What are Open Source LLMs for Planning Tasks?
Open source LLMs for planning tasks are specialized Large Language Models designed to excel at complex reasoning, task decomposition, sequential planning, and agent-based workflows. Using advanced architectures including reinforcement learning and Mixture-of-Experts designs, they can break down complex goals into actionable steps, reason through multi-stage processes, and integrate with external tools to execute plans. These models foster collaboration, accelerate innovation in autonomous systems, and democratize access to powerful planning capabilities, enabling applications from software engineering agents to strategic business planning and autonomous workflow orchestration.
DeepSeek-R1
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness.
DeepSeek-R1: Elite Reasoning and Planning Powerhouse
DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) with 671B total parameters using a Mixture-of-Experts architecture and 164K context length. It addresses the issues of repetition and readability while incorporating cold-start data to optimize reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks—making it exceptional for complex planning scenarios that require deep multi-step reasoning, logical decomposition, and strategic task orchestration. Through carefully designed RL training methods, it has enhanced overall effectiveness in planning workflows, software engineering tasks, and autonomous agent applications.
Pros
- Elite reasoning capabilities comparable to OpenAI-o1.
- Massive 671B parameters with MoE efficiency.
- 164K context length for complex planning scenarios.
Cons
- Higher computational requirements due to model size.
- Premium pricing tier compared to smaller models.
Why We Love It
- It delivers state-of-the-art reasoning and planning capabilities through reinforcement learning, making it the go-to model for complex autonomous workflows and strategic task planning.
Qwen3-30B-A3B-Thinking-2507
Qwen3-30B-A3B-Thinking-2507 is the latest thinking model in the Qwen3 series, released by Alibaba's Qwen team. As a Mixture-of-Experts (MoE) model with 30.5 billion total parameters and 3.3 billion active parameters, it is focused on enhancing capabilities for complex tasks.
Qwen3-30B-A3B-Thinking-2507: Efficient Planning with Thinking Mode
Qwen3-30B-A3B-Thinking-2507 is the latest thinking model in the Qwen3 series with a Mixture-of-Experts (MoE) architecture featuring 30.5 billion total parameters and 3.3 billion active parameters. The model demonstrates significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise. It excels in planning tasks through its specialized 'thinking mode' that tackles highly complex problems through step-by-step reasoning and agentic capabilities. With native 256K context support (extendable to 1M tokens), it's ideal for long-horizon planning, tool integration, and sequential task execution.
Pros
- Specialized thinking mode for step-by-step planning.
- Efficient MoE architecture with only 3.3B active parameters.
- Extended 256K context (up to 1M tokens).
Cons
- Smaller parameter count than flagship models.
- Thinking mode may increase inference latency.
Why We Love It
- It offers an optimal balance of efficiency and planning capability through dedicated thinking mode, making it perfect for complex multi-step planning tasks without the computational overhead of larger models.
GLM-4.5-Air
GLM-4.5-Air is a foundational model specifically designed for AI agent applications, built on a Mixture-of-Experts (MoE) architecture. It has been extensively optimized for tool use, web browsing, software development, and front-end development, enabling seamless integration with coding agents such as Claude Code and Roo Code.
GLM-4.5-Air: Agent-Optimized Planning Model
GLM-4.5-Air is a foundational model specifically designed for AI agent applications and planning tasks, built on a Mixture-of-Experts (MoE) architecture with 106B total parameters and 12B active parameters. It has been extensively optimized for tool use, web browsing, software development, and front-end development, making it exceptional for planning workflows that require autonomous agent behavior. The model employs a hybrid reasoning approach, allowing it to adapt effectively to a wide range of planning scenarios—from complex reasoning tasks to everyday workflow automation. Its native 131K context length supports comprehensive planning documents and long-horizon task sequences.
Pros
- Purpose-built for AI agent and planning workflows.
- Extensive optimization for tool use and integration.
- Hybrid reasoning for flexible planning approaches.
Cons
- Not as large as flagship reasoning models.
- May require fine-tuning for highly specialized planning domains.
Why We Love It
- It's specifically engineered for agent-based planning with exceptional tool integration capabilities, making it the ideal choice for autonomous workflow orchestration and software development planning tasks.
Planning LLM Comparison
In this table, we compare 2025's leading open source LLMs for planning tasks, each with unique strengths. For maximum reasoning depth and complex strategic planning, DeepSeek-R1 leads with elite RL-trained capabilities. For efficient step-by-step planning with thinking mode, Qwen3-30B-A3B-Thinking-2507 offers optimal balance. For agent-based workflows with tool integration, GLM-4.5-Air excels in autonomous planning. This side-by-side view helps you choose the right model for your specific planning and reasoning requirements.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Planning Strength |
|---|---|---|---|---|---|
| 1 | DeepSeek-R1 | deepseek-ai | Reasoning | $2.18/M Output | $0.5/M Input | Elite multi-step reasoning |
| 2 | Qwen3-30B-A3B-Thinking-2507 | Qwen | Reasoning | $0.4/M Output | $0.1/M Input | Efficient thinking mode planning |
| 3 | GLM-4.5-Air | zai | Reasoning & Agent | $0.86/M Output | $0.14/M Input | Agent-optimized workflows |
Frequently Asked Questions
Our top three picks for 2025 are DeepSeek-R1, Qwen3-30B-A3B-Thinking-2507, and GLM-4.5-Air. Each of these models stood out for their exceptional reasoning capabilities, planning optimization, and unique approaches to solving complex multi-step planning challenges, from strategic task decomposition to autonomous agent workflows.
Our in-depth analysis shows several leaders for different planning needs. DeepSeek-R1 is the top choice for complex strategic planning requiring deep reasoning and long-horizon task sequences. Qwen3-30B-A3B-Thinking-2507 excels at step-by-step planning with efficient MoE architecture and thinking mode. GLM-4.5-Air is ideal for autonomous agent workflows requiring extensive tool integration and software development planning.