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Model comparison
Meta's Muse Spark 1.3 against Qwen's Qwen3.8 2.4T A95B: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
Muse Spark 1.3 lists at $2.00 per million tokens blended against $3.00 for Qwen3.8 2.4T A95B - 1.5x less.
Muse Spark 1.3 scores 48 on the intelligence index against 40 - 8 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 50 - 5 points apart.
only Muse Spark 1.3 offers image input; only Qwen3.8 2.4T A95B offers open weights.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Muse Spark 1.3 | Qwen3.8 2.4T A95B |
|---|---|---|
| Input price / 1M tokens | $1.25 | $2.00 |
| Output price / 1M tokens | $4.25 | $6.00 |
| Blended (3 in : 1 out) | $2.00 | $3.00 |
| Cached input / 1M | $0.15 | $0.25 |
| Context window | 1.0M | 1.0M |
| Max output | 944K | 909K |
| Input | text, image, video, file | text |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 48 | 40 |
| Coding index | 76 | 72 |
| Agentic index | 56 | 50 |
| Released | Sep 2, 2026 | Aug 12, 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 | Qwen3.8 2.4T A95B | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $46.25 | $70.00 | $23.75 less on Muse Spark 1.3 |
| Document summaries100M in, 5M out - long inputs, short answers. | $146 | $230 | $83.75 less on Muse Spark 1.3 |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $160 | $240 | $80.00 less on Muse Spark 1.3 |
Muse Spark 1.3 is both cheaper and stronger on the intelligence index, so with both in the catalogue the router sends it almost everything; Qwen3.8 2.4T A95B earns a request only on something it alone offers - a longer context, a modality, a data boundary or a region your rules require.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: muse-spark-1.3, kind: llm, metadata: { model: meta/muse-spark-1.3 } }
- { id: qwen3.8-2.4t-a95b, kind: llm, metadata: { model: qwen/qwen3.8-2.4t-a95b } }
# 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: [muse-spark-1.3]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [qwen3.8-2.4t-a95b]
weight: 0.8Every Meta and Qwen model, with prices, is on the vendor pages; the rankings show what the router actually picks.