{"slug": "muse-spark-1-3-intelligence-performance-and-price-analysis", "title": "Muse Spark 1.3 Intelligence, Performance and Price Analysis", "summary": "Meta's Muse Spark 1.3 (xhigh) reasoning model, released September 2, 2026, scores 61 on the Artificial Analysis Intelligence Index, well above the median of 36 for comparable reasoning models, and is priced at $1.25 per 1M input tokens and $4.25 per 1M output tokens. The model supports text, image, and video input, outputs text, has a 1M token context window, and generates output at 181.7 tokens per second, notably faster than the median of 68.1 t/s.", "body_md": "# Muse Spark 1.3 (xhigh) Intelligence, Performance & Price Analysis\n\n### Model summary\n\nMuse Spark 1.3 (xhigh) is amongst the leading models in intelligence and well priced when comparing to other models of similar price. It's also notably fast, however somewhat verbose. The model supports text, image, and video input, outputs text, and has a 1M tokens context window.\n\nMuse Spark 1.3 (xhigh) scores 61 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 36). When evaluating the Intelligence Index, it generated 100M tokens, which is somewhat verbose in comparison to the median of 71M.\n\nPricing for Muse Spark 1.3 (xhigh) is $1.25 per 1M input tokens (moderately priced, median: $1.75) and $4.25 per 1M output tokens (moderately priced, median: $10.00). In total, it cost $810.19 to evaluate Muse Spark 1.3 (xhigh) on the Intelligence Index.\n\nAt 182 tokens per second, Muse Spark 1.3 (xhigh) is notably fast (68).\n\n| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. |\n|---|---|\n| Input modality | Supports: text, image, and video |\n| Output modality | Supports: text |\n| Context window | 1M ~1500 A4 pages of size 12 Arial font |\n\nMetrics are compared against models of the same class:\n\n- Non-reasoning models → compared only with other non-reasoning models\n- Reasoning models → compared across both reasoning and non-reasoning\n- Open weights models → compared only with other open weights models of the same size class:\n- Tiny: ≤4B parameters\n- Small: 4B–40B parameters\n- Medium: 40B–150B parameters\n- Large: >150B parameters\n- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:\n- <$0.15 per 1M tokens\n- $0.15–$1 per 1M tokens\n- >$1 per 1M tokens\n\nHighlights\n\n## Intelligence\n\n[Artificial Analysis Intelligence Index](/evaluations/artificial-analysis-intelligence-index)\n\n### Artificial Analysis Intelligence Index by Open Weights / Proprietary\n\n### Intelligence Evaluations\n\nAgentic real-world work tasks, (Elo-500)/2000\n\nAgentic tool use\n\nAgentic coding & terminal use\n\nCoding\n\nReasoning & knowledge\n\nScientific reasoning\n\nPhysics reasoning\n\nKnowledge\n\n1 - hallucination rate\n\nLong context reasoning\n\nAgentic knowledge work, Elo\n\nAgentic SaaS workflows\n\nLegal agentic work, criterion pass rate\n\nAgentic business operations\n\nQuantitative analysis on spreadsheets & documents\n\nInstruction following\n\nLong-horizon agentic tasks\n\nKubernetes incident root-cause analysis\n\nVisual reasoning\n\n### AA-Omniscience\n\n### AA-Omniscience Index\n\n## Intelligence Index Comparisons\n\n### Intelligence Index vs. Cost per Intelligence Index Task\n\n## Token Use\n\n### Output Tokens per Intelligence Index Task\n\n## Cost\n\n### Cost per Intelligence Index Task\n\n### Cost to Run Artificial Analysis Intelligence Index\n\n### Pricing: Cache Hit, Input, and Output\n\n## Context Window\n\n### Context Window\n\n## Speed\n\nMeasured by Output Speed (tokens per second)\n\n### Output Speed\n\n### Time per Intelligence Index Task\n\n## Latency\n\nMeasured by Time (seconds) to First Token\n\n### Latency: Time To First Answer Token\n\n## End-to-End Response Time\n\nSeconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed\n\n### End-to-End Response Time\n\n## Frequently Asked Questions\n\nCommon questions about Muse Spark 1.3 (xhigh)\n\nMuse Spark 1.3 (xhigh) was released on September 2, 2026.\n\nMuse Spark 1.3 (xhigh) was created by Meta.\n\nMuse Spark 1.3 (xhigh) scores 61 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 36).\n\nMuse Spark 1.3 (xhigh) generates output at 181.7 tokens per second (based on Meta's API), which is well above average compared to other reasoning models in a similar price tier (median: 68.1 t/s).\n\nMuse Spark 1.3 (xhigh) has a time to first token (TTFT) of 27.51s (based on Meta's API), which is at the higher end compared to other reasoning models in a similar price tier (median: 3.04s).\n\nMuse Spark 1.3 (xhigh) costs $1.25 per 1M input tokens (better than average, median: $1.75) and $4.25 per 1M output tokens (better than average, median: $10.00), based on Meta's API.\n\nMuse Spark 1.3 (xhigh) costs $1.25 per 1M input tokens and $4.25 per 1M output tokens (based on Meta's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.78 per 1M tokens. Pricing may vary by provider. [Compare provider pricing](/models/muse-spark-1-3-xhigh/providers)\n\nWhen evaluated on the Intelligence Index, Muse Spark 1.3 (xhigh) generated 100M output tokens, which is somewhat higher than average compared to other reasoning models in a similar price tier (median: 71M).\n\nYes, Muse Spark 1.3 (xhigh) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.\n\nMuse Spark 1.3 (xhigh) supports text, image, and video input.\n\nMuse Spark 1.3 (xhigh) supports text output.\n\nYes, Muse Spark 1.3 (xhigh) supports image input and can analyze, describe, and answer questions about images.\n\nYes, Muse Spark 1.3 (xhigh) is multimodal. It can process text, image, and video input and generate text output.\n\nMuse Spark 1.3 (xhigh) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.\n\nNo, Muse Spark 1.3 (xhigh) is proprietary. The model weights are not publicly available.\n\nMuse Spark 1.3 (xhigh) is a proprietary model and Meta has not disclosed the model size or parameter count.\n\nMuse Spark 1.3 (xhigh) achieves a score of 61 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.\n\nYes, Muse Spark 1.3 (xhigh) is available via API through 1 provider. [Compare API providers](/models/muse-spark-1-3-xhigh/providers)\n\nMuse Spark 1.3 (xhigh) is available through 1 API provider. [Compare providers](/models/muse-spark-1-3-xhigh/providers)", "url": "https://wpnews.pro/news/muse-spark-1-3-intelligence-performance-and-price-analysis", "canonical_source": "https://artificialanalysis.ai/models/muse-spark-1-3-xhigh", "published_at": "2026-09-02 23:00:14+00:00", "updated_at": "2026-09-02 23:22:43.117706+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-products"], "entities": ["Meta", "Muse Spark 1.3 (xhigh)", "Artificial Analysis Intelligence Index"], "alternates": {"html": "https://wpnews.pro/news/muse-spark-1-3-intelligence-performance-and-price-analysis", "markdown": "https://wpnews.pro/news/muse-spark-1-3-intelligence-performance-and-price-analysis.md", "text": "https://wpnews.pro/news/muse-spark-1-3-intelligence-performance-and-price-analysis.txt", "jsonld": "https://wpnews.pro/news/muse-spark-1-3-intelligence-performance-and-price-analysis.jsonld"}}