Qwen3 14B vs GPT-5 Nano vs DeepSeek-V3.1
GPT-5 Nano comes out ahead, 70 to 55 and 54 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
- Our pick
OpenAIGPT-5 Nano
70/100- ECI139.4
- Price$0.05 / $0.40
- Context400K
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
GPT-5 Nano is our pick
GPT-5 Nano is the better all-round choice, scoring 70/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · GPT-5 Nano 139.4 · Qwen3 14B 138.2
- Lowest priceGPT-5 NanoGPT-5 Nano $0.138 · DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 per 1M tokens (3:1 blend)
- Longest contextGPT-5 NanoGPT-5 Nano 400,000 · Qwen3 14B 131,072 · DeepSeek-V3.1 131,072 tokens
- Widest inputsGPT-5 NanoQwen3 14B: Text · GPT-5 Nano: Text, Images · DeepSeek-V3.1: Text
- Self-hostingQwen3 14B and DeepSeek-V3.1Publishes downloadable weights (MIT License)
| Measure | Weight | Qwen3 14B | GPT-5 Nano | DeepSeek-V3.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 63 | 65 | 65 |
| Price | 25% | 60 | 91 | 60 |
| Inputs & features | 15% | 35 | 70 | 35 |
| Context window | 10% | 24 | 44 | 24 |
| Overall | 100% | 54/100 | 70/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 14BAlibaba (Qwen) | GPT-5 NanoOpenAI | DeepSeek-V3.1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 138.2 | 139.4 | 139.9 (best) |
| ECI rank | #107 of 148 | #102 of 148 | #100 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 63.8% | 69.4% (best) | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 20.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% | 81.1% (best) | — |
| SimpleQA VerifiedShort factual questions | — | 11.7% | — |
| Price per million tokens | |||
| Input | $0.35 | $0.05 (best) | $0.385 |
| Output | $1.40 | $0.40 (best) | $1.25 |
| Cached input | — | $0.005 | — |
| Blended (3:1) | $0.613 | $0.138 (best) | $0.601 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official OpenAI API | Median of 8 providers |
| Limits | |||
| Context window | 131,072 tokens | 400,000 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 128,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yesminimal · low · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | OpenMIT License |
| API model ID | qwen3-14b | gpt-5-nano | — |
| API providers | 1 | 21 (best) | 8 |
| Released | Apr 29, 2025 | Aug 7, 2025 | Aug 21, 2025 |
| Knowledge cutoff | Apr 2025 | May 30, 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 14B$6.30
- GPT-5 Nano$1.30
- DeepSeek-V3.1$6.35
Which should you choose?
Which is better: Qwen3 14B, GPT-5 Nano or DeepSeek-V3.1?
GPT-5 Nano is the better all-round choice, scoring 70/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 14B, GPT-5 Nano or DeepSeek-V3.1?
GPT-5 Nano is cheaper at $0.05 input / $0.40 output per million tokens (official OpenAI API price). 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 $0.138 per million tokens for GPT-5 Nano versus $0.601 for DeepSeek-V3.1 (4.4× as much) and $0.613 for Qwen3 14B (4.5× as much).
Which scores higher on benchmarks?
DeepSeek-V3.1 scores higher on the Capabilities Index (ECI): DeepSeek-V3.1 139.9 (#100 of 148), GPT-5 Nano 139.4 (#102 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (136.1–143.3 vs 134.9–141.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 14B, GPT-5 Nano and DeepSeek-V3.1 yet, so there is no like-for-like coding score. On overall capability, DeepSeek-V3.1 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?
GPT-5 Nano has the largest context window at 400,000 tokens, against 131,072 for Qwen3 14B and 131,072 for DeepSeek-V3.1. Maximum output per response: Qwen3 14B up to 8,192, GPT-5 Nano up to 128,000, DeepSeek-V3.1 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen3 14B accepts text; GPT-5 Nano accepts text and images; DeepSeek-V3.1 accepts text. GPT-5 Nano handles the widest range of inputs.
Are any of these open source?
Qwen3 14B and DeepSeek-V3.1 publishes its weights (MIT License) and can be self-hosted; GPT-5 Nano is proprietary.
Which is newer?
DeepSeek-V3.1 is the newest, released Aug 21, 2025. GPT-5 Nano came out Aug 7, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: Qwen3 14B Apr 2025, GPT-5 Nano May 30, 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.
