DeepSeek-V3.1 vs GPT-5.4 nano vs Qwen3 14B
GPT-5.4 nano comes out ahead, 68 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 pick
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-5.4 nanoCapabilities Index (ECI): GPT-5.4 nano 145.8 · DeepSeek-V3.1 139.9 · Qwen3 14B 138.2
- Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · DeepSeek-V3.1 131,072 · Qwen3 14B 131,072 tokens
- Widest inputsGPT-5.4 nanoDeepSeek-V3.1: Text · GPT-5.4 nano: Text, Images · Qwen3 14B: Text
- Self-hostingDeepSeek-V3.1 and Qwen3 14BPublishes downloadable weights (MIT License)
| Measure | Weight | DeepSeek-V3.1 | GPT-5.4 nano | Qwen3 14B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 73 | 63 |
| Price | 25% | 60 | 66 | 60 |
| Inputs & features | 15% | 35 | 70 | 35 |
| Context window | 10% | 24 | 44 | 24 |
| Overall | 100% | 55/100 | 68/100 | 54/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-V3.1DeepSeek | GPT-5.4 nanoOpenAI | Qwen3 14BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.9 | 145.8 (best) | 138.2 |
| ECI rank | #100 of 148 | #75 of 148 (best) | #107 of 148 |
| GPQA DiamondGraduate-level science questions | — | 78.5% (best) | 63.8% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 44.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 87.8% (best) | 66.4% |
| SimpleQA VerifiedShort factual questions | — | 11.7% | — |
| Price per million tokens | |||
| Input | $0.385 | $0.20 (best) | $0.35 |
| Output | $1.25 (best) | $1.25 (best) | $1.40 |
| Cached input | — | $0.02 | — |
| Blended (3:1) | $0.601 | $0.463 (best) | $0.613 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 8 providers | Official OpenAI API | Official Alibaba API |
| 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 | Yeslow · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenMIT License | Proprietary | Open |
| API model ID | — | gpt-5.4-nano | qwen3-14b |
| API providers | 8 | 26 (best) | 1 |
| Released | Aug 21, 2025 | Mar 17, 2026 | Apr 29, 2025 |
| Knowledge cutoff | — | Aug 31, 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-V3.1$6.35
- GPT-5.4 nano$4.50
- Qwen3 14B$6.30
Which should you choose?
Which is better: DeepSeek-V3.1, GPT-5.4 nano or Qwen3 14B?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on capability, price, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-V3.1, GPT-5.4 nano or Qwen3 14B?
GPT-5.4 nano is cheaper at $0.20 input / $1.25 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 $0.463 per million tokens for GPT-5.4 nano versus $0.601 for DeepSeek-V3.1 (1.3× as much) and $0.613 for Qwen3 14B (1.3× as much).
Which scores higher on benchmarks?
GPT-5.4 nano scores higher on the Capabilities Index (ECI): GPT-5.4 nano 145.8 (#75 of 148), DeepSeek-V3.1 139.9 (#100 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (143.2–147.7 vs 136.1–143.3), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-V3.1, GPT-5.4 nano and Qwen3 14B yet, so there is no like-for-like coding score. On overall capability, GPT-5.4 nano 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-5.4 nano has the largest context window at 400,000 tokens, against 131,072 for DeepSeek-V3.1 and 131,072 for Qwen3 14B. Maximum output per response: DeepSeek-V3.1 up to 8,192, GPT-5.4 nano up to 128,000, Qwen3 14B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-V3.1 accepts text; GPT-5.4 nano accepts text and images; Qwen3 14B accepts text. GPT-5.4 nano handles the widest range of inputs.
Are any of these open source?
DeepSeek-V3.1 and Qwen3 14B publishes its weights (MIT License) and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
GPT-5.4 nano is the newest, released Mar 17, 2026. DeepSeek-V3.1 came out Aug 21, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, 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.
