bandwidth cost. 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. On a tight budget at scale, Muse Spark Open is the value pick.

Gemma 4 26B A4B (Google) and Muse Spark Open (Meta) are two of the models people most often weigh against each other in 2026. Gemma 4 26B A4B is an Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference 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. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecGemma 4 26B A4BMuse Spark Open
ProviderGoogle (US) Meta (US)
ReleasedApril 2, 2026 October 15, 2026
Context window256K (~393 pages) 1M tokens (~1,500 pages)
Price (in/out)$0.12/$0.37 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, video, code text, image, video, audio, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Fast, cheap inference from a sparse MoE (3.8B active of 25.2B total)

Gemma 4 26B A4B

Gemma 4 26B A4B lists fast, cheap inference from a sparse MoE (3.8B active of 25.2B total) among its strengths; Muse Spark Open does not.

Near-31B-dense quality at a fraction of the compute and memory-bandwidth cost

Gemma 4 26B A4B

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

Strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6)

Gemma 4 26B A4B

Gemma 4 26B A4B lists strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6) 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

Gemma 4 26B A4B is comparatively weak here — all 25.2B parameters must be loaded into memory even though only 3.8B are active per token

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 Gemma 4 26B A4B's 256K in a single prompt.

Lowest cost at scale

Muse Spark Open

Its weights are open, so at volume you pay for your own hardware instead of Gemma 4 26B A4B's $0.12/$0.37 per 1M tokens.

Largest single-prompt input

Muse Spark Open

Its 1M tokens window is about 3.8× larger than Gemma 4 26B A4B's 256K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Muse Spark Open

At Open weight (self-host / free) it undercuts Gemma 4 26B A4B, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Muse Spark Open

Larger 1M tokens window fits more in one prompt.

Anyone whose priority is fast, cheap inference from a sparse moe (3.8b active of 25.2b total)

→ Gemma 4 26B A4B

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.

Gemma 4 26B A4B: where it fits

An Apache-2.0 open MoE with 25.2B total but only 3.8B active parameters, delivering near-31B-dense quality at a fraction of the inference cost. Released April 2, 2026 by Google, it is built for fast, cheap inference from a sparse MoE (3.8B active of 25.2B total), near-31B-dense quality at a fraction of the compute and memory-bandwidth cost, strong reasoning and coding (88.3% AIME 2026 no-tools, 77.1% LiveCodeBench v6), and multimodal input (text/image, plus video processed as frames up to 60s) with native function calling.

Its trade-offs are real: all 25.2B parameters must be loaded into memory even though only 3.8B are active per token, and 256K context trails 1M-token frontier rivals, and this variant has no audio input (audio is E2B/E4B/12B only). At $0.12 in / $0.37 out per million tokens, it sits in the budget price band.

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

Gemma 4 26B A4B and Muse Spark Open overlap enough that the right pick depends on your specific job. Muse Spark Open costs less per token; Muse Spark Open holds the larger context; and each leads in its own area — Gemma 4 26B A4B for fast, cheap inference from a sparse moe (3.8b active of 25.2b total), Muse Spark Open for meta's first open-weight muse model - 816b-parameter moe with 42b active, accepting text, image, video and audio input. Rather than crowning one, run the same hard task through both once and let the results decide.

Want both Gemma 4 26B A4B 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 pricing

Frequently asked questions

Is Gemma 4 26B A4B 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, Gemma 4 26B A4B leans toward fast, cheap inference from a sparse moe (3.8b active of 25.2b total) 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, Gemma 4 26B A4B or Muse Spark Open?

Muse Spark Open is cheaper — $0.12/$0.37 per 1M tokens vs Open weight (self-host / free).

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 Gemma 4 26B A4B and Muse Spark Open together?

Yes — a multi-model platform like LumiChats gives you Gemma 4 26B A4B, 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, Gemma 4 26B A4B or Muse Spark Open?

Muse Spark Open — released October 15, 2026, about 7 months after Gemma 4 26B A4B.

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.