Muse Spark 1.3
Meta · Proprietary · bench-align-v5
Canonical idmuse-spark-1-3
Access typeProprietary
Blended $/1M$2.0075% input / 25% output
Capability shape
Seven axes from the ranking source. A missing axis is drawn as a gap.
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
| Measurement | Value | Observed | Last good | Evidence |
|---|---|---|---|---|
| Time to first token | 31.02 s | 2026-09-17 | 2026-09-17 | |
| Throughput | 228 tok/s | 2026-09-17 | 2026-09-17 |
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.
| Component | USD | Evidence |
|---|---|---|
| Input / 1M tokens | $1.25 | |
| Output / 1M tokens | $4.25 | |
| Cache read / 1M tokens | $0.15 | |
| Cache write / 1M tokens | Unavailable | |
| Blended / 1M (75% input / 25% output) | $2.00 |
Self-hosted listingMeta Model API lists `muse-spark-1.3` at $1.25 input, $0.15 cached input, and $4.25 output per million tokens. Meta separately lists `muse-spark-1.3-contributor`, whose data may be used to improve Meta products, at $0.10 input, $0.002 cached input, and $0.20 output per million tokens; the standard non-contributor SKU is the headline rate.The rates above are a hosted price matched from another provider, not a first-party list price.
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.
Benchmark record
31 matched benchmark rows with their published value, unit, and provenance.
Knowledge
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index | 48.2 | Cross-benchmark intelligence index | Display-only external reference | Artificial Analysis |
| AA-GPQA Diamond Artificial Analysis GPQA Diamond | 93.5 | Graduate-level science questions | Graduate-level science reasoning | Artificial Analysis GPQA Diamond Benchmark Leaderboard |
| AA-HLE Artificial Analysis Humanity's Last Exam | 48.7 | Expert-level questions | Frontier expert reasoning | Artificial Analysis Humanity's Last Exam Benchmark Leaderboard |
| AA-Omniscience Index Artificial Analysis Omniscience Index | 25.0 | Knowledge questions | Broad factual knowledge | AA-Omniscience: Knowledge and Hallucination Benchmark |
| AA-Omniscience Accuracy Artificial Analysis Omniscience Accuracy | 43.6 | Knowledge questions | Broad knowledge | Artificial Analysis model benchmarks |
| AA-Omniscience Hallucination Rate Artificial Analysis Omniscience Hallucination Rate | 32.9 | Knowledge questions | Factuality | Artificial Analysis model benchmarks |
Coding
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Terminal-Bench 2.1 Terminal-Bench 2.1 (provider run) | 88.8 | Terminal-based software-agent tasks | Professional software engineering | DeepSeek V4 Flash 0731 update |
| SWE-Atlas Codebase QnA SWE-Atlas Codebase QnA | 59.4 | 124 codebase questions across 11 repositories | Production codebase comprehension | Muse Spark 1.3 Evaluation Methodology |
| AA Coding Index Artificial Analysis Coding Index | 75.8 | Cross-benchmark coding index | Display-only external reference | Artificial Analysis model leaderboards |
| AA-SciCode Artificial Analysis SciCode | 58.8 | Scientific coding subproblems | Scientific programming | Artificial Analysis SciCode Benchmark Leaderboard |
| DeepSWE DeepSWE | 75.4 | 113 software engineering tasks across 91 repositories and 5 languages | Long-horizon software engineering | DeepSWE benchmark blog |
Reasoning
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| MRCR v2 256K-512K OpenAI MRCR v2 8-needle 256K-512K | 98.5 | 100 eight-needle retrieval examples | Very long-context retrieval | Muse Spark 1.3 Evaluation Methodology |
| MRCR v2 512K-1M OpenAI MRCR v2 8-needle 512K-1M | 98.1 | 100 eight-needle retrieval examples | Million-token retrieval | Muse Spark 1.3 Evaluation Methodology |
| AA-LCR Artificial Analysis Long Context Reasoning | 83.0 | Long-context reasoning tasks | Long-context reasoning | Artificial Analysis model benchmarks |
| MLCR-AA Medical Long Context Reasoning (MLCR-AA) | 41.1 | Long, fragmented medical-record reasoning | Long-context medical reasoning | Medical Long Context Reasoning (MLCR-AA) |
| CritPt Critical Physics Tasks | 24.9 | Research-level physics questions | Research-level physics reasoning | CritPt Benchmark Leaderboard |
Agentic
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| AA Briefcase Artificial Analysis Briefcase | 1589 | Professional knowledge-work tasks | Professional work | Artificial Analysis Briefcase Benchmark Leaderboard |
| AA AutomationBench Artificial Analysis AutomationBench | 57.9 | Business-process automation tasks | Agentic automation | Artificial Analysis AutomationBench Benchmark Leaderboard |
| AA Tau3 Banking Artificial Analysis Tau3-Banking | 50.5 | Banking tool-use workflows | Agentic banking workflows | Artificial Analysis Tau3-Banking Benchmark Leaderboard |
| Terminal-Bench 2.1 Terminal-Bench 2.1 (provider run) | 88.8 | Terminal-based software-agent tasks | Professional software engineering | DeepSeek V4 Flash 0731 update |
| GDPval-AA GDPval-AA | 1754 | Agentic real-world work tasks | Professional agentic workflows | DeepSeek-V4 Technical Report |
| GDPval-AA GDPval-AA normalized | 60.2 | Economically valuable tasks | Professional agentic workflows | Artificial Analysis model benchmarks |
| AA Agentic Index Artificial Analysis Agentic Index | 55.7 | Cross-benchmark agentic index | Display-only external reference | Artificial Analysis model leaderboards |
| AA Terminal-Bench 4.0 Artificial Analysis Terminal-Bench v4.0 | 33.3 | Terminal-based agent tasks | Agentic software engineering | Artificial Analysis Terminal-Bench v4.0 Benchmark Leaderboard |
| GDP.pdf Artificial Analysis GDP.pdf | 26.6 | Professional document-production tasks | Professional knowledge work | GDP.pdf Benchmark Leaderboard |
| OSWorld 2.0 OSWorld 2.0 | 66.9 | 108 long-horizon computer-use workflows | Long-horizon professional workflows | OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks |
| JobBench JobBench | 64.9 | 130 tasks across 35 occupations | Professional multi-source workflows | JobBench: Aligning Agent Work With Human Will |
| AutomationBench AutomationBench | 49.4 | 600 public automation tasks | Long-horizon automation | Kimi K3: Open Frontier Intelligence |
| DeepSearchQA DeepSearchQA | 89.4 | Agentic browsing and list-answer questions | Agentic web research | Muse Spark Eval Methodology |
| ApprenticeBench ApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable job | 19 | 100 vendor bills processed in sequence inside a simulated construction company | Long-horizon computer use with offline and online continual learning | ApprenticeBench: a step change in AI's job readiness |
Multimodal & Grounded
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| Design Arena Website Design Arena Website Elo | 1364 | Website generation comparisons | Design and website generation | OpenRouter Grok 4.3 benchmarks |
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: 2026-09-17 17:17 UTC.