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We’re excited to announce the availability of Qwen QwQ-32B on Chat-O! This powerful model, developed by Qwen, leverages Reinforcement Learning (RL) to achieve performance comparable to the much larger DeepSeek-R1 (671 billion parameters). As usual, the model is integrated on Chat-O on the same day that it was announced! The original announcement by Qwen can be seen here.
Chat-O is now open to everyone! Sign up here and receive 1000 free credits to try Chat-O today!
Qwen’s research demonstrates the effectiveness of RL in enhancing large language models. QwQ-32B showcases this, achieving remarkable performance through RL applied to a robust foundation model. Integrated agent-related capabilities enable critical thinking, tool utilization, and adaptation based on environmental feedback.
As Qwen states:
Our research explores the scalability of Reinforcement Learning (RL) and its impact on enhancing the intelligence of large language models. We are excited to introduce QwQ-32B, a model with 32 billion parameters that achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated). This remarkable outcome underscores the effectiveness of RL when applied to robust foundation models pretrained on extensive world knowledge. Furthermore, we have integrated agent-related capabilities into the reasoning model, enabling it to think critically while utilizing tools and adapting its reasoning based on environmental feedback. These advancements not only demonstrate the transformative potential of RL but also pave the way for further innovations in the pursuit of artificial general intelligence.
QwQ-32B uses a dense transformer architecture with 32 billion active parameters. Unlike mixture-of-experts (MoE) models that activate only a subset of parameters per token, QwQ-32B’s dense design means all 32 billion parameters are used for every forward pass. This approach trades theoretical efficiency for more predictable and consistent reasoning behavior.
The model was trained using a multi-stage RL pipeline. Starting from a pretrained foundation model, Qwen applied RL with both code-based and math-based reward signals. The training used a variant of GRPO (Group Relative Policy Optimization), which has become a standard approach for reasoning-oriented fine-tuning.
| Benchmark | QwQ-32B | DeepSeek-R1 (671B) | GPT-4.5 |
|---|---|---|---|
| AIME 2024 | 79.5% | 79.2% | 76.8% |
| MATH-500 | 96.3% | 95.8% | 94.2% |
| LiveCodeBench | 61.4% | 60.8% | 58.1% |
| MMLU-Pro | 75.2% | 74.9% | 74.1% |
These numbers highlight a crucial insight: with sufficient RL training, a 32B dense model can match or exceed a 671B MoE model on reasoning benchmarks. This has profound implications for deployment cost and inference speed.
What sets QwQ-32B apart from earlier reasoning models is its built-in agent capabilities. The model can:
These capabilities make QwQ-32B particularly effective for tasks that combine reasoning with external knowledge retrieval or computation.
On Chat-O, QwQ-32B is available in the Balanced tier. This placement reflects the model’s excellent price-to-performance ratio. The Balanced tier is our middle option, designed for users who need more than lightweight responses but do not require the full power of our top-tier models.
Since QwQ-32B’s launch, the AI landscape has evolved significantly. Newer models like GLM-5.2 and Kimi K3 have pushed the boundaries of context length and parameter count. However, QwQ-32B remains a standout choice for users who want strong reasoning performance without paying for Power tier pricing. It is especially competitive against Kimi K2.6 and GLM-4.7, both of which occupy a similar price-performance niche.
QwQ-32B excels in several specific scenarios:
The model’s RL training focused heavily on math and code, making it ideal for solving complex mathematical problems, proving theorems, and working through scientific literature.
Tasks that require breaking down a problem into sequential logical steps benefit from QwQ-32B’s RL-trained reasoning chain. This includes puzzles, logic problems, and analytical writing.
Unlike pure code models that produce output directly, QwQ-32B reasons about the problem before writing code. This results in more robust solutions with better error handling.
The model’s ability to show its reasoning makes it an excellent tutor. Students can follow the step-by-step thinking process to understand not just the answer but the methodology.
QwQ-32B is not without trade-offs. Its relatively small context window (32K tokens) means it cannot handle the million-token workloads that models like GLM-5.2 or Kimi K3 manage. It also lacks multimodal capabilities, processing text only. For tasks requiring image understanding or very long document analysis, consider one of our Power tier models.
Additionally, the dense architecture means inference cost scales linearly with parameter count. While 32B is modest by today’s standards, newer models that use MoE architectures can offer more total parameters with similar per-token costs.
As of July 2026, the Balanced tier on Chat-O includes models like Kimi K2.6, GLM-4.7, Kimi K2.7 Code, and QwQ-32B. Among these, QwQ-32B stands out for pure reasoning capability, while Kimi K2.6 and GLM-4.7 offer stronger general knowledge and larger context windows.
For the latest on our model lineup, see our overview of the Z-AI GLM family and the complete Kimi family on Chat-O.
Qwen QwQ-32B is available now in the Balanced tier. New users get 1,000 free credits to try it. Existing users can select it from the model picker in any chat session. No configuration, no API keys needed.