Gemma 4 vs Muse Wonder 1.0

Google · US  |  Meta · US · Updated June 2026

Quick verdict

Pick Gemma 4 for self-hosted, data-private deployment or running locally or on edge devices. Pick Muse Wonder 1.0 for meta superintelligence labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused muse spark family or artificial analysis intelligence index score of 52 (a gated 'wonder full' subscription variant scores 56, tied with claude sonnet 5.5, behind only opus 5.5 at 58). Choose Gemma 4 if you need self-hosting or data privacy; Muse Wonder 1.0 if you want a managed API.

Gemma 4 (Google) and Muse Wonder 1.0 (Meta) are two of the models people most often weigh against each other in 2026. Gemma 4 is google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Muse Wonder 1.0 is meta Superintelligence Labs' October 12, 2026 debut frontier model — distinct from Muse Spark, scoring 52 on the independent Artificial Analysis Index with a 1M-token, four-modality context window. 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

Side-by-side specs

SpecGemma 4Muse Wonder 1.0
ProviderGoogle (US) Meta (US)
ReleasedApril 2, 2026 October 12, 2026
Context window256K (~384 pages) 1M tokens (~1,573 pages)
Price (in/out)Open weight (self-host / free) $4/$20 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, image, code text, image, video, audio
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Self-hosted, data-private deployment

Gemma 4

Open weights make this possible at all — Muse Wonder 1.0 is API-only, so it cannot leave the vendor's servers.

Running locally or on edge devices

Gemma 4

Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting — and its weights are open while Muse Wonder 1.0 is API-only.

Fine-tuning on your own data

Gemma 4

Gemma 4 lists fine-tuning on your own data among its strengths; Muse Wonder 1.0 does not.

Meta Superintelligence Labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused Muse Spark family

Muse Wonder 1.0

Gemma 4 is comparatively weak here — trails frontier closed models on the hardest tasks

Artificial Analysis Intelligence Index score of 52 (a gated 'Wonder Full' subscription variant scores 56, tied with Claude Sonnet 5.5, behind only Opus 5.5 at 58)

Muse Wonder 1.0

Meta Superintelligence Labs' October 12, 2026 debut frontier model — distinct from Muse Spark, scoring 52 on the independent Artificial Analysis Index with a 1M-token, four-modality context window — and it carries the larger 1M tokens context.

78.8% on DeepSWE v1.1 long-horizon coding, ahead of both Claude Opus 5.5 (74.2%) and GPT-6 Astra (74.1%)

Muse Wonder 1.0

Its 1M tokens window holds about 4.1× more than Gemma 4's 256K in a single prompt.

Lowest cost at scale

Gemma 4

Its weights are open, so at volume you pay for your own hardware instead of Muse Wonder 1.0's $4/$20 per 1M tokens.

Largest single-prompt input

Muse Wonder 1.0

Its 1M tokens window is about 4.1× larger than Gemma 4's 256K, fitting roughly 1,573 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

→ Gemma 4

At Open weight (self-host / free) it undercuts Muse Wonder 1.0, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

→ Muse Wonder 1.0

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

→ Gemma 4

Open weights let you run it on your own hardware; Muse Wonder 1.0 is API-only.

Anyone whose priority is self-hosted, data-private deployment

→ Gemma 4

It is specifically built for that.

Anyone whose priority is meta superintelligence labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused muse spark family

→ Muse Wonder 1.0

That is its strongest area.

Gemma 4: where it fits

Google's open-weight family: Apache 2.0 licensed, multimodal, and sized from edge devices up, for private self-hosting. Released April 2, 2026 by Google, it is built for self-hosted, data-private deployment, running locally or on edge devices, fine-tuning on your own data, and multimodal tasks over a 256K context.

Its trade-offs are real: trails frontier closed models on the hardest tasks, and needs your own hardware to run. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

Muse Wonder 1.0: where it fits

Meta Superintelligence Labs' October 12, 2026 debut frontier model — distinct from Muse Spark, scoring 52 on the independent Artificial Analysis Index with a 1M-token, four-modality context window. Released October 12, 2026 by Meta, it is built for meta Superintelligence Labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused Muse Spark family, artificial Analysis Intelligence Index score of 52 (a gated 'Wonder Full' subscription variant scores 56, tied with Claude Sonnet 5.5, behind only Opus 5.5 at 58), 78.8% on DeepSWE v1.1 long-horizon coding, ahead of both Claude Opus 5.5 (74.2%) and GPT-6 Astra (74.1%), and accepts text, image, video, audio, and PDF input against a 1,048,576-token context window.

Its trade-offs: the stronger 'Wonder Full' variant is gated behind $50/$200-per-month Muse Code subscriptions, with no public API or independently verifiable pricing, meta's full safety report had not been published as of launch, first release in a brand-new model family — no track record yet next to Meta's more established Muse Spark line, and benchmark comparisons against Opus 5.5 and GPT-6 Astra are Meta's own self-reported figures. At $4 in / $20 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

The defining split here is open vs. closed. Gemma 4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Wonder 1.0 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 Gemma 4 and Muse Wonder 1.0 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 or Muse Wonder 1.0 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 leans toward self-hosted, data-private deployment while Muse Wonder 1.0 leans toward meta superintelligence labs' first frontier-class reasoning model — a new line distinct from the agentic-coding-focused muse spark family, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Gemma 4 or Muse Wonder 1.0?

Gemma 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Wonder 1.0 is API-metered at $4/$20 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?

Muse Wonder 1.0 — 1M tokens vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Gemma 4 and Muse Wonder 1.0 together?

Yes — a multi-model platform like LumiChats gives you Gemma 4, Muse Wonder 1.0 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 or Muse Wonder 1.0?

Muse Wonder 1.0 — released October 12, 2026, about 6 months after Gemma 4.

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.