Muse Glimmer vs Muse Spark 1.3

Meta · US  |  Meta · US · Updated June 2026

Quick verdict

Both are Meta models. Muse Spark 1.3 is the newer, generally stronger default; reach for Muse Glimmer when its lower price or a specific cost or latency profile matters more than the latest capabilities.

Muse Glimmer and Muse Spark 1.3 are both Meta models, so the real question is not which lab to trust but which tier fits your workload and budget. Muse Glimmer is meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Muse Spark 1.3 is meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.

Key differences at a glance

Side-by-side specs

SpecMuse GlimmerMuse Spark 1.3
ProviderMeta (US) Meta (US)
ReleasedAugust 10, 2026 September 2, 2026
Context window128K (~197 pages) 1M tokens (~1,500 pages)
Price (in/out)Open weight (self-host / free) $1.25/$4.25 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, image, code text, image, video
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published 98.1%

Who wins what

Runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit

Muse Glimmer

Open weights make this possible at all — Muse Spark 1.3 is API-only, so it cannot leave the vendor's servers.

Open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine

Muse Glimmer

Muse Spark 1.3 is comparatively weak here — not yet open-weight, despite Meta roadmapping a future Muse Spark weights release

Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery

Muse Glimmer

Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models — and its weights are open while Muse Spark 1.3 is API-only.

Agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2

Muse Spark 1.3

Its 1M tokens window holds about 7.6× more than Muse Glimmer's 128K in a single prompt.

DeepSWE v1.1 long-horizon software engineering (75.4)

Muse Spark 1.3

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review — and it carries the larger 1M tokens context.

Near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1)

Muse Spark 1.3

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review — and it is the newer of the two.

Lowest cost at scale

Muse Glimmer

Its weights are open, so at volume you pay for your own hardware instead of Muse Spark 1.3's $1.25/$4.25 per 1M tokens.

Largest single-prompt input

Muse Spark 1.3

Its 1M tokens window is about 7.6× larger than Muse Glimmer's 128K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Muse Glimmer

At Open weight (self-host / free) it undercuts Muse Spark 1.3, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Muse Spark 1.3

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

Muse Glimmer

Open weights let you run it on your own hardware; Muse Spark 1.3 is API-only.

Anyone whose priority is runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit

Muse Glimmer

It is specifically built for that.

Anyone whose priority is agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2

Muse Spark 1.3

That is its strongest area.

Muse Glimmer: where it fits

Meta's open ~30B agentic model - runs tool-using AI agents locally on a single consumer GPU, offline and free, though it trails frontier cloud models. Released August 10, 2026 by Meta, it is built for runs real agentic tasks locally on one consumer GPU - fits 24GB of VRAM in 4-bit, open weights (Apache 2.0), free to self-host and offline-capable - data never leaves your machine, agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery, and 128K context, text and image input, trained on 100+ languages.

Its trade-offs are real: a distilled ~30B model - ceiling below its closed teacher (Muse Spark) and cloud flagships, no independent Artificial Analysis intelligence score published yet, published benchmarks (e.g. SWE-Bench Verified 76.0) are Meta's own, and 4-bit quantization to fit consumer GPUs trades away some accuracy. As an open-weight model, its running cost is your own hardware rather than a per-token fee.

Muse Spark 1.3: where it fits

Meta's September 2, 2026 agentic-coding model update, cutting tool calls and token usage versus Muse Spark 1.2 while its highest-reasoning mode remains gated for safety review. Released September 2, 2026 by Meta, it is built for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than Muse Spark 1.2, deepSWE v1.1 long-horizon software engineering (75.4), near-perfect long-context retrieval (MRCR v2 512K-1M: 98.1), and ranks third overall on the Artificial Analysis Intelligence Index (score 61, xhigh variant; the limited-preview max variant scores 62) - behind only Claude Fable 5.1 and Claude Opus 5.

Its trade-offs: strongest 'max' reasoning configuration still gated pending additional safety testing, not yet open-weight, despite Meta roadmapping a future Muse Spark weights release, and no official SWE-bench Verified score published. At $1.25 in / $4.25 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

Because Muse Glimmer and Muse Spark 1.3 come from the same lab (Meta), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Muse Spark 1.3 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Muse Spark 1.3 and drop down only with a concrete reason.

Want both Muse Glimmer and Muse Spark 1.3 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 Glimmer or Muse Spark 1.3 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, Muse Glimmer leans toward runs real agentic tasks locally on one consumer gpu - fits 24gb of vram in 4-bit while Muse Spark 1.3 leans toward agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Muse Glimmer or Muse Spark 1.3?

Muse Glimmer is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Spark 1.3 is API-metered at $1.25/$4.25 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 Spark 1.3 — 1M tokens vs 128K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.

Should I upgrade from Muse Glimmer to Muse Spark 1.3?

Since both are Meta models, the newer one (Muse Spark 1.3) is usually the better default unless you need a specific cost or latency profile from the other.

Which is newer, Muse Glimmer or Muse Spark 1.3?

Muse Spark 1.3 — released September 2, 2026, about 23 days after Muse Glimmer.

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.