Both are Meta models. Muse Spark 1.1 is the newer, generally stronger default; reach for Llama 4 Maverick when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Llama 4 Maverick 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. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. 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: Llama 4 Maverick 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: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Recency: Muse Spark 1.1 is the newer model by about 15 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Llama 4 Maverick
Muse Spark 1.1
Provider
Meta (US)
Meta (US)
Released
April 2025
July 9, 2026
Context window
1M (~1,500 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
Open weights, 1M context: Llama 4 Maverick — Open weights make this possible at all — Muse Spark 1.1 is API-only, so it cannot leave the vendor's servers.
Strong image + text understanding: Llama 4 Maverick — Meta's open-weight 1M-context multimodal model for self-hosted deployments — and its weights are open while Muse Spark 1.1 is API-only.
Self-hostable: Llama 4 Maverick — 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
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 is the newer of the two.
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; Llama 4 Maverick 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; Llama 4 Maverick does not.
Lowest cost at scale: Llama 4 Maverick — 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.
Which should you pick?
A cost-sensitive startup shipping high volume: Llama 4 Maverick — 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: Llama 4 Maverick — Open weights let you run it on your own hardware; Muse Spark 1.1 is API-only.
Anyone whose priority is open weights, 1m context: Llama 4 Maverick — 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.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs are real: needs serious hardware to self-host, and trails closed frontier on reasoning. 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 Llama 4 Maverick 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 Spark 1.1 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 1.1 and drop down only with a concrete reason.
Frequently asked questions
Is Llama 4 Maverick 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, Llama 4 Maverick leans toward open weights, 1m context 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, Llama 4 Maverick or Muse Spark 1.1?
Llama 4 Maverick 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?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Llama 4 Maverick to Muse Spark 1.1?
Since both are Meta models, the newer one (Muse Spark 1.1) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Llama 4 Maverick or Muse Spark 1.1?
Muse Spark 1.1 — released July 9, 2026, about 15 months after Llama 4 Maverick.
Llama 4 Maverick vs Muse Spark 1.1
Meta · US | Meta · US · Updated June 2026
Quick verdict
Both are Meta models. Muse Spark 1.1 is the newer, generally stronger default; reach for Llama 4 Maverick when its lower price or a specific cost or latency profile matters more than the latest capabilities.
Llama 4 Maverick 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. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. 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: Llama 4 Maverick 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: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
▸Recency: Muse Spark 1.1 is the newer model by about 15 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Llama 4 Maverick
Muse Spark 1.1
Provider
Meta (US)
Meta (US)
Released
April 2025
July 9, 2026
Context window
1M (~1,500 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
Open weights, 1M context
Llama 4 Maverick
Open weights make this possible at all — Muse Spark 1.1 is API-only, so it cannot leave the vendor's servers.
Strong image + text understanding
Llama 4 Maverick
Meta's open-weight 1M-context multimodal model for self-hosted deployments — and its weights are open while Muse Spark 1.1 is API-only.
Self-hostable
Llama 4 Maverick
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
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 is the newer of the two.
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; Llama 4 Maverick 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; Llama 4 Maverick does not.
Lowest cost at scale
Llama 4 Maverick
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.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Maverick
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
→ Llama 4 Maverick
Open weights let you run it on your own hardware; Muse Spark 1.1 is API-only.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
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.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs are real: needs serious hardware to self-host, and trails closed frontier on reasoning. 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 Llama 4 Maverick 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 Spark 1.1 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 1.1 and drop down only with a concrete reason.
Want both Llama 4 Maverick 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 Llama 4 Maverick 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, Llama 4 Maverick leans toward open weights, 1m context 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, Llama 4 Maverick or Muse Spark 1.1?
Llama 4 Maverick 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?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Llama 4 Maverick to Muse Spark 1.1?
Since both are Meta models, the newer one (Muse Spark 1.1) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Llama 4 Maverick or Muse Spark 1.1?
Muse Spark 1.1 — released July 9, 2026, about 15 months after Llama 4 Maverick.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.