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Model comparison
Meta's Muse Spark 1.3 against OpenAI's GPT-5.6 Sol: 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 $8.00 for GPT-5.6 Sol - 4.0x less.
Muse Spark 1.3 scores 48 on the intelligence index against 47 - 1 points apart.
GPT-5.6 Sol scores 77 on the coding index against 76 - 2 points apart.
Muse Spark 1.3 scores 56 on the agentic index against 50 - 5 points apart.
GPT-5.6 Sol reads 1.1M tokens at once, Muse Spark 1.3 1.0M.
Muse Spark 1.3 was released about 2 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Muse Spark 1.3 | GPT-5.6 Sol |
|---|---|---|
| Input price / 1M tokens | $1.25 | $4.00 |
| Output price / 1M tokens | $4.25 | $20.0 |
| Blended (3 in : 1 out) | $2.00 | $8.00 |
| Cached input / 1M | $0.15 | $0.40 |
| Context window | 1.0M | 1.1M |
| Max output | 944K | 128K |
| Input | text, image, video, file | file, image, text |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 48 | 47 |
| Coding index | 76 | 77 |
| Agentic index | 56 | 50 |
| Released | Sep 2, 2026 | Jul 9, 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 | GPT-5.6 Sol | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $46.25 | $180 | $134 less on Muse Spark 1.3 |
| Document summaries100M in, 5M out - long inputs, short answers. | $146 | $500 | $354 less on Muse Spark 1.3 |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $160 | $640 | $480 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; GPT-5.6 Sol 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: gpt-5.6-sol, kind: llm, metadata: { model: openai/gpt-5.6-sol } }
# 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: [gpt-5.6-sol]
weight: 0.8Every Meta and OpenAI model, with prices, is on the vendor pages; the rankings show what the router actually picks.