meta Muse Spark 1.1

Muse Spark 1.1 by meta excels in the Coding category, where it ranks #55 with a composite score of 81/100. Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context... It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Muse Spark 1.1 is priced at $1.25 per million input tokens and $4.25 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Muse Spark 1.1 holds rank #55 out of 320 models tracked. Its quality rank is #55 and adoption rank is #55. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Muse Spark 1.1 has been evaluated across 6 different signals. Its strongest areas include Capabilities (100/100), Benchmarks (79/100), Pricing (96/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 81/100 reflects a weighted combination of all tracked signals.

Muse Spark 1.1 is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check meta's website for current free tier availability and promotional offers.

Muse Spark 1.1 supports a 1,049K token context window (1,048,576 tokens total). That translates to roughly 786,432 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Muse Spark 1.1 supports image understanding (vision), function/tool calling, structured JSON output, extended reasoning/chain-of-thought, web search, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. Vision support means it can analyze images, screenshots, and diagrams alongside text. These capabilities determine which workflows and integrations the model can handle natively.

Muse Spark 1.1 was developed by meta. It was released on July 16, 2026. You can access it through meta's API. Check our provider page for all models from meta and how they compare against each other.

Pick Muse Spark 1.1 when you need top-tier performance and can justify the cost for quality-critical tasks like production code generation, complex reasoning, or enterprise deployments. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Muse Spark 1.1 through meta's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.