Model comparison
GLM 5.3 vs Muse Spark 1.3
Z.ai against Meta, compared on context, price, and verified benchmark results.
Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
Z.ai
GLM 5.3
Frontier · Open weights
Meta
Muse Spark 1.3
Frontier
| Specification | GLM 5.3 | Muse Spark 1.3 |
|---|---|---|
| Provider | Z.ai | Meta |
| Tier | Frontier | Frontier |
| Context window | 1M | 1M |
| Max output | 128K | Winner: 131K |
| Input / 1M tokens | $1.40 | Winner: $1.25 |
| Output / 1M tokens | $4.40 | Winner: $4.25 |
| Weights | Open | Closed |
| Parameters | 753B total (MoE; active count unpublished) | Meta frontier model |
| Reasoning levels | low, high, max | low, medium, high |
| Modalities | text | text, image |
| License | glm-5.3 (custom) | Not disclosedUnverified |
| API model id | glm-5.3 | muse-spark-1.3 |
| Released | August 14, 2026 | September 2, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-14) | 60 | Not verifiedUnverified |
| Terminal-Bench 2.1 (2026-08-14) | 88.2 | Winner: 88.8 |
| DeepSWE 1.1 (2026-08-14) | 66.9 | Winner: 75.4 |
| Humanity's Last Exam (2026-08-14) | 62.5 | Not verifiedUnverified |
| SWE-bench Verified (2026-09-01) | 95.4 | Not verifiedUnverified |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
- 2026-09-03: Meta launch table (transcribed by explainx.ai) (Vendor self-report; absent from the official tbench.ai leaderboard)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-09-02: Meta launch table (Thinking mode: max — self-reported; not broadly available and NOT on the official Datacurve leaderboard)Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task. Comparability: directly comparable
- 2026-09-01: vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); Zhipu docs list 77.8 inherited from GLM-5 — attribution contested, both notedReal GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
- 2026-08-14: Z.ai GLM-5.3 blog + HF model card (vendor, with tools, full set)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
Pricing tiers: GLM 5.3: Token API listed at $1.40/$4.40 per MTok from 2026-08-18 (cached input $0.26); Coding Plan access on a points quota. 1M context, 128K max output per official docs; thinking always on. 753B total MoE per the Hugging Face card (active count unpublished). · Muse Spark 1.3: $1.25/$4.25 per MTok on Meta's Model API (cached input $0.15); a $0.10/$0.20 'contributor' tier applies when your data is used for training. Third-party reports put list at $1.50/$7.50 from 2027-01-01 — confirm before long-term commits.
GLM 5.3
GLM 5.3 is Zhipu's flagship (~753B MoE), near the top of the leaderboards, and the current GLM Coding Plan default.
Best for
- Coding Plan subscribers
- Long-horizon coding
- Chinese + English
Watch out
Open weights dropped 2026-08-28 under Z.ai's custom glm-5.3 licence (not a standard open-source licence — review before commercial use; secondary coverage says >$10B-revenue providers need a security review). 5.2/5.1 Coding Plan requests route to 5.3.
Muse Spark 1.3
Muse Spark 1.3 is Meta's September 2026 frontier refresh — a self-reported DeepSWE 1.1 field leader at $1.25/$4.25, with a 1M-token context aimed at autonomous agent workflows.
Best for
- Long-horizon coding agents
- Frontier quality below frontier pricing
- Meta ecosystem
Watch out
The headline 75.4% DeepSWE score comes from the 'max' thinking mode, which is not broadly available yet and is pending independent verification. The 131,072 output cap is per third-party API docs — Meta's own spec page does not publish it. Closed weights, unlike Muse Spark 1.1.
When the cheaper one wins
Muse Spark 1.3 is cheaper on output at $4.25 per million tokens against $4.40 for GLM 5.3 — about 1.0×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 5/5 core specs verified on both sides — All core specifications verified for both models.
- 2 shared named benchmarks with differing scores — Measured on: Terminal-Bench 2.1, DeepSWE 1.1.
- Verified within the last 90 days — Newest catalog check was 6 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- GLM 5.3: Z.ai GLM-5.3 announcement (accessed 2026-08-29)
- GLM 5.3: Hugging Face — zai-org/GLM-5.3 (weights, 2026-08-28) (accessed 2026-08-30)
- Muse Spark 1.3: Meta — Introducing Muse Spark 1.3 (accessed 2026-09-03)
- Muse Spark 1.3: Meta developer — Muse Spark pricing ($1.25/$4.25) (accessed 2026-09-03)
- Muse Spark 1.3: VentureBeat — Muse Spark 1.3 best results need a config developers can't broadly use yet (accessed 2026-09-03)
- Muse Spark 1.3: HaiMaker — muse-spark-1.3 (131,072 max output tokens) (accessed 2026-09-05)
- Muse Spark 1.3: Promptfoo — Meta provider docs (muse-spark-1.3 max_tokens ceiling) (accessed 2026-09-05)
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Diving deeper on one model? GLM 5.3 · Muse Spark 1.3
Common questions
GLM 5.3 vs Muse Spark 1.3
Answered from the verified figures on this page rather than general guidance.
Is GLM 5.3 or Muse Spark 1.3 cheaper for input?
Muse Spark 1.3 is cheaper at $1.25 per million input tokens, against $1.40 for GLM 5.3 — roughly 1.1× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GLM 5.3 has tiered pricing: Token API listed at $1.40/$4.40 per MTok from 2026-08-18 (cached input $0.26); Coding Plan access on a points quota. 1M context, 128K max output per official docs; thinking always on. 753B total MoE per the Hugging Face card (active count unpublished). Muse Spark 1.3 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (cached input $0.15); a $0.10/$0.20 'contributor' tier applies when your data is used for training. Third-party reports put list at $1.50/$7.50 from 2027-01-01 — confirm before long-term commits.
Is GLM 5.3 or Muse Spark 1.3 cheaper for output?
Muse Spark 1.3 is cheaper at $4.25 per million output tokens, against $4.40 for GLM 5.3 — roughly 1.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GLM 5.3 has tiered pricing: Token API listed at $1.40/$4.40 per MTok from 2026-08-18 (cached input $0.26); Coding Plan access on a points quota. 1M context, 128K max output per official docs; thinking always on. 753B total MoE per the Hugging Face card (active count unpublished). Muse Spark 1.3 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (cached input $0.15); a $0.10/$0.20 'contributor' tier applies when your data is used for training. Third-party reports put list at $1.50/$7.50 from 2027-01-01 — confirm before long-term commits.
Which has the larger context window, GLM 5.3 or Muse Spark 1.3?
Both accept about 1M tokens of context, so document length will not decide between them.
Do GLM 5.3 and Muse Spark 1.3 support the same reasoning levels?
GLM 5.3 exposes low, high, max, while Muse Spark 1.3 exposes low, medium, high.
Should I use GLM 5.3 or Muse Spark 1.3?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. GLM 5.3 suits coding plan subscribers; Muse Spark 1.3 suits long-horizon coding agents.
Can I self-host GLM 5.3 or Muse Spark 1.3?
GLM 5.3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Muse Spark 1.3 is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: Muse Spark 1.3 costs $1.25 per 1M tokens versus $1.40 for GLM 5.3 — a 1.1x difference at the headline tier.
- Deployment: GLM 5.3 publishes weights you can self-host; the other is API-only.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.