Muse Spark 1.1 vs Solar Pro 4
At a Glance
| Compare |  Muse Spark 1.1Meta |  Solar Pro 4Upstage |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.090Upstage ↗ · Sep 18, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $0.36Upstage ↗ · Sep 18, 2026 |
| Context windowMaximum documented tokens | 1,000K | 524K |
| Model facts checked | Sep 3, 2026View model evidence → | Sep 18, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Muse Spark 1.1 | Solar Pro 4 |
|---|---|---|
| Developer | Meta | Upstage |
| Family | Muse Spark | Solar Pro |
| Model | Muse Spark 1.1 | Solar Pro 4 |
| Version | Muse Spark 1.1 | Solar Pro 4 |
| Lifecycle | preview | active |
| Released | 2026-07-09 | 2026-08-14 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video, Audio | Text |
| Output modalities | Text | Text |
| Context window | 1,000K | 524K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Unknown | Upstage (Standard) |
| Capabilities | chat, computer-use, generation, reasoning, research, structured_outputs, tools | agents, chat, generation, reasoning, structured_outputs, tools |
| Supported languages | Unknown | English, Korean, Japanese |
| Training data cutoff | Unknown | February 2026 |
Muse Spark 1.1 Capabilities
Solar Pro 4 Capabilities
Related Comparisons
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Muse Spark 1.1Meta | vs |  GLM-5.3Z.ai | cross-developer peerstext |
Muse Spark 1.1Meta | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Muse Spark 1.1Meta | cross-developer peersimage, text, video |
Muse Spark 1.1Meta | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Muse Spark 1.1 vs Solar Pro 4 FAQs
Is Muse Spark 1.1 or Solar Pro 4 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Muse Spark 1.1 and Solar Pro 4, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Muse Spark 1.1 or Solar Pro 4?+
Only Solar Pro 4 has a directly sourced input price: $0.090 per million tokens. Only Solar Pro 4 has a directly sourced output price: $0.36 per million tokens.
Which has a larger context window, Muse Spark 1.1 or Solar Pro 4?+
Muse Spark 1.1 has the larger sourced context window. Muse Spark 1.1 supports 1,000K and Solar Pro 4 supports 524K.
Which performs better in benchmarks, Muse Spark 1.1 or Solar Pro 4?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Muse Spark 1.1 or Solar Pro 4 be self-hosted?+
Solar Pro 4 is the only model in this pair currently marked as self-hostable. Muse Spark 1.1 is not marked open weight; Solar Pro 4 is not marked open weight.
Can Muse Spark 1.1 and Solar Pro 4 understand images?+
Muse Spark 1.1 is documented with image input; Solar Pro 4 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Muse Spark 1.1 or Solar Pro 4?+
Neither has a larger sourced maximum output. Muse Spark 1.1 is — and Solar Pro 4 is 131K.
Do Muse Spark 1.1 and Solar Pro 4 support reasoning and tool use?+
Muse Spark 1.1: reasoning, tool calling, and image input. Solar Pro 4: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Muse Spark 1.1 or Solar Pro 4?+
Muse Spark 1.1 has 0 sourced provider routes; Solar Pro 4 has 1, so Solar Pro 4 has broader tracked availability.
Which offers better value, Muse Spark 1.1 or Solar Pro 4?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.