DeepSeek V3 0324 vs DeepSeek R1 0528 vs QwQ 32B
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.
DeepSeekDeepSeek V3 0324
52/100- ECI135.9
- Price$0.275 / $1.12
- Context164K
- DeepSeek
DeepSeek R1 0528
53/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
Alibaba (Qwen)QwQ 32B
53/100- ECI137.5
- Price$0.53 / $0.70
- Context131K
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 V3 0324 and DeepSeek R1 0528DeepSeek V3 0324 163,840 · DeepSeek R1 0528 163,840 · QwQ 32B 131,072 tokens
- Widest inputsSame inputsDeepSeek V3 0324: Text · DeepSeek R1 0528: Text · QwQ 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek V3 0324 | DeepSeek R1 0528 | QwQ 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 59 | 66 | 61 |
| Price | 25% | 62 | 49 | 59 |
| Inputs & features | 15% | 25 | 35 | 35 |
| Context window | 10% | 28 | 28 | 25 |
| Overall | 100% | 52/100 | 53/100 | 53/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek V3 0324DeepSeek | DeepSeek R1 0528DeepSeek | QwQ 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 135.9 | 141.2 (best) | 137.5 |
| ECI rank | #128 of 165 | #108 of 165 (best) | #123 of 165 |
| GPQA DiamondGraduate-level science questions | 67.6% | 76.3% (best) | 65.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 37.8% | 66.4% (best) | 59.2% |
| Price per million tokens | |||
| Input | $0.275 (best) | $0.512 | $0.53 |
| Output | $1.12 | $2.19 | $0.70 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.486 (best) | $0.932 | $0.573 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 13 providers | Median of 16 providers | Median of 2 providers |
| Limits | |||
| Context window | 163,840 tokens (best) | 163,840 tokens (best) | 131,072 tokens |
| Max output | 163,840 tokens (best) | 32,768 tokens | 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 | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenMIT | Open |
| API model ID | — | — | — |
| API providers | 13 | 16 (best) | 2 |
| Released | Mar 24, 2025 | May 28, 2025 | Mar 5, 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.
- DeepSeek V3 0324$4.99
- DeepSeek R1 0528$9.50
- QwQ 32B$6.70
Which should you choose?
Which is better: DeepSeek V3 0324, DeepSeek R1 0528 or QwQ 32B?
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, DeepSeek V3 0324, DeepSeek R1 0528 or QwQ 32B?
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 DeepSeek V3 0324, DeepSeek R1 0528 and QwQ 32B 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 V3 0324 and DeepSeek R1 0528 have the largest context windows (163,840 and 163,840 tokens), against 131,072 for QwQ 32B. Maximum output per response: DeepSeek V3 0324 up to 163,840, DeepSeek R1 0528 up to 32,768, QwQ 32B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V3 0324 accepts text; DeepSeek R1 0528 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 (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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