DeepSeek-R1 vs Qwen3 14B vs Qwen3 32B
Too close to call on our weighted score (Qwen3 14B 54, DeepSeek-R1 51, Qwen3 32B 51). The right pick depends on what you value most.
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
- Alibaba (Qwen)
Qwen3 32B
51/100- ECI138.5
- Price$0.70 / $2.80
- Context131K
Too close to call
It is close. Our weighted score puts them within 3 points (Qwen3 14B 54/100, DeepSeek-R1 51/100, Qwen3 32B 51/100), so choose by what matters most for your work: DeepSeek-R1 for raw capability and Qwen3 14B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-R1Capabilities Index (ECI): DeepSeek-R1 139.0 · Qwen3 32B 138.5 · Qwen3 14B 138.2
- Lowest priceQwen3 14BQwen3 14B $0.613 · DeepSeek-R1 $1.18 · Qwen3 32B $1.23 per 1M tokens (3:1 blend)
- Longest contextQwen3 14B and Qwen3 32BQwen3 14B 131,072 · Qwen3 32B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsDeepSeek-R1: Text · Qwen3 14B: Text · Qwen3 32B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-R1 | Qwen3 14B | Qwen3 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 63 | 64 |
| Price | 25% | 47 | 60 | 46 |
| Inputs & features | 15% | 35 | 35 | 35 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 51/100 | 54/100 | 51/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1DeepSeek | Qwen3 14BAlibaba (Qwen) | Qwen3 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.0 (best) | 138.2 | 138.5 |
| ECI rank | #104 of 148 (best) | #107 of 148 | #106 of 148 |
| GPQA DiamondGraduate-level science questions | 71.7% (best) | 63.8% | 65.7% |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | 66.4% | 66.9% (best) |
| Price per million tokens | |||
| Input | $0.70 | $0.35 (best) | $0.70 |
| Output | $2.60 | $1.40 (best) | $2.80 |
| Cached input | — | — | — |
| Blended (3:1) | $1.18 | $0.613 (best) | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 32,768 tokens (best) | 8,192 tokens | 16,384 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 | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | qwen3-14b | qwen3-32b |
| API providers | 12 | 1 | 14 (best) |
| Released | Jan 20, 2025 | Apr 29, 2025 | Apr 29, 2025 |
| Knowledge cutoff | Jul 2024 | Apr 2025 | Apr 2025 |
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$12.20
- Qwen3 14B$6.30
- Qwen3 32B$12.60
Which should you choose?
Which is better: DeepSeek-R1, Qwen3 14B or Qwen3 32B?
It is close. Our weighted score puts them within 3 points (Qwen3 14B 54/100, DeepSeek-R1 51/100, Qwen3 32B 51/100), so choose by what matters most for your work: DeepSeek-R1 for raw capability and Qwen3 14B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-R1, Qwen3 14B or Qwen3 32B?
Qwen3 14B is cheaper at $0.35 input / $1.40 output per million tokens (official Alibaba API price). DeepSeek-R1 costs $0.70 input / $2.60 output per million tokens (median across 11 API providers); Qwen3 32B costs $0.70 input / $2.80 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.613 per million tokens for Qwen3 14B versus $1.18 for DeepSeek-R1 (1.9× as much) and $1.23 for Qwen3 32B (2× as much).
Which scores higher on benchmarks?
DeepSeek-R1 scores higher on the Capabilities Index (ECI): DeepSeek-R1 139.0 (#104 of 148), Qwen3 32B 138.5 (#106 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (136.2–140.4 vs 135.1–140.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek-R1 71.7%, Qwen3 32B 65.7%, Qwen3 14B 63.8%; OTIS Mock AIME 2024–2025 — Qwen3 32B 66.9%, Qwen3 14B 66.4%, DeepSeek-R1 53.3%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1, Qwen3 14B and Qwen3 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. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 14B and Qwen3 32B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, Qwen3 14B up to 8,192, Qwen3 32B up to 16,384 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; Qwen3 14B accepts text; Qwen3 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?
Qwen3 14B is the newest, released Apr 29, 2025. Qwen3 32B came out Apr 29, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, Qwen3 14B Apr 2025, Qwen3 32B Apr 2025.
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.
