Best Qwen Models for OpenClaw — Alibaba's Qwen 3 Series Ranked

The best Qwen model for OpenClaw depends on whether you run locally or through an API. For local deployment via Ollama, Qwen3.5 9B offers the best balance of capability and hardware requirements at 6.6 GB with a 256K context window. For cloud API access through DashScope, Qwen3-235B-A22B is Alibaba's flagship — scoring 85.7 on AIME'24 and 70.7 on LiveCodeBench v5 while costing $0.26 per million input tokens.

Part of The Complete Guide to OpenClaw — the full reference covering setup, security, memory, and operations.

Qwen 3 Overview for OpenClaw

Qwen 3 is Alibaba Cloud's third-generation large language model family, released under the Apache 2.0 license. The release includes 8 models: 6 dense architectures ranging from 0.6B to 32B parameters, plus 2 Mixture-of-Experts (MoE) models — Qwen3-30B-A3B and Qwen3-235B-A22B.

What makes Qwen 3 particularly relevant for OpenClaw operators is the breadth of the lineup. You can run the same model family on a budget laptop (4B) and on a cloud API (235B), which means you can prototype locally and scale to production without switching model families or rewriting prompts.

Alibaba also ships the newer Qwen3.5 family through Ollama, which adds sizes from 0.8B to 122B with a consistent 256K context window. The Qwen3.5 models are generally recommended for new local deployments because they represent the latest training improvements, but the Qwen3 lineup remains relevant for cloud API access through DashScope (Alibaba Cloud Model Studio).

Model Comparison by Size

Qwen3 models scale predictably — larger models score higher on benchmarks but require more hardware or cost more through APIs.

The flagship Qwen3-235B-A22B achieves a CodeForces Elo rating of 2,056 — higher than DeepSeek-R1 and Gemini 2.5 Pro on that benchmark. It also scores 81.5 on AIME'25 and 70.8 on BFCL v3 (function calling), which matters directly for OpenClaw agent tool use.

At the other end, Qwen3-4B delivers surprisingly strong results for its size. According to Alibaba's benchmarks, it rivals Qwen2.5-7B-Base performance while requiring only ~3.5 GB of RAM — making it viable for testing OpenClaw on older or resource-constrained hardware.

Local vs Cloud Setup

Qwen gives OpenClaw operators two distinct deployment paths, and the right choice depends on your hardware, privacy requirements, and workload complexity.

Local via Ollama

Running Qwen locally through Ollama gives you full privacy — no data leaves your machine — and eliminates per-token costs. The tradeoff is hardware requirements and the practical limits of smaller models on complex agent tasks.

For most local OpenClaw operators, Qwen3.5 9B is the recommended starting point. It runs on 16 GB hardware, supports 256K context, and handles routine OpenClaw workflows — content generation, summarization, basic coding, email drafting — without dropping to a toy model.

If you have stronger hardware (24+ GB VRAM), Qwen3.5 27B is a significant step up in capability while staying inside the same model family. See our Ollama models guide for the full comparison.

Cloud via DashScope API

DashScope is Alibaba's native API platform (also called Model Studio). It provides the most complete set of features and parameters for Qwen models, including batch invocation at 50% of real-time pricing.

DashScope also exposes an OpenAI-compatible endpoint, which means OpenClaw can connect without custom integration. New accounts in the international region get a free quota of 1 million input tokens and 1 million output tokens, valid for 90 days.

Use the cloud path when you need the full 235B model, when your workload requires long complex agent chains, or when local hardware cannot sustain 64K+ context cleanly.

Ollama Configuration for OpenClaw

Qwen models are first-class citizens in the Ollama library. Setting up Qwen for OpenClaw through Ollama takes three steps.

# Step 1: Pull the model
ollama pull qwen3.5:9b

# Step 2: Set context to at least 64K for OpenClaw
OLLAMA_CONTEXT_LENGTH=64000 ollama serve

# Step 3: Launch OpenClaw with the model
ollama launch openclaw --model qwen3.5:9b

The context setting matters as much as the model choice. Ollama's documentation recommends at least 64,000 tokens for agent workflows, and OpenClaw falls squarely into that category. Leaving context at the default (which varies by VRAM tier) is the most common mistake operators make with local Qwen models.

To verify your context allocation is correct:

ollama ps

For operators who want to switch between Qwen sizes without restarting:

# Budget setup
ollama run qwen3.5:9b

# Higher capability
ollama run qwen3.5:27b

# Or use the Qwen3 dense model directly
ollama run qwen3:8b

For GPU optimization and VRAM management with Qwen models, see the GPU optimization guide.

Cost Comparison

Qwen3 API pricing through DashScope is competitive with other Chinese model providers and significantly cheaper than Western frontier models.

DashScope also offers tiered pricing for some models, where the cost per token changes based on the number of input tokens per request. For high-volume workloads, batch invocation cuts costs by 50% compared to real-time inference. Free-tier new accounts get 1M input + 1M output tokens for 90 days, which is enough to validate whether Qwen3 works for your OpenClaw setup before committing.

The local vs cloud cost tradeoff is worth calculating for your specific workload. Running Qwen3.5 9B locally is free at the token level but costs electricity and requires dedicated hardware. DashScope's API pricing is low enough that many operators find the convenience worth it until their volume justifies a local setup.

Limitations and Tradeoffs

Qwen is one of the most flexible model families for OpenClaw, but flexibility does not mean it is the best at everything.

• Local model ceiling: Even Qwen3.5 27B, the largest practical local option for most hardware, will struggle with the most complex multi-step agent chains. If your OpenClaw usage involves serious agentic coding or long-horizon planning, the cloud 235B model is a meaningful step up.
• Ollama context tradeoffs: Running at 64K+ context on a budget machine means something has to give — usually speed or available memory for other applications. Do not assume a 9B model at 64K context will feel fast on 16 GB of RAM.
• Qwen3.5 GGUF limitations: As of April 2026, Qwen3.5 GGUF models in Ollama do not support vision capabilities due to separate mmproj files. If you need multimodal input, use the API or an alternative backend like llama.cpp.
• DashScope regional availability: DashScope's international region (Singapore endpoint) may have higher latency for operators in the Americas or Europe compared to US-based providers.
• Dense vs MoE confusion: Qwen3 ships both dense (4B, 8B, 32B) and MoE (30B-A3B, 235B-A22B) variants. Make sure you know which type you are using — a 30B MoE with 3B active parameters behaves very differently from a 32B dense model, even though the total parameter counts look similar.

When NOT to use Qwen for OpenClaw: if you need the absolute highest English-language agentic performance (Claude and GPT-5 still lead), if you need guaranteed low-latency API responses from a US-based provider, or if your compliance requirements restrict models trained with Chinese-origin data.

Related Guides

• Qwen3 8B OpenClaw Local Guide
• OpenClaw Qwen Model Studio Migration
• Best Ollama Models for OpenClaw
• Ollama vs OpenRouter for OpenClaw

FAQ

What is the best Qwen model for OpenClaw in 2026?

For local use, Qwen3.5 9B is the best starting point — it runs on 16 GB hardware with 256K context and handles most routine OpenClaw workflows. For cloud API use, Qwen3-235B-A22B is the flagship, scoring 70.7 on LiveCodeBench v5 and 2,056 Elo on CodeForces at $0.26 per million input tokens.

Can I run Qwen3 locally for OpenClaw with Ollama?

Yes. Qwen3 and Qwen3.5 models are available in the Ollama library. Pull the model with ollama pull qwen3.5:9b, set context to at least 64K with OLLAMA_CONTEXT_LENGTH=64000 ollama serve, and launch OpenClaw with ollama launch openclaw --model qwen3.5:9b.

How much does Qwen3-235B cost through DashScope?

Qwen3-235B-A22B costs approximately $0.26 per million input tokens and $0.90 per million output tokens through DashScope. Batch invocation is available at 50% of the real-time price. New accounts get a free quota of 1M input + 1M output tokens for 90 days.

What is the difference between Qwen3 and Qwen3.5 for OpenClaw?

Qwen3.5 is the newer generation with training improvements and a consistent 256K context window across sizes. Qwen3 models have 128K context. For new local deployments through Ollama, Qwen3.5 is generally recommended. For cloud API access, Qwen3-235B-A22B remains the strongest option through DashScope.

Should I use Qwen or GLM for OpenClaw?

Qwen's advantage is flexibility — you can run the same model family locally and through APIs, with sizes from 4B to 235B. GLM's advantage is bilingual Chinese-English fluency and a free Flash model. If you prioritize local deployment flexibility and open-source licensing, Qwen is the stronger pick. If you need strong Chinese language support or a free API model, GLM has the edge.