Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. Pick Muse Spark 1.3 for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2 or deepswe v1.1 long-horizon software engineering (75.4). Choose GLM 4.7 if you need self-hosting or data privacy; Muse Spark 1.3 if you want a managed API.
GLM 4.7 (Z.ai, China) and Muse Spark 1.3 (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: GLM 4.7 is about 2.1× cheaper on input ($0.6/$2.2 per 1M tokens vs $1.25/$4.25 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: Muse Spark 1.3 holds 4.9× more — 1M tokens (~1,500 pages) vs 200K (~304 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 1.3 is the newer model by about 8 months (released September 2, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
GLM 4.7
Muse Spark 1.3
Provider
Z.ai (China)
Meta (US)
Released
December 22, 2025
September 2, 2026
Context window
200K (~304 pages)
1M tokens (~1,500 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$1.25/$4.25 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, video
SWE-Bench Verified
73.8%
Not published
MRCR v2 @ 1M
Not published
98.1%
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions: GLM 4.7 — Open weights make this possible at all — Muse Spark 1.3 is API-only, so it cannot leave the vendor's servers.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch: GLM 4.7 — At $0.6/$2.2 per 1M tokens it undercuts Muse Spark 1.3 ($1.25/$4.25 per 1M tokens), and that gap compounds at volume.
An unusually generous 128K maximum output, which suits bulk refactors and long generation: GLM 4.7 — An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
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 4.9× more than GLM 4.7's 200K 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: GLM 4.7 — At $0.6/$2.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Muse Spark 1.3 — Its 1M tokens window is about 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: GLM 4.7 — At $0.6/$2.2 per 1M tokens 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: GLM 4.7 — Open weights let you run it on your own hardware; Muse Spark 1.3 is API-only.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions: GLM 4.7 — 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.
An enterprise with regional data-residency rules: Muse Spark 1.3 or GLM 4.7 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 out per million tokens, it sits in the budget price band.
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
The defining split here is open vs. closed. GLM 4.7 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.3 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 GLM 4.7 or Muse Spark 1.3 better for coding?
Public SWE-Bench figures are not available for Muse Spark 1.3, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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, GLM 4.7 or Muse Spark 1.3?
GLM 4.7 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 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and Muse Spark 1.3 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, Muse Spark 1.3 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, GLM 4.7 or Muse Spark 1.3?
Muse Spark 1.3 — released September 2, 2026, about 8 months after GLM 4.7.
GLM 4.7 vs Muse Spark 1.3
Z.ai · China | Meta · US · Updated June 2026
Quick verdict
Pick GLM 4.7 for genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions or strong agentic coding for the price — 73.8% on swe-bench verified undercut most closed frontier models at launch. Pick Muse Spark 1.3 for agentic coding with ~20% fewer tool calls and ~25% fewer tokens than muse spark 1.2 or deepswe v1.1 long-horizon software engineering (75.4). Choose GLM 4.7 if you need self-hosting or data privacy; Muse Spark 1.3 if you want a managed API.
GLM 4.7 (Z.ai, China) and Muse Spark 1.3 (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. GLM 4.7 is an MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. 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. 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
▸Price: GLM 4.7 is about 2.1× cheaper on input ($0.6/$2.2 per 1M tokens vs $1.25/$4.25 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: Muse Spark 1.3 holds 4.9× more — 1M tokens (~1,500 pages) vs 200K (~304 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 1.3 is the newer model by about 8 months (released September 2, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
GLM 4.7
Muse Spark 1.3
Provider
Z.ai (China)
Meta (US)
Released
December 22, 2025
September 2, 2026
Context window
200K (~304 pages)
1M tokens (~1,500 pages)
Price (in/out)
$0.6/$2.2 per 1M tokens
$1.25/$4.25 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, video
SWE-Bench Verified
73.8%
Not published
MRCR v2 @ 1M
Not published
98.1%
Who wins what
Genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions
GLM 4.7
Open weights make this possible at all — Muse Spark 1.3 is API-only, so it cannot leave the vendor's servers.
Strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch
GLM 4.7
At $0.6/$2.2 per 1M tokens it undercuts Muse Spark 1.3 ($1.25/$4.25 per 1M tokens), and that gap compounds at volume.
An unusually generous 128K maximum output, which suits bulk refactors and long generation
GLM 4.7
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2 — and it runs cheaper at $0.6/$2.2 per 1M tokens.
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 4.9× more than GLM 4.7's 200K in a single prompt.
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.
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
GLM 4.7
At $0.6/$2.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Muse Spark 1.3
Its 1M tokens window is about 4.9× larger than GLM 4.7's 200K, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ GLM 4.7
At $0.6/$2.2 per 1M tokens 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
→ GLM 4.7
Open weights let you run it on your own hardware; Muse Spark 1.3 is API-only.
Anyone whose priority is genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions
→ GLM 4.7
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.
An enterprise with regional data-residency rules
→ Muse Spark 1.3 or GLM 4.7
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
GLM 4.7: where it fits
An MIT-licensed 358B open mixture-of-experts with strong 73.8% SWE-Bench Verified coding — but two generations behind GLM 5.2. Released December 22, 2025 by Z.ai, it is built for genuinely permissive open weights — an MIT-licensed 358B mixture-of-experts with no commercial restrictions, strong agentic coding for the price — 73.8% on SWE-Bench Verified undercut most closed frontier models at launch, an unusually generous 128K maximum output, which suits bulk refactors and long generation, and cheap long-running agent loops thanks to aggressive prompt caching.
Its trade-offs are real: two generations behind — GLM 5, 5.1 and 5.2 have all shipped since, and new builds should default to those, its Verified lead narrows sharply on harder evaluations like SWE-Bench Pro, and text-only with no vision, and self-hosting a 358B model is a serious hardware commitment. At $0.6 in / $2.2 out per million tokens, it sits in the budget price band.
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
The defining split here is open vs. closed. GLM 4.7 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Muse Spark 1.3 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 GLM 4.7 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.
Public SWE-Bench figures are not available for Muse Spark 1.3, so the honest test is your own repository — run an identical real bug through both. By design, GLM 4.7 leans toward genuinely permissive open weights — an mit-licensed 358b mixture-of-experts with no commercial restrictions 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, GLM 4.7 or Muse Spark 1.3?
GLM 4.7 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 200K, about 4.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GLM 4.7 and Muse Spark 1.3 together?
Yes — a multi-model platform like LumiChats gives you GLM 4.7, Muse Spark 1.3 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, GLM 4.7 or Muse Spark 1.3?
Muse Spark 1.3 — released September 2, 2026, about 8 months after GLM 4.7.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.