QwQ 32B vs DeepSeek R1 0528 vs DeepSeek V3 0324
Too close to call on our weighted score (DeepSeek R1 0528 53, QwQ 32B 53, DeepSeek V3 0324 52). The right pick depends on what you value most.
Alibaba (Qwen)QwQ 32B
53/100- ECI137.5
- Price$0.53 / $0.70
- Context131K
DeepSeekDeepSeek R1 0528
53/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
- DeepSeek
DeepSeek V3 0324
52/100- ECI135.9
- Price$0.275 / $1.12
- Context164K
Too close to call
It is close. Our weighted score puts them within a point (DeepSeek R1 0528 53/100, QwQ 32B 53/100, DeepSeek V3 0324 52/100), so choose by what matters most for your work: DeepSeek R1 0528 for raw capability and DeepSeek V3 0324 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek R1 0528Capabilities Index (ECI): DeepSeek R1 0528 141.2 · QwQ 32B 137.5 · DeepSeek V3 0324 135.9
- Lowest priceDeepSeek V3 0324DeepSeek V3 0324 $0.486 · QwQ 32B $0.573 · DeepSeek R1 0528 $0.932 per 1M tokens (3:1 blend)
- Longest contextDeepSeek R1 0528 and DeepSeek V3 0324DeepSeek R1 0528 163,840 · DeepSeek V3 0324 163,840 · QwQ 32B 131,072 tokens
- Widest inputsSame inputsQwQ 32B: Text · DeepSeek R1 0528: Text · DeepSeek V3 0324: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | QwQ 32B | DeepSeek R1 0528 | DeepSeek V3 0324 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 61 | 66 | 59 |
| Price | 25% | 59 | 49 | 62 |
| Inputs & features | 15% | 35 | 35 | 25 |
| Context window | 10% | 25 | 28 | 28 |
| Overall | 100% | 53/100 | 53/100 | 52/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | QwQ 32BAlibaba (Qwen) | DeepSeek R1 0528DeepSeek | DeepSeek V3 0324DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.5 | 141.2 (best) | 135.9 |
| ECI rank | #123 of 165 | #108 of 165 (best) | #128 of 165 |
| GPQA DiamondGraduate-level science questions | 65.3% | 76.3% (best) | 67.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 59.2% | 66.4% (best) | 37.8% |
| Price per million tokens | |||
| Input | $0.53 | $0.512 | $0.275 (best) |
| Output | $0.70 (best) | $2.19 | $1.12 |
| Cached input | — | — | — |
| Blended (3:1) | $0.573 | $0.932 | $0.486 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Median of 16 providers | Median of 13 providers |
| Limits | |||
| Context window | 131,072 tokens | 163,840 tokens (best) | 163,840 tokens (best) |
| Max output | 8,192 tokens | 32,768 tokens | 163,840 tokens (best) |
| 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 | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenMIT | Open |
| API model ID | — | — | — |
| API providers | 2 | 16 (best) | 13 |
| Released | Mar 5, 2025 | May 28, 2025 | Mar 24, 2025 |
| Knowledge cutoff | 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.
- QwQ 32B$6.70
- DeepSeek R1 0528$9.50
- DeepSeek V3 0324$4.99
Which should you choose?
Which is better: QwQ 32B, DeepSeek R1 0528 or DeepSeek V3 0324?
It is close. Our weighted score puts them within a point (DeepSeek R1 0528 53/100, QwQ 32B 53/100, DeepSeek V3 0324 52/100), so choose by what matters most for your work: DeepSeek R1 0528 for raw capability and DeepSeek V3 0324 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, QwQ 32B, DeepSeek R1 0528 or DeepSeek V3 0324?
DeepSeek V3 0324 is cheaper at $0.275 input / $1.12 output per million tokens (median across 13 API providers). QwQ 32B costs $0.53 input / $0.70 output per million tokens (median across 2 API providers); DeepSeek R1 0528 costs $0.512 input / $2.19 output per million tokens (median across 16 API providers). At a typical mix of three input tokens to one output token, that is $0.486 per million tokens for DeepSeek V3 0324 versus $0.573 for QwQ 32B (1.2× as much) and $0.932 for DeepSeek R1 0528 (1.9× as much).
Which scores higher on benchmarks?
DeepSeek R1 0528 scores higher on the Capabilities Index (ECI): DeepSeek R1 0528 141.2 (#108 of 165), QwQ 32B 137.5 (#123 of 165) and DeepSeek V3 0324 135.9 (#128 of 165). The confidence ranges of the top two overlap (139.0–142.9 vs 133.1–141.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek R1 0528 76.3%, DeepSeek V3 0324 67.6%, QwQ 32B 65.3%; OTIS Mock AIME 2024–2025 — DeepSeek R1 0528 66.4%, QwQ 32B 59.2%, DeepSeek V3 0324 37.8%.
Which is better for coding?
There are no published SWE-bench Verified results for QwQ 32B, DeepSeek R1 0528 and DeepSeek V3 0324 yet, so there is no like-for-like coding score. On overall capability, DeepSeek R1 0528 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
DeepSeek R1 0528 and DeepSeek V3 0324 have the largest context windows (163,840 and 163,840 tokens), against 131,072 for QwQ 32B. Maximum output per response: QwQ 32B up to 8,192, DeepSeek R1 0528 up to 32,768, DeepSeek V3 0324 up to 163,840 tokens.
Which can read images, PDFs, audio or video?
QwQ 32B accepts text; DeepSeek R1 0528 accepts text; DeepSeek V3 0324 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (MIT), so you can self-host them.
Which is newer?
DeepSeek R1 0528 is the newest, released May 28, 2025. DeepSeek V3 0324 came out Mar 24, 2025; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: 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 Nov 2, 2026.
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