DeepSeek-V3.1 vs DeepSeek V3.2 vs Qwen3 14B
DeepSeek V3.2 comes out ahead, 64 to 55 and 54 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
- Our pickDeepSeek
DeepSeek V3.2
64/100- ECI146.3
- Price$0.296 / $0.48
- Context128K
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
DeepSeek V3.2 is our pick
DeepSeek V3.2 is the better all-round choice, scoring 64/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V3.2Capabilities Index (ECI): DeepSeek V3.2 146.3 · DeepSeek-V3.1 139.9 · Qwen3 14B 138.2
- Lowest priceDeepSeek V3.2DeepSeek V3.2 $0.342 · DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 per 1M tokens (3:1 blend)
- Longest contextDeepSeek-V3.1 and Qwen3 14BDeepSeek-V3.1 131,072 · Qwen3 14B 131,072 · DeepSeek V3.2 128,000 tokens
- Widest inputsSame inputsDeepSeek-V3.1: Text · DeepSeek V3.2: Text · Qwen3 14B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-V3.1 | DeepSeek V3.2 | Qwen3 14B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 73 | 63 |
| Price | 25% | 60 | 72 | 60 |
| Inputs & features | 15% | 35 | 45 | 35 |
| Context window | 10% | 24 | 24 | 24 |
| Overall | 100% | 55/100 | 64/100 | 54/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-V3.1DeepSeek | DeepSeek V3.2DeepSeek | Qwen3 14BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.9 | 146.3 (best) | 138.2 |
| ECI rank | #100 of 148 | #69 of 148 (best) | #107 of 148 |
| GPQA DiamondGraduate-level science questions | — | 83.4% (best) | 63.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 87.8% (best) | 66.4% |
| Price per million tokens | |||
| Input | $0.385 | $0.296 (best) | $0.35 |
| Output | $1.25 | $0.48 (best) | $1.40 |
| Cached input | — | — | — |
| Blended (3:1) | $0.601 | $0.342 (best) | $0.613 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 8 providers | Median of 15 providers | Official Alibaba API |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens | 64,000 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 | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenMIT License | OpenMIT License | Open |
| API model ID | — | — | qwen3-14b |
| API providers | 8 | 15 (best) | 1 |
| Released | Aug 21, 2025 | Dec 1, 2025 | Apr 29, 2025 |
| Knowledge cutoff | — | Jul 2024 | 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-V3.1$6.35
- DeepSeek V3.2$3.92
- Qwen3 14B$6.30
Which should you choose?
Which is better: DeepSeek-V3.1, DeepSeek V3.2 or Qwen3 14B?
DeepSeek V3.2 is the better all-round choice, scoring 64/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-V3.1, DeepSeek V3.2 or Qwen3 14B?
DeepSeek V3.2 is cheaper at $0.296 input / $0.48 output per million tokens (median across 15 API providers). DeepSeek-V3.1 costs $0.385 input / $1.25 output per million tokens (median across 8 API providers); Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.342 per million tokens for DeepSeek V3.2 versus $0.601 for DeepSeek-V3.1 (1.8× as much) and $0.613 for Qwen3 14B (1.8× as much).
Which scores higher on benchmarks?
DeepSeek V3.2 scores higher on the Capabilities Index (ECI): DeepSeek V3.2 146.3 (#69 of 148), DeepSeek-V3.1 139.9 (#100 of 148) and Qwen3 14B 138.2 (#107 of 148). Their confidence ranges do not overlap (144.4–147.5 vs 136.1–143.3), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-V3.1, DeepSeek V3.2 and Qwen3 14B yet, so there is no like-for-like coding score. On overall capability, DeepSeek V3.2 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-V3.1 and Qwen3 14B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for DeepSeek V3.2. Maximum output per response: DeepSeek-V3.1 up to 8,192, DeepSeek V3.2 up to 64,000, Qwen3 14B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-V3.1 accepts text; DeepSeek V3.2 accepts text; Qwen3 14B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (MIT License), so you can self-host them.
Which is newer?
DeepSeek V3.2 is the newest, released Dec 1, 2025. DeepSeek-V3.1 came out Aug 21, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: DeepSeek V3.2 Jul 2024, Qwen3 14B 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.
