Chat With Muse Spark 1.1 and Automate Complex Workflows
Try Muse Spark 1.1 on Lorka AI to plan, coordinate, and complete demanding coding, research, computer use, and multimodal tasks.
Proprietary multimodal reasoning model designed for agentic workflows, advanced coding, tool and computer use, and long-context reasoning.
Try Muse Spark 1.1 and Move From Simple Answers to Completed Tasks
Muse Spark 1.1 combines long-context reasoning, tool use, and multimodal understanding to handle practical tasks that span multiple steps and applications.
Personal agentic workflows
Strong computer use
Large-codebase engineering
Multimodal reasoning
Advanced safety
Muse Spark 1.1 Benchmarks: Real Agentic Task Examples
See how the Muse Spark 1.1 model can turn compact instructions into structured, production-ready work.
Plan and implement a new feature in this full-stack app: design the UI from this mockup, update the backend, write tests, and produce a summary of changes.
I’ll implement this as a new saved-filters feature using the existing React, Express, and PostgreSQL architecture. Plan 1. Inspect the current filter state and API conventions. 2. Build the saved-filter UI from the supplied mockup. 3. Add create, list, apply, rename, and delete endpoints. 4. Add database persistence and ownership checks. 5. Cover API and component behavior with tests. 6. Run linting, type checks, and the affected test suites. Backend implementation
1CREATE TABLE saved_filters (2 id UUID PRIMARY KEY DEFAULT gen_random_uuid(),3 user_id UUID NOT NULL REFERENCES users(id) ON DELETE CASCADE,4 name VARCHAR(80) NOT NULL,5 filter JSONB NOT NULL,6 created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),7 updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),8 UNIQUE (user_id, name)9);1011CREATE INDEX saved_filters_user_id_idx12ON saved_filters(user_id);1314router.post("/saved-filters", requireUser, async (req, res) => {15 const parsed = savedFilterSchema.safeParse(req.body);1617 if (!parsed.success) {18 return res.status(400).json({19 error: "INVALID_FILTER",20 details: parsed.error.flatten(),21 });22 }2324 const savedFilter = await db.savedFilter.create({25 data: {26 userId: req.user.id,27 name: parsed.data.name,28 filter: parsed.data.filter,29 },30 });3132 return res.status(201).json(savedFilter);33});Frontend behavior - Added a Save filter action beside the existing filter controls. - Added validation for empty and duplicate names. - Added an accessible menu for applying, renaming, or deleting saved filters. - Preserved the active filter in the URL so shared links still work. Verification ✓ api/saved-filters.test.ts 9 passed ✓ SavedFilterMenu.test.tsx 7 passed ✓ npm run typecheck passed ✓ npm run lint passed Summary of changes Implemented saved filters across the database, API, and React interface. Added authorization checks, validation, keyboard navigation, error states, and 16 automated tests.”
Use my browser workspace to collect competitor pricing from three sites, compile a table, and draft a pricing memo.
I opened the three supplied competitor pages, switched each plan selector to annual billing, and recorded the prices shown for comparable team plans. Collected pricing Competitor A Team plan: $24 per user/month, billed annually Minimum seats: 5 Notable inclusion: Unlimited projects Competitor B Business plan: $29 per user/month, billed annually Minimum seats: None displayed Notable inclusion: Advanced reporting Competitor C Growth plan: $19 per user/month, billed annually Minimum seats: 10 Notable inclusion: Workflow automation Pricing memo The visible market range is $19–$29 per user each month. Competitor C has the lowest headline price but requires at least 10 seats, producing a higher minimum commitment. Competitor A sits near the midpoint and emphasizes broad product access. Competitor B charges the most and positions reporting as its premium differentiator. A practical entry point would be $23–$25 per user/month for the core team plan. This remains competitive without signaling a budget product. Consider reserving advanced reporting and automation for a higher tier rather than discounting the core plan. Recommended follow-up - Confirm monthly-billing prices before publication. - Check whether taxes or usage fees are excluded. - Revisit all three pages quarterly because pricing may change.
From this smartphone video, extract good product photos, summarize the item, and create a listing description for Marketplace.
I reviewed the video and selected frames at 00:04, 00:11, 00:18, and 00:27 because they provide the clearest front, side, rear, and condition views. Item summary - Product: Mid-century-style wooden side table - Finish: Warm walnut tone - Condition: Good used condition - Visible wear: Small surface mark near the back-right corner - Not confirmed from the video: Brand, dimensions, and wood type Listing title Walnut-Finish Wooden Side Table in Good Condition Listing description Compact wooden side table with a warm walnut-style finish and clean, mid-century-inspired lines. It appears sturdy and is well suited to a living room, bedroom, home office, or reading corner. The table is in good used condition. A small cosmetic mark is visible near the back-right corner and is shown in the photos. No major cracks or structural damage were visible in the supplied video. Please confirm the measurements before publishing the listing. Suggested photo order 1. Full front view 2. Three-quarter side view 3. Top surface and condition 4. Close-up of the visible mark
Read these PDFs and screenshots, identify the key risks, and propose an action plan with clear owner, timeline, and dependencies.
I reviewed the supplied policy documents, vendor assessment, implementation notes, and dashboard screenshots. Key risks 1. Unclear data retention terms — High The vendor agreement allows retention for operational purposes but does not define a fixed deletion period. 2. Incomplete access controls — High The screenshots show shared administrator credentials and no visible role separation. 3. Unresolved integration dependency — Medium Production deployment depends on an identity-provider upgrade that has not yet been scheduled. 4. Limited monitoring coverage — Medium Current alerts cover availability but not unusual exports, permission changes, or failed authentication spikes. Action plan Week 1 — Security owner - Replace shared administrator access with named accounts. - Enforce multifactor authentication. - Export the current permissions list for review. Dependency: Identity team availability. Weeks 1–2 — Legal and procurement - Request a defined retention period and deletion confirmation process. - Add breach-notification timing and subprocessors to the contract review. Dependency: Vendor legal response. Weeks 2–3 — Platform engineering - Complete identity-provider compatibility testing. - Add role-based access controls to the staging environment. - Create rollback steps before production deployment. Dependency: Identity upgrade date. Weeks 3–4 — Security operations - Add alerts for large exports, administrator changes, and repeated login failures. - Run a tabletop incident-response exercise. Dependency: Logging access from the vendor Decision recommendation Do not approve unrestricted production use yet. Proceed with a limited staging pilot while the two high-priority risks are resolved.
Combine Muse Spark 1.1 With More Frontier Models on Lorka AI
Access Muse Spark 1.1 on Lorka’s AI platform, where you can mix its main advantages with other top AI models like GPT-5.6, Opus 5, Kimi K3, and beyond under a single plan.
One multi-model workspace
Access Muse Spark alongside Claude, ChatGPT, Gemini, Kimi, GLM, Grok, and other leading models.
Faster workflow experimentation
Test agentic plans, computer-use tasks, multimodal prompts, and tool-assisted processes without rebuilding your workspace.
Side-by-side coding comparisons
Compare architecture decisions, patches, debugging approaches, and tests across several frontier models.
Centralized working history
Keep long conversations, uploaded materials, and model outputs organized in one place.
Cost-aware model selection
Review visible pricing and guidance before choosing a model for short prompts or context-heavy workflows.
Muse Spark 1.1 Context Window, Pricing, and More
Model Type
- Multimodal reasoning model designed for agentic tasks, coding, tool use, and computer operation
- Built to plan multi-step work and coordinate tools or subordinate agents
- Intended for complex workflow execution rather than basic question answering
Context Length
- Supports a 1,000,000-token context window
- Expanded from the 262,000-token window reported for Muse Spark 1.0
- Designed to manage and compact long-running context while preserving important decisions and intermediate steps
- Suitable for extensive codebases, long document collections, and persistent agent sessions
Modalities
- Inputs: Text, images, audio, video, PDFs, and other documents
- Outputs: Text, including structured responses and JSON-schema-compatible data
- Can incorporate visual or document evidence into tool-assisted workflows
- Does not natively return generated audio, images, or video
Core Strengths
- Personal agents and multi-agent orchestration
- Long-horizon software engineering
- Computer-use workflows across multiple applications
- Multimodal analysis and visual-to-code tasks
- Function calling and external tool coordination
- Scientific and technical reasoning
Performance and Pricing
- Reported median output speed: approximately 114 tokens per second
- Reported median time to first token: approximately 21 seconds
- API input pricing in the supplied assessment: $1.25 per million tokens
- API output pricing: $4.25 per million tokens
- Cache-hit pricing: $0.15 per million tokens
Availability and Considerations
- Described in the supplied materials as being in public preview
- Availability may vary by region or platform
- The provided source does not confirm a specific Muse Spark 1.1 release date
- Early evidence emphasizes tool use and agentic workflows, while specialized coding-only models may still perform better on some static benchmarks
- Long reasoning tasks can have higher initial latency
- Important outputs should still be reviewed before code deployment, external actions, or high-stakes decisions
Simplify Technical Work, Research, and Daily Operations With Muse Spark 1.1
System debugging for software engineers and DevOps teams
Investigate distributed failures, implement targeted fixes, and validate changes across services, infrastructure, and automated tests.
Investigate this failing microservice, identify the root cause, implement the safest fix, and add regression tests with deployment notes.
"Multi-app automation for operations leaders
Coordinate recurring reporting, data collection, dashboard updates, and administrative work across connected tools and services.
Use the available tools to compile weekly operational metrics, flag anomalies, update the report, and document every completed action.
"Strategy development for product managers
Turn interviews, meeting notes, recordings, screenshots, and product documents into prioritized decisions and stakeholder-ready plans.
Convert these call notes, support themes, and Figma screenshots into a prioritized roadmap with evidence, dependencies, and success metrics.
"Multi-source analysis for strategy and data teams
Analyze extensive documents and exported data while keeping risks, assumptions, and unresolved questions visible.
Analyze these PDFs, CSV exports, and screenshots, then produce a concise opportunity brief with risks, assumptions, and recommendations.
"Ticket assessment for customer-operations teams
Review support conversations, logs, and interface screenshots to identify patterns and produce consistent responses or internal escalations.
Review these tickets, logs, and screenshots, identify recurring issues, and draft customer replies plus detailed engineering handoff notes.
"Agent orchestration for AI builders
Use the Muse Spark 1.1 chat as a primary coordinator or specialized subagent across custom skills, functions, and MCP-connected systems.
Coordinate the available tools and specialist agents to triage these issues, resolve safe cases, and return an auditable decision log.
"Repository migrations for engineering teams
Plan and execute framework upgrades, dependency changes, interface migrations, and large refactors without losing cross-file context.
Plan this framework migration, map affected modules, update the code incrementally, run tests, and summarize remaining compatibility risks.
"Document-led risk reviews for project owners
Combine policies, contracts, screenshots, and implementation plans into clear decisions with owners, timelines, and dependencies.
Review these project documents and screenshots, rank the major risks, and create an action plan with owners, deadlines, and dependencies.
"Muse Spark 1.1 vs. Grok 4.5, GPT-5.6, and Other AI Models
In the table below, you can see how different AI models found on Lorka AI stack up in terms of context handling, speed, and more.
| Models | Reasoning | Speed | Multimodality | Context | Ideal use cases |
|---|---|---|---|---|---|
Muse Spark 1.1 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Agentic coding, multi-app computer use, multimodal reasoning, and long-horizon workflow orchestration. |
Claude Opus 5 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Intricate software development, advanced logical analysis, corporate due diligence, and extended agentic operations. |
Claude Fable 5 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Advanced logical reasoning, intricate codebase architecture, and comprehensive analytical research. |
Claude Sonnet 5 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Optimized professional workflows, strategic planning, and high-ROI automated programming. |
Grok 4.5 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Accelerated automated programming, advanced technical troubleshooting, document generation, and the optimization of tool-driven workflows. |
Gemini 3.6 Flash | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Agentic workflows, sophisticated programming, and multimodal execution |
GPT-5.6 Sol | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Expert programming, digital defense, autonomous agents, and biotechnological workflows. |
GLM-5.2 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Automating systems, refactoring codebases, executing projects autonomously, and advancing the development of deep open-weight models. |
Kimi K3 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Engineering of extensive repositories, multi-step agent workflows, user interface and visual design, as well as systemic analysis. |
Muse Spark 1.1
Agentic coding, multi-app computer use, multimodal reasoning, and long-horizon workflow orchestration.
Claude Opus 5
Intricate software development, advanced logical analysis, corporate due diligence, and extended agentic operations.
Claude Fable 5
Advanced logical reasoning, intricate codebase architecture, and comprehensive analytical research.
Claude Sonnet 5
Optimized professional workflows, strategic planning, and high-ROI automated programming.
Grok 4.5
Accelerated automated programming, advanced technical troubleshooting, document generation, and the optimization of tool-driven workflows.
Gemini 3.6 Flash
Agentic workflows, sophisticated programming, and multimodal execution
GPT-5.6 Sol
Expert programming, digital defense, autonomous agents, and biotechnological workflows.
GLM-5.2
Automating systems, refactoring codebases, executing projects autonomously, and advancing the development of deep open-weight models.
Kimi K3
Engineering of extensive repositories, multi-step agent workflows, user interface and visual design, as well as systemic analysis.
Strengths and Limitations of Muse 1.1 and Top LLMs
Muse Spark 1.1
Multimodal agentic model with 1M context, strong tool use, and aggressive pricing.
Public preview, text-only outputs, and weaker performance on some static coding benchmarks.
Claude Sonnet 5
Cost-efficient agentic model with strong planning, coding, and 1M-context professional performance.
Trails frontier models on the hardest reasoning and long-horizon tasks.
Claude Opus 5
Delivers near-Fable performance in coding, reasoning, and agentic work at about half the cost.
Remains premium and closed, while lagging Fable 5 in advanced cyber and biology tasks.
Claude Fable 5
Mythos-class model with elite long-horizon reasoning, coding, and large-context performance.
Premium closed model with stricter safeguards and more frequent refusals or rerouting.
GPT-5.6 Sol
Frontier model for agentic coding, defensive cybersecurity, and deep reasoning modes.
Preview-limited access, tight safeguards, and less flexible pricing and availability.
GLM-5.2
Open-weight model with 1M context, strong coding, and self-hosting flexibility.
Text-only and less ecosystem-mature, requiring more integration and infrastructure work.
Kimi K3
Flagship model with 1M context, multimodal input, structured outputs, and strong coding.
Evolving controls and a less mature global ecosystem than OpenAI or Anthropic.
Kimi K2.6
Open-weight multimodal agentic model with strong coding, agent swarms, and self-hosting flexibility.
Smaller context, lower ceiling than Kimi K3, and greater deployment complexity.
How to Use Muse Spark 1.1 on Lorka
Try Muse Spark 1.1 on Lorka’s multi-model AI platform by following the steps below:
Select
Write
Use
Chat With Muse Spark 1.1 Today
Create your Lorka account now and start using Muse Spark 1.1 for agentic workflows, coding, multimodal analysis, and computer-use tasks in one unified workspace.
Muse Spark 1.1 Model FAQs
You can access Muse Spark 1.1 through the Meta Model API public preview or Meta AI’s Thinking mode. On Lorka AI, simply choose Muse Spark 1.1 from the model list in the AI chat and get started.