Qwen3 235B-A22B vs Qwen3 14B vs DeepSeek-R1
Too close to call on our weighted score (Qwen3 14B 54, Qwen3 235B-A22B 51, DeepSeek-R1 51). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 235B-A22B
51/100- ECI139.4
- Price$0.70 / $2.80
- Context131K
- Alibaba (Qwen)
Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
Too close to call
It is close. Our weighted score puts them within 3 points (Qwen3 14B 54/100, Qwen3 235B-A22B 51/100, DeepSeek-R1 51/100), so choose by what matters most for your work: Qwen3 235B-A22B for raw capability and Qwen3 14B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 235B-A22BCapabilities Index (ECI): Qwen3 235B-A22B 139.4 · DeepSeek-R1 139.0 · Qwen3 14B 138.2
- Lowest priceQwen3 14BQwen3 14B $0.613 · DeepSeek-R1 $1.18 · Qwen3 235B-A22B $1.23 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B and Qwen3 14BQwen3 235B-A22B 131,072 · Qwen3 14B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsQwen3 235B-A22B: Text · Qwen3 14B: Text · DeepSeek-R1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 235B-A22B | Qwen3 14B | DeepSeek-R1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 63 | 64 |
| Price | 25% | 46 | 60 | 47 |
| 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 | Qwen3 235B-A22BAlibaba (Qwen) | Qwen3 14BAlibaba (Qwen) | DeepSeek-R1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.4 (best) | 138.2 | 139.0 |
| ECI rank | #103 of 148 (best) | #107 of 148 | #104 of 148 |
| GPQA DiamondGraduate-level science questions | 70.7% | 63.8% | 71.7% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 66.4% (best) | 53.3% |
| Price per million tokens | |||
| Input | $0.70 | $0.35 (best) | $0.70 |
| Output | $2.80 | $1.40 (best) | $2.60 |
| Cached input | — | — | — |
| Blended (3:1) | $1.23 | $0.613 (best) | $1.18 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Alibaba API | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Max output | 16,384 tokens | 8,192 tokens | 32,768 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 | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen3-235b-a22b | qwen3-14b | — |
| API providers | 7 | 1 | 12 (best) |
| Released | Apr 28, 2025 | Apr 29, 2025 | Jan 20, 2025 |
| Knowledge cutoff | Apr 2025 | Apr 2025 | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3 235B-A22B$12.60
- Qwen3 14B$6.30
- DeepSeek-R1$12.20
Which should you choose?
Which is better: Qwen3 235B-A22B, Qwen3 14B or DeepSeek-R1?
It is close. Our weighted score puts them within 3 points (Qwen3 14B 54/100, Qwen3 235B-A22B 51/100, DeepSeek-R1 51/100), so choose by what matters most for your work: Qwen3 235B-A22B for raw capability and Qwen3 14B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 235B-A22B, Qwen3 14B or DeepSeek-R1?
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 235B-A22B 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 235B-A22B (2× as much).
Which scores higher on benchmarks?
Qwen3 235B-A22B scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B 139.4 (#103 of 148), DeepSeek-R1 139.0 (#104 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (135.2–140.8 vs 136.2–140.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek-R1 71.7%, Qwen3 235B-A22B 70.7%, Qwen3 14B 63.8%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 235B-A22B, Qwen3 14B and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B 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 235B-A22B and Qwen3 14B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for DeepSeek-R1. Maximum output per response: Qwen3 235B-A22B up to 16,384, Qwen3 14B up to 8,192, DeepSeek-R1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 235B-A22B accepts text; Qwen3 14B accepts text; DeepSeek-R1 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 235B-A22B came out Apr 28, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Qwen3 235B-A22B Apr 2025, Qwen3 14B Apr 2025, DeepSeek-R1 Jul 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.
