QwQ is Alibaba’s open-weights reasoning model, released in late 2024 as part of the Qwen model family. It is a 32B parameter model trained specifically to reason through hard problems using extended chain-of-thought, and it achieved benchmark scores competitive with OpenAI o1-preview and DeepSeek R1 at the time of release. QwQ is notable for a few distinctive characteristics: it frequently second-guesses itself during reasoning, has a tendency to explore multiple approaches before settling on one, and produces very long thinking traces. This guide covers what QwQ is, how it compares to alternatives, and how to use it effectively.
What QwQ Stands For and the Model’s Character
QwQ stands for “Qwen with Questions” — a name that reflects the model’s reasoning style. Unlike DeepSeek R1, which tends to work through problems relatively efficiently, QwQ has a distinctive pattern of constant self-questioning. It will write out a reasoning step, then immediately ask “but wait, is that right?”, work through an alternative, potentially backtrack again, and sometimes cycle through this loop multiple times before converging on an answer. This behaviour makes QwQ’s thinking traces longer and more exploratory than R1’s. Whether this is a strength or a weakness depends on the task: for problems where the initial approach might be subtly wrong, QwQ’s scepticism helps it catch errors; for straightforward problems, it can feel verbose and slow.
Benchmark Performance
At the time of QwQ-32B’s release in late 2024, it scored competitively with the best reasoning models available. On MATH-500, it scored around 97%, matching DeepSeek R1 and exceeding o1-preview. On AIME 2024, it scored approximately 50%, below R1’s ~79% but comparable to o1-mini. On LiveCodeBench (coding), it performed well on structured algorithmic problems but sometimes over-complicated solutions. The overall picture: QwQ-32B is a strong reasoning model, clearly ahead of standard 32B models on hard reasoning tasks, but behind DeepSeek R1 on the hardest mathematical problems. It is best understood as a high-quality alternative in the reasoning model space rather than a direct competitor for the top position.
QwQ-32B vs DeepSeek R1-Distill-32B
The most natural comparison for most practitioners is QwQ-32B versus R1-Distill-32B, since both are 32B open-weights reasoning models that run on similar hardware. R1-Distill-32B generally outperforms QwQ-32B on pure mathematical reasoning benchmarks — it benefits from being distilled from the much larger 671B R1 model. QwQ-32B tends to be more conversational and handles instruction-following more naturally, making it sometimes preferable for tasks where the reasoning needs to integrate with multi-turn conversation or tool use. QwQ also has better multilingual reasoning capability, particularly for Chinese, which matters for applications targeting Chinese-language users. In practice, both are capable enough that the choice often comes down to which fits your serving setup and which performs better on your specific evaluation set.
Figure 1 — QwQ-32B vs DeepSeek R1-Distill-32B: strengths compared
Running QwQ Locally with Ollama
QwQ-32B is available on Ollama and runs identically to R1-Distill-32B in terms of hardware requirements — a GPU with 24GB VRAM at 4-bit quantisation. The model ID on Ollama is qwq:
ollama run qwq # pulls QwQ-32B and starts a session
ollama run qwq:32b-q4_K_M # explicit 4-bit version
# API access via Ollama's OpenAI-compatible endpoint
curl http://localhost:11434/v1/chat/completions -H "Content-Type: application/json" -d '{"model":"qwq","messages":[{"role":"user","content":"What is 23 × 47 using long multiplication?"}]}'QwQ’s Distinctive Reasoning Patterns
Spending time reading QwQ’s thinking traces reveals patterns worth understanding. QwQ frequently switches approaches mid-reasoning — it will start solving a problem algebraically, then say “actually, let me try a different approach” and switch to geometric reasoning. This is not failure; it is often how correct solutions are found on hard problems. QwQ also has a tendency to verify by working backwards: after deriving an answer, it often plugs it back into the original problem to confirm. For users reviewing QwQ’s reasoning, these patterns are worth knowing because they can look like uncertainty or confusion when they are actually productive exploration. The thinking process is more verbose than R1 but the final answers are often just as accurate on problems both models can handle.
QwQ-32B-Preview vs QwQ-32B
The original release was labelled QwQ-32B-Preview, indicating it was a research preview rather than a production-ready model. Alibaba’s notes acknowledged that it had known weaknesses in language mixing (sometimes switching between English and Chinese mid-response), recursive loops where it would revisit the same reasoning step repeatedly without progress, and inconsistent performance on common-sense reasoning tasks. A subsequent updated version addressed several of these issues. When running QwQ, check the model version — the non-preview release is substantially more reliable for production use and less prone to the looping behaviour that made the preview frustrating for interactive applications.
When to Choose QwQ Over Alternatives
QwQ is a good choice when you want a 32B local reasoning model and prefer a more exploratory reasoning style, when your application has significant Chinese-language input or output, or when you have found through evaluation that QwQ outperforms R1-Distill-32B on your specific task. It is also a reasonable choice simply for variety — running evaluations on both QwQ and R1-Distill-32B and comparing outputs is easy and can reveal which model handles your use case’s specific structure better. For most English-language mathematical and coding tasks, R1-Distill-32B has a slight edge; for more conversational reasoning and Chinese-language tasks, QwQ is competitive or superior.
QwQ in the Broader Qwen Ecosystem
QwQ is part of Alibaba’s Qwen model family, which spans a range of sizes from Qwen-0.5B to Qwen-72B across standard and instruction-tuned variants. The QwQ series is the reasoning branch of this family. The naming convention “QwQ” carries philosophical connotations in Chinese culture — “wèn” (问) means “to question,” and the doubled form suggests persistent inquiry, which aligns with the model’s reasoning style. The international release used the romanised abbreviation QwQ, which has become the recognised name for the model series globally. Alibaba has invested heavily in the Qwen ecosystem — the base models have strong multilingual performance across East Asian languages, and the fine-tuned variants perform well on coding benchmarks. The Qwen-2.5 architecture underpins the DeepSeek R1 distilled 14B and 32B models as well, making the QwQ and R1-Distill models architectural cousins despite being developed by different organisations. This architectural similarity means knowledge about prompt formatting, context handling, and generation parameters for one often transfers to the other.
QwQ-32B is a capable open-weights reasoning model that sits in the same tier as DeepSeek R1-Distill-32B — genuinely better than standard models of equivalent size on hard reasoning tasks, with a more exploratory and conversational reasoning style that some users prefer. The hardware requirements are identical to R1-Distill-32B. Try both on your specific evaluation set: if they perform similarly, the choice is a matter of preference; if one consistently outperforms the other on your tasks, you have your answer.
Figure 1 — QwQ-32B vs DeepSeek R1-Distill-32B: where each wins
Practical Tips for Getting the Best from QwQ
QwQ’s exploratory reasoning style responds well to a few prompt adjustments. Giving it explicit permission to think at length — “take your time and work through this carefully” — tends to improve output quality on hard problems. Specifying the format you want for the final answer helps cut through the verbose reasoning: “show your reasoning, then give a concise final answer in a box” or “at the end, state only the answer on its own line.” For problems where QwQ tends to loop — revisiting the same reasoning step multiple times — adding “if you find yourself going in circles, step back and try a completely different approach” in the prompt can break the pattern. For tasks requiring concise responses rather than extended reasoning, QwQ may not be the right tool — a standard Qwen-2.5 or similar instruct model will be faster and produce tighter output without the reasoning overhead.
The fact that two companies — DeepSeek and Alibaba — released competitive open-weights reasoning models within weeks of each other in late 2024 and early 2025 tells you something important about the direction of the field. Reasoning capability is converging toward being a commodity available in open-weights form. QwQ was an early signal of that convergence, and the practitioners who evaluated it carefully at the time built useful intuition about reasoning model behaviour that transferred directly to working with R1 and subsequent models.