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Model comparison
Meta's Muse Spark 1.3 against Z.ai's GLM 5.3 Flash: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
GLM 5.3 Flash lists at $0.237 per million tokens blended against $2.00 for Muse Spark 1.3 - 8.4x less.
Muse Spark 1.3 scores 48 on the intelligence index against 42 - 6 points apart.
Muse Spark 1.3 scores 76 on the coding index against 72 - 4 points apart.
Muse Spark 1.3 scores 56 on the agentic index against 51 - 5 points apart.
only GLM 5.3 Flash offers open weights.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Muse Spark 1.3 | GLM 5.3 Flash |
|---|---|---|
| Input price / 1M tokens | $1.25 | $0.15 |
| Output price / 1M tokens | $4.25 | $0.50 |
| Blended (3 in : 1 out) | $2.00 | $0.24 |
| Cached input / 1M | $0.15 | $0.03 |
| Context window | 1.0M | 1.0M |
| Max output | 944K | 944K |
| Input | text, image, video, file | text, image, video |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 48 | 42 |
| Coding index | 76 | 72 |
| Agentic index | 56 | 51 |
| Released | Sep 2, 2026 | Aug 26, 2026 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | Muse Spark 1.3 | GLM 5.3 Flash | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $46.25 | $5.50 | $40.75 less on GLM 5.3 Flash |
| Document summaries100M in, 5M out - long inputs, short answers. | $146 | $17.50 | $129 less on GLM 5.3 Flash |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $160 | $19.00 | $141 less on GLM 5.3 Flash |
GLM 5.3 Flash takes the short and general requests - at $0.237 blended it is the default - and Muse Spark 1.3 the ones whose complexity or quality floor rules the cheaper model out. The split is per request, not per app, and every decision carries the scores for both.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: glm-5.3-flash, kind: llm, metadata: { model: z-ai/glm-5.3-flash } }
- { id: muse-spark-1.3, kind: llm, metadata: { model: meta/muse-spark-1.3 } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [glm-5.3-flash]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [muse-spark-1.3]
weight: 0.8Every Meta and Z.ai model, with prices, is on the vendor pages; the rankings show what the router actually picks.