Pick Muse Spark 1.1 for scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported) or subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck. Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). Choose Qwen3 235B A22B if you need self-hosting or data privacy; Muse Spark 1.1 if you want a managed API.
Muse Spark 1.1 (Meta, US) and Qwen3 235B A22B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Qwen3 235B A22B 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 4× more — 1M (~1,573 pages) vs 256K (~393 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 Spark 1.1 is the newer model by about 12 months (released July 9, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Muse Spark 1.1
Qwen3 235B A22B
Provider
Meta (US)
Alibaba (China)
Released
July 9, 2026
July 21, 2025
Context window
1M (~1,573 pages)
256K (~393 pages)
Price (in/out)
$1.25/$4.25 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
54.1%
Not published
Who wins what
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 — 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.
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; Qwen3 235B A22B does not.
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux): Qwen3 235B A22B — An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and its weights are open while Muse Spark 1.1 is API-only.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench): Qwen3 235B A22B — Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Muse Spark 1.1 does not.
Outstanding structured logic — 95.0 on ZebraLogic: Qwen3 235B A22B — Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Muse Spark 1.1 does not.
Lowest cost at scale: Qwen3 235B A22B — 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 4× larger than Qwen3 235B A22B's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3 235B A22B — 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: Qwen3 235B A22B — Open weights let you run it on your own hardware; Muse Spark 1.1 is API-only.
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 — It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux): Qwen3 235B A22B — That is its strongest area.
An enterprise with regional data-residency rules: Muse Spark 1.1 or Qwen3 235B A22B — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
The defining split here is open vs. closed. Qwen3 235B A22B gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.1 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.
Frequently asked questions
Is Muse Spark 1.1 or Qwen3 235B A22B 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 Spark 1.1 leans toward scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported) while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Muse Spark 1.1 or Qwen3 235B A22B?
Qwen3 235B A22B 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 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Muse Spark 1.1 and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you Muse Spark 1.1, Qwen3 235B A22B 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, Muse Spark 1.1 or Qwen3 235B A22B?
Muse Spark 1.1 — released July 9, 2026, about 12 months after Qwen3 235B A22B.
Muse Spark 1.1 vs Qwen3 235B A22B
Meta · US | Alibaba · China · Updated June 2026
Quick verdict
Pick Muse Spark 1.1 for scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported) or subagent orchestration — trained to run as a main agent or a subagent that escalates when stuck. Pick Qwen3 235B A22B for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). Choose Qwen3 235B A22B if you need self-hosting or data privacy; Muse Spark 1.1 if you want a managed API.
Muse Spark 1.1 (Meta, US) and Qwen3 235B A22B (Alibaba, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Qwen3 235B A22B is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. 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
▸Cost model: Qwen3 235B A22B 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 4× more — 1M (~1,573 pages) vs 256K (~393 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 Spark 1.1 is the newer model by about 12 months (released July 9, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Muse Spark 1.1
Qwen3 235B A22B
Provider
Meta (US)
Alibaba (China)
Released
July 9, 2026
July 21, 2025
Context window
1M (~1,573 pages)
256K (~393 pages)
Price (in/out)
$1.25/$4.25 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
54.1%
Not published
Who wins what
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
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.
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; Qwen3 235B A22B does not.
Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)
Qwen3 235B A22B
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding — and its weights are open while Muse Spark 1.1 is API-only.
Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)
Qwen3 235B A22B
Qwen3 235B A22B lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; Muse Spark 1.1 does not.
Outstanding structured logic — 95.0 on ZebraLogic
Qwen3 235B A22B
Qwen3 235B A22B lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; Muse Spark 1.1 does not.
Lowest cost at scale
Qwen3 235B A22B
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 4× larger than Qwen3 235B A22B's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3 235B A22B
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
→ Qwen3 235B A22B
Open weights let you run it on your own hardware; Muse Spark 1.1 is API-only.
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
It is specifically built for that.
Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)
→ Qwen3 235B A22B
That is its strongest area.
An enterprise with regional data-residency rules
→ Muse Spark 1.1 or Qwen3 235B A22B
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Qwen3 235B A22B: where it fits
An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.
Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
The defining split here is open vs. closed. Qwen3 235B A22B gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.1 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 Muse Spark 1.1 and Qwen3 235B A22B 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 Spark 1.1 or Qwen3 235B A22B 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 Spark 1.1 leans toward scaled tool use — 88.1 on mcp atlas, ahead of opus 4.8 and gpt-5.5 (vendor-reported) while Qwen3 235B A22B leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Muse Spark 1.1 or Qwen3 235B A22B?
Qwen3 235B A22B 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 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Muse Spark 1.1 and Qwen3 235B A22B together?
Yes — a multi-model platform like LumiChats gives you Muse Spark 1.1, Qwen3 235B A22B 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, Muse Spark 1.1 or Qwen3 235B A22B?
Muse Spark 1.1 — released July 9, 2026, about 12 months after Qwen3 235B A22B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.