Both are Meta models. Muse Glimmer is the newer, generally stronger default; reach for Muse Spark 1.1 when a specific cost or latency profile matters more than the latest capabilities.
Muse Glimmer and Muse Spark 1.1 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 1.1 is meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Cost model: Muse Glimmer ships open weights you can self-host (hardware cost only, no per-token fee), while Muse Spark 1.1 is API-metered at $1.25/$4.25 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Muse Spark 1.1 holds 8× more — 1M (~1,573 pages) vs 128K (~197 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 Glimmer is the newer model by about 32 days (released August 10, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Muse Glimmer
Muse Spark 1.1
Provider
Meta (US)
Meta (US)
Released
August 10, 2026
July 9, 2026
Context window
128K (~197 pages)
1M (~1,573 pages)
Price (in/out)
Open weight (self-host / free)
$1.25/$4.25 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
54.1%
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 1.1 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 1.1 is comparatively weak here — closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery: Muse Glimmer — Muse Spark 1.1 is comparatively weak here — not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark
Scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported): Muse Spark 1.1 — Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding — and it carries the larger 1M context.
Subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck: Muse Spark 1.1 — Muse Spark 1.1 lists subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck among its strengths; Muse Glimmer does not.
Professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported): Muse Spark 1.1 — Muse Spark 1.1 lists professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported) among its strengths; Muse Glimmer does not.
Lowest cost at scale: Muse Glimmer — Its weights are open, so at volume you pay for your own hardware instead of Muse Spark 1.1's $1.25/$4.25 per 1M tokens.
Largest single-prompt input: Muse Spark 1.1 — Its 1M window is about 8× larger than Muse Glimmer's 128K, fitting roughly 1,573 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 1.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Muse Spark 1.1 — Larger 1M 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 1.1 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 scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported): Muse Spark 1.1 — 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 1.1: where it fits
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. Released July 9, 2026 by Meta, it is built for scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported), subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck, professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported), and managing its own context: it compacts the 1M window mid-run instead of relying on external windowing.
Its trade-offs: not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark, the 1M window oversells its recall: 54.1 on MRCR v2 at 1M against GPT-5.5's 74.0, closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for, and uS-only public preview behind a waitlist, and every benchmark is vendor-reported with no third-party replication. 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 1.1 come from the same lab (Meta), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Muse Glimmer 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 Glimmer and drop down only with a concrete reason.
Frequently asked questions
Is Muse Glimmer or Muse Spark 1.1 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 1.1 leans toward scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Muse Glimmer or Muse Spark 1.1?
Muse Glimmer is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Spark 1.1 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 1.1 — 1M vs 128K, about 8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Muse Spark 1.1 to Muse Glimmer?
Since both are Meta models, the newer one (Muse Glimmer) 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 1.1?
Muse Glimmer — released August 10, 2026, about 32 days after Muse Spark 1.1.
Muse Glimmer vs Muse Spark 1.1
Meta · US | Meta · US · Updated June 2026
Quick verdict
Both are Meta models. Muse Glimmer is the newer, generally stronger default; reach for Muse Spark 1.1 when a specific cost or latency profile matters more than the latest capabilities.
Muse Glimmer and Muse Spark 1.1 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 1.1 is meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. 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
▸Cost model: Muse Glimmer ships open weights you can self-host (hardware cost only, no per-token fee), while Muse Spark 1.1 is API-metered at $1.25/$4.25 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Muse Spark 1.1 holds 8× more — 1M (~1,573 pages) vs 128K (~197 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 Glimmer is the newer model by about 32 days (released August 10, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Muse Glimmer
Muse Spark 1.1
Provider
Meta (US)
Meta (US)
Released
August 10, 2026
July 9, 2026
Context window
128K (~197 pages)
1M (~1,573 pages)
Price (in/out)
Open weight (self-host / free)
$1.25/$4.25 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, image, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
54.1%
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 1.1 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 1.1 is comparatively weak here — closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for
Agentic by design: tool use, coding, file and screenshot reading, multi-step failure recovery
Muse Glimmer
Muse Spark 1.1 is comparatively weak here — not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark
Scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported)
Muse Spark 1.1
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding — and it carries the larger 1M context.
Subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck
Muse Spark 1.1
Muse Spark 1.1 lists subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck among its strengths; Muse Glimmer does not.
Professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported)
Muse Spark 1.1
Muse Spark 1.1 lists professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported) among its strengths; Muse Glimmer does not.
Lowest cost at scale
Muse Glimmer
Its weights are open, so at volume you pay for your own hardware instead of Muse Spark 1.1's $1.25/$4.25 per 1M tokens.
Largest single-prompt input
Muse Spark 1.1
Its 1M window is about 8× larger than Muse Glimmer's 128K, fitting roughly 1,573 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 1.1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Muse Spark 1.1
Larger 1M 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 1.1 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 scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported)
→ Muse Spark 1.1
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 1.1: where it fits
Meta's first paid, closed-weight frontier model — class-leading agentic tool use at a quarter of rivals' price, but it trails on coding. Released July 9, 2026 by Meta, it is built for scaled tool use — 88.1 on MCP Atlas, ahead of Opus 4.8 and GPT-5.5 (vendor-reported), subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck, professional agentic work — 54.7 on JobBench, a wide margin over rivals (vendor-reported), and managing its own context: it compacts the 1M window mid-run instead of relying on external windowing.
Its trade-offs: not the coding leader its launch framing implied — Meta's own report concedes it trails Opus 4.8 and GPT-5.5 on every coding benchmark, the 1M window oversells its recall: 54.1 on MRCR v2 at 1M against GPT-5.5's 74.0, closed weights end the free, self-hostable Llama path — this is the first model Meta has charged for, and uS-only public preview behind a waitlist, and every benchmark is vendor-reported with no third-party replication. 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 1.1 come from the same lab (Meta), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Muse Glimmer 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 Glimmer and drop down only with a concrete reason.
Want both Muse Glimmer and Muse Spark 1.1 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.
Is Muse Glimmer or Muse Spark 1.1 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 1.1 leans toward scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Muse Glimmer or Muse Spark 1.1?
Muse Glimmer is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Muse Spark 1.1 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 1.1 — 1M vs 128K, about 8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from Muse Spark 1.1 to Muse Glimmer?
Since both are Meta models, the newer one (Muse Glimmer) 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 1.1?
Muse Glimmer — released August 10, 2026, about 32 days after Muse Spark 1.1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.