DeepSeek-R1-Distill-Qwen-32B vs DeepSeek-R1 vs QwQ 32B
Too close to call on our weighted score (DeepSeek-R1 53, QwQ 32B 52, DeepSeek-R1-Distill-Qwen-32B 47). The right pick depends on what you value most.
DeepSeekDeepSeek-R1-Distill-Qwen-32B
47/100- ECI137.4
- Price—
- Context131K
- DeepSeek
DeepSeek-R1
53/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
Alibaba (Qwen)QwQ 32B
52/100- ECI137.6
- Price$0.66 / $1.00
- Context131K
Too close to call
It is close. Our weighted score puts them within 1 points (DeepSeek-R1 53/100, QwQ 32B 52/100, DeepSeek-R1-Distill-Qwen-32B 47/100), so choose by what matters most for your work: DeepSeek-R1 for raw capability and QwQ 32B on price. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityDeepSeek-R1Capabilities Index (ECI): DeepSeek-R1 139.0 · QwQ 32B 137.6 · DeepSeek-R1-Distill-Qwen-32B 137.4
- Lowest priceQwQ 32BQwQ 32B $0.745 · DeepSeek-R1 $1.18 per 1M tokens (3:1 blend) · DeepSeek-R1-Distill-Qwen-32B unpriced
- Longest contextDeepSeek-R1-Distill-Qwen-32B and QwQ 32BDeepSeek-R1-Distill-Qwen-32B 131,072 · QwQ 32B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsDeepSeek-R1-Distill-Qwen-32B: Text · DeepSeek-R1: Text · QwQ 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-R1-Distill-Qwen-32B | DeepSeek-R1 | QwQ 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 62 | 64 | 62 |
| Inputs & features | 20% | 10 | 35 | 35 |
| Context window | 13% | 24 | 24 | 24 |
| Overall | 100% | 47/100 | 53/100 | 52/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1-Distill-Qwen-32BDeepSeek | DeepSeek-R1DeepSeek | QwQ 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.4 | 139.0 (best) | 137.6 |
| ECI rank | #110 of 148 | #104 of 148 (best) | #109 of 148 |
| GPQA DiamondGraduate-level science questions | 64.1% | 71.7% (best) | 65.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 55.6% | 53.3% | 59.2% (best) |
| Price per million tokens | |||
| Input | — | $0.70 | $0.66 (best) |
| Output | — | $2.60 | $1.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $1.18 | $0.745 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Median of 11 providers | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 32,768 tokens (best) | 32,768 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | — | — |
| API providers | — | 12 (best) | 1 |
| Released | Jan 20, 2025 | Jan 20, 2025 | Mar 5, 2025 |
| Knowledge cutoff | — | Jul 2024 | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek-R1-Distill-Qwen-32B—
- DeepSeek-R1$12.20
- QwQ 32B$8.60
Which should you choose?
Which is better: DeepSeek-R1-Distill-Qwen-32B, DeepSeek-R1 or QwQ 32B?
It is close. Our weighted score puts them within 1 points (DeepSeek-R1 53/100, QwQ 32B 52/100, DeepSeek-R1-Distill-Qwen-32B 47/100), so choose by what matters most for your work: DeepSeek-R1 for raw capability and QwQ 32B on price. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, DeepSeek-R1-Distill-Qwen-32B, DeepSeek-R1 or QwQ 32B?
QwQ 32B is cheaper at $0.66 input / $1.00 output per million tokens (median across 1 API provider). DeepSeek-R1 costs $0.70 input / $2.60 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.745 per million tokens for QwQ 32B versus $1.18 for DeepSeek-R1 (1.6× as much). DeepSeek-R1-Distill-Qwen-32B has no published per-token price.
Which scores higher on benchmarks?
DeepSeek-R1 scores higher on the Capabilities Index (ECI): DeepSeek-R1 139.0 (#104 of 148), QwQ 32B 137.6 (#109 of 148) and DeepSeek-R1-Distill-Qwen-32B 137.4 (#110 of 148). The confidence ranges of the top two overlap (136.2–140.4 vs 133.1–141.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek-R1 71.7%, QwQ 32B 65.3%, DeepSeek-R1-Distill-Qwen-32B 64.1%; OTIS Mock AIME 2024–2025 — QwQ 32B 59.2%, DeepSeek-R1-Distill-Qwen-32B 55.6%, DeepSeek-R1 53.3%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1-Distill-Qwen-32B, DeepSeek-R1 and QwQ 32B yet, so there is no like-for-like coding score. On overall capability, DeepSeek-R1 leads, which tends to carry over to coding, but test on your own codebase. Note that DeepSeek-R1-Distill-Qwen-32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
DeepSeek-R1-Distill-Qwen-32B and QwQ 32B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1-Distill-Qwen-32B up to 32,768, DeepSeek-R1 up to 32,768, QwQ 32B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1-Distill-Qwen-32B accepts text; DeepSeek-R1 accepts text; QwQ 32B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
QwQ 32B is the newest, released Mar 5, 2025. DeepSeek-R1-Distill-Qwen-32B came out Jan 20, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, QwQ 32B Apr 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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