TL;DRMuse Spark by Meta scores 60.48 overall and carries a context window of 262K tokens — observed 2026-10-23.
Canonical idmuse-spark
Context window262K tokens Access typeProprietary
Blended $/1MUnavailable75% input / 25% output
Capability shape
Seven axes from the ranking source. A missing axis is drawn as a gap.
Capability evidence
Multimodal & Grounded84.4 Instruction Following93.3 Runtime service evidence
Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim.
Time to first token: latency to the first answer chunk (Artificial Analysis, via BenchLM). Reasoning models include thinking time, so values can run to tens or hundreds of seconds.
Evidence key: ObservedUnavailable
Runtime measurements| Measurement | Value | Observed | Last good | Evidence |
|---|
| Time to first token | Unavailable | Unavailable | Unavailable | |
| Throughput | Unavailable | Unavailable | Unavailable | |
Regional or per-endpoint measurements appear only when the API supplies them; none are modelled here.
Endpoint and price matrix
Every published price component, including cache reads and writes.
Price components| Component | USD | Evidence |
|---|
| Input / 1M tokens | Unavailable | |
| Output / 1M tokens | Unavailable | |
| Cache read / 1M tokens | Unavailable | |
| Cache write / 1M tokens | Unavailable | |
| Blended / 1M (75% input / 25% output) | Unavailable | |
| Cost per successful task (LiveBench) | Unavailable | |
Workload-aware monthly cost example
10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. This uses the same calculator as the cost simulator, so an unavailable applicable rate makes the example unavailable too.
Modelled monthly costUnavailableThe applicable input rate is unavailable.
Modelled tokensUnavailable
Benchmark record
40 matched benchmark rows with their published value, unit, and provenance.
Knowledge
Knowledge benchmarks| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|
| GPQA-D GPQA Diamond | 89.5 | Graduate-level science questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| HLE Humanity's Last Exam | 50.4 | Expert-level questions | Frontier expert level | Humanity's Last Exam |
| Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index | 31.3 | Cross-benchmark intelligence index | Display-only external reference | Artificial Analysis |
| AA-GPQA Diamond Artificial Analysis GPQA Diamond | |
Lifecycle and limitations log
Lifecycle events the source associates with this model, plus what this profile does not claim.
No lifecycle event references this model.That is not evidence the model has no lifecycle plan — only that this source published none.
What this profile does not claimValues are reproduced exactly as their sources published them, in the units those sources declared; none are converted, interpolated, or averaged across providers. Any field marked unavailable was attempted and not returned — the attempt timestamp is in each badge. Last attempted fetch for this model’s score: 2026-10-23 13:17 UTC.
ModelScale