# Muse Spark Open Meta · Open Weight · rank 47 · bench-align-v5 > Every figure below is reproduced as its upstream source published it: nothing is modelled, estimated, interpolated or converted. `Unavailable` means no source published the value — it is never a zero. Each value carries its evidence state and the date it was observed. Page: https://modelscale.dev/models/muse-spark-open JSON: https://modelscale.dev/api/model/muse-spark-open ## Facts | Field | Value | Evidence | | --- | --- | --- | | Canonical id | `muse-spark-open` | — | | Overall score | 62.76 | Observed 2026-10-23 · source benchlm:models | | Context window | 1M tokens | Observed 2026-10-23 · source benchlm:models | | Release date | 2026-10-15 | Observed 2026-10-23 · source benchlm:models | | Access type | Open Weight | — | | Blended $/1M (75% input / 25% output) | $2.00 | Derived from the input and output rates below | ## Capability evidence Seven axes from the ranking source. An axis the source did not score is unavailable, not zero. | Axis | Score | Evidence | | --- | --- | --- | | Agentic | Unavailable | Unavailable · source benchlm:models | | Coding | Unavailable | Unavailable · source benchlm:models | | Knowledge | 63.3 | Observed 2026-10-23 · source benchlm:models | | Reasoning | 78.8 | Observed 2026-10-23 · source benchlm:models | | Multimodal & Grounded | 83.6 | Observed 2026-10-23 · source benchlm:models | | Instruction Following | Unavailable | Unavailable · source benchlm:models | | Math | Unavailable | Unavailable · source benchlm:models | ## Runtime service evidence Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim. Regional or per-endpoint measurements appear only when the API supplies them; none are modelled. | Measurement | Value | Observed | Last good | Evidence | | --- | --- | --- | --- | --- | | Time to first token | 15.23 s | 2026-10-23 | 2026-10-23 | Observed 2026-10-23 · source benchlm:speed | | Throughput | 139 tok/s | 2026-10-23 | 2026-10-23 | Observed 2026-10-23 · source benchlm:speed | ## Endpoint and price matrix Every published price component, including cache reads and writes. | Component | USD | Evidence | | --- | --- | --- | | Input / 1M tokens | $1.25 | Observed 2026-10-23 · source openrouter:pricing | | Output / 1M tokens | $4.25 | Observed 2026-10-23 · source openrouter:pricing | | Cache read / 1M tokens | $0.15 | Observed 2026-10-23 · source openrouter:pricing | | Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing | | Blended / 1M (75% input / 25% output) | $2.00 | Derived — from the input and output rates above; it has no source record of its own | | Cost per successful task (LiveBench) | Unavailable | Fetch failed · source livebench:table | **Self-hosted listing.** Meta publishes downloadable Muse Model License weights and names hosted API partners (Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter, Together AI). Neither the launch post nor the model card publishes a per-token price, and the Hugging Face page lists no deployed inference provider at the time of review. Partner prices are not copied here. 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. Derived here from the published rates above by this site's own calculator — not a figure any source published. | Field | Value | | --- | --- | | Modelled monthly cost | $5.63 | | Modelled tokens | 2.82M | ## Benchmark record 25 matched benchmark rows with their published value, unit, and provenance. ### Knowledge | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | Artificial Analysis Intelligence Index | 38.9 | Cross-benchmark intelligence index | Display-only external reference | [Artificial Analysis](https://artificialanalysis.ai/) | | AA-GPQA Diamond (Artificial Analysis GPQA Diamond) | 89.0 | Graduate-level science questions | Graduate-level science reasoning | [Artificial Analysis GPQA Diamond Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/gpqa-diamond) | | AA-HLE (Artificial Analysis Humanity's Last Exam) | 43.9 | Expert-level questions | Frontier expert reasoning | [Artificial Analysis Humanity's Last Exam Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/hle) | | AA-Omniscience Index (Artificial Analysis Omniscience Index) | 24.7 | Knowledge questions | Broad factual knowledge | [AA-Omniscience: Knowledge and Hallucination Benchmark](https://artificialanalysis.ai/evaluations/omniscience) | | AA-Omniscience Accuracy (Artificial Analysis Omniscience Accuracy) | 39.3 | Knowledge questions | Broad knowledge | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA-Omniscience Hallucination Rate (Artificial Analysis Omniscience Hallucination Rate) | 24.1 | Knowledge questions | Factuality | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | ### Coding | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | SWE-Atlas Codebase QnA | 58.0 | 124 codebase questions across 11 repositories | Production codebase comprehension | [Muse Spark 1.3 Evaluation Methodology](https://research.meta.ai/static/muse-spark-1-3-multimodal-evaluation-methodology) | | AA-SciCode (Artificial Analysis SciCode) | 58.4 | Scientific coding subproblems | Scientific programming | [Artificial Analysis SciCode Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/scicode) | | DeepSWE | 60.0 | 113 software engineering tasks across 91 repositories and 5 languages | Long-horizon software engineering | [DeepSWE benchmark blog](https://deepswe.datacurve.ai/blog) | ### Reasoning | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-LCR (Artificial Analysis Long Context Reasoning) | 78.3 | Long-context reasoning tasks | Long-context reasoning | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | CritPt (Critical Physics Tasks) | 14.9 | Research-level physics questions | Research-level physics reasoning | [CritPt Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/critpt) | ### Agentic | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA Briefcase (Artificial Analysis Briefcase) | 1279 | Professional knowledge-work tasks | Professional work | [Artificial Analysis Briefcase Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/aa-briefcase) | | AA AutomationBench (Artificial Analysis AutomationBench) | 49.7 | Business-process automation tasks | Agentic automation | [Artificial Analysis AutomationBench Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/automationbench-aa) | | HLE w/ tools (Humanity's Last Exam with tools) | 61.0 | Expert questions with tool use | Frontier tool-augmented reasoning | [DeepSeek-V4 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) | | GDPval-AA | 1401 | Agentic real-world work tasks | Professional agentic workflows | [DeepSeek-V4 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) | | GDPval-AA (GDPval-AA normalized) | 45.1 | Economically valuable tasks | Professional agentic workflows | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA Terminal-Bench 4.0 (Artificial Analysis Terminal-Bench v4.0) | 8.1 | Terminal-based agent tasks | Agentic software engineering | [Artificial Analysis Terminal-Bench v4.0 Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/terminalbench-v4-0) | | GDP.pdf (Artificial Analysis GDP.pdf) | 18.6 | Professional document-production tasks | Professional knowledge work | [GDP.pdf Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/gdp-pdf) | | JobBench | 61.8 | 130 tasks across 35 occupations | Professional multi-source workflows | [JobBench: Aligning Agent Work With Human Will](https://arxiv.org/abs/2605.26329) | | MCP Atlas | 89.2 | Tool-integrated agent tasks | Advanced tool use | [Introducing GPT-5.4 mini and nano](https://openai.com/index/introducing-gpt-5-4-mini-and-nano/) | | Toolathlon-Verified | 72.8 | Verified multi-tool workflows | Advanced tool use | [Kimi K3: Open Frontier Intelligence](https://www.kimi.com/blog/kimi-k3) | ### Multimodal & Grounded | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-MMMU-Pro (Artificial Analysis MMMU-Pro) | 80.9 | Multimodal academic reasoning | Frontier multimodal | [Artificial Analysis MMMU-Pro Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/mmmu-pro) | | ZeroBench | 50.0 | 100 visual reasoning questions | Tool-augmented visual reasoning | [Muse Spark Eval Methodology](https://ai.meta.com/static-resource/muse-spark-eval-methodology) | | SimpleVQA | 73.6 | Visual QA tasks | General visual understanding | [GLM-5V-Turbo](https://docs.z.ai/guides/vlm/glm-5v-turbo) | | BabyVision | 80.2 | Visual perception tasks | Fine-grained visual perception | [Muse Spark 1.1 Evaluation Report](https://ai.meta.com/static-resource/muse-spark-1-1-evaluation-report) | ## Lifecycle and limitations log Lifecycle events the source associates with this model. 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 claim Values 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. Last attempted fetch for this model's score: 2026-10-23 13:17 UTC.