Overview
Mellum by JetBrains is a family of fast, open-source language models designed for ultra-low-latency inference and high-performance coding tasks. The latest model, Mellum2, is a 12B-parameter mixture-of-experts (MoE) model that delivers strong coding quality while halving inference costs. It is built for real-time workflows, from code completion to broader language tasks, and can be deployed locally or on the cloud.
Deep Work Plan is an open-source (MIT) methodology and agent harness that transforms any repository into a structured environment for AI coding agents. It uses spec-driven development: the plan is the durable source of truth, with atomic tasks, acceptance criteria, and validation gates. It is agent-agnostic, works with any coding agent (Claude Code, Cursor, Codex, etc.), and ensures long-horizon work survives context resets.
Feature Comparison
| Feature | Mellum by JetBrains | Deep Work Plan |
|---|---|---|
| Primary Function | Family of fast language models (LLMs) optimized for low-latency inference and coding tasks | Spec-driven development methodology and agent harness that turns any repo into a structured environment for AI agents |
| Architecture | Mixture-of-experts (MoE) with 12B parameters; compact KV-cache footprint | Markdown-based spec files, AGENTS.md, .agents/ directory, and DWP skill; no daemon or external state |
| Deployment | Local (Ollama, JetBrains AI Assistant) or cloud; GPU required (H100/H200/B200/B300) | Any repository; works with any agent (Claude Code, Cursor, Codex, etc.); no special hardware |
| Target Users | AI/ML engineers, researchers, developers needing fast, cost-efficient inference | Developers and teams using AI coding agents for long-horizon tasks (migrations, refactors, new subsystems) |
| Open Source | Yes, Apache 2.0 license; open weights on Hugging Face | Yes, MIT license; open methodology and skill |
| Latency | Ultra-low latency (milliseconds); designed for real-time workflows | N/A (methodology, not a model); latency depends on the agent/model used |
| Context Handling | N/A (model-level); compact KV-cache for high concurrency | Durable plan survives context resets; any agent can resume where last left off |
| Customization | Fine-tunable; supports local and cloud deployment | Adapts to any repo's stack; generates AGENTS.md, docs, and per-module docs; extensible via skills |
| Validation | N/A (model-level); performance benchmarks available | Built-in verification (dwp-verify) produces pass/fail report against specification |
Pricing
Mellum is open-source under the Apache 2.0 license and free to use. The main costs come from the hardware required for deployment: Mellum2 requires a GPU (e.g., H100, H200, B200, B300) for optimal performance. Cloud deployment costs vary by provider.
Deep Work Plan is open-source under the MIT license and completely free. There are no direct costs for the methodology itself. Users only pay for the AI agent services they choose to use (e.g., Claude Code, Cursor, OpenAI Codex, etc.).
Pros and Cons
Mellum by JetBrains
Pros:
- Ultra-low latency and high throughput, often twice as fast as similar-sized models
- Strong coding quality with lower inference costs due to MoE architecture
- Open-source with Apache 2.0 license; fine-tunable and deployable locally or on cloud
- Compact KV-cache footprint enables high concurrency on a single GPU
- Supports both natural language and programming tasks
Cons:
- Requires GPU hardware (H100/H200/B200/B300) for optimal performance
- Limited to 12B parameters; may not match larger models on complex reasoning tasks
- Primarily focused on coding and math; general knowledge may be narrower
Deep Work Plan
Pros:
- Agent-agnostic; works with any coding agent (Claude Code, Cursor, Codex, etc.)
- Durable plans survive context resets; long-horizon tasks can be resumed by any agent
- Adapts to any repository's stack automatically; no manual configuration
- Built-in verification ensures conformance to the specification
- Open-source (MIT) with no lock-in; methodology is reusable across projects
Cons:
- Not a model; requires an external AI agent to execute plans
- Onboarding and setup may be complex for non-standard repositories
- Effectiveness depends on the underlying agent's capabilities and context window
Verdict
Choose Mellum if you need a fast, cost-efficient language model for real-time inference and coding tasks, especially if you have GPU infrastructure. Choose Deep Work Plan if you want to structure long-horizon AI agent work across any repository, ensuring consistency and resumability without model lock-in. They are complementary: Mellum can be the model that powers the agent executing a Deep Work Plan.
Deep Work Plan
Mellum by JetBrains