DeepSeek V3.2 vs Qwen3.6 Flash vs GPT-5.4 nano
Too close to call on our weighted score (Qwen3.6 Flash 70, GPT-5.4 nano 68, DeepSeek V3.2 64). The right pick depends on what you value most.
DeepSeekDeepSeek V3.2
64/100- ECI146.3
- Price$0.296 / $0.48
- Context128K
Alibaba (Qwen)Qwen3.6 Flash
70/100- ECI143.3
- Price$0.188 / $1.13
- Context1M
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.6 Flash 70/100, GPT-5.4 nano 68/100, DeepSeek V3.2 64/100), so choose by what matters most for your work: DeepSeek V3.2 for raw capability and Qwen3.6 Flash for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek V3.2Capabilities Index (ECI): DeepSeek V3.2 146.3 · GPT-5.4 nano 145.8 · Qwen3.6 Flash 143.3
- Lowest priceDeepSeek V3.2DeepSeek V3.2 $0.342 · Qwen3.6 Flash $0.422 · GPT-5.4 nano $0.463 per 1M tokens (3:1 blend)
- Longest contextQwen3.6 FlashQwen3.6 Flash 1,000,000 · GPT-5.4 nano 400,000 · DeepSeek V3.2 128,000 tokens
- Widest inputsQwen3.6 FlashDeepSeek V3.2: Text · Qwen3.6 Flash: Text, Images, Video · GPT-5.4 nano: Text, Images
- Self-hostingDeepSeek V3.2Publishes downloadable weights (MIT License)
| Measure | Weight | DeepSeek V3.2 | Qwen3.6 Flash | GPT-5.4 nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 70 | 73 |
| Price | 25% | 72 | 68 | 66 |
| Inputs & features | 15% | 45 | 80 | 70 |
| Context window | 10% | 24 | 60 | 44 |
| Overall | 100% | 64/100 | 70/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek V3.2DeepSeek | Qwen3.6 FlashAlibaba (Qwen) | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.3 (best) | 143.3 | 145.8 |
| ECI rank | #69 of 148 (best) | #85 of 148 | #75 of 148 |
| GPQA DiamondGraduate-level science questions | 83.4% (best) | 83.3% | 78.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 22.5% | 44.9% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 87.8% (best) | 84.4% | 87.8% |
| SimpleQA VerifiedShort factual questions | — | 15.9% (best) | 11.7% |
| Price per million tokens | |||
| Input | $0.296 | $0.188 (best) | $0.20 |
| Output | $0.48 (best) | $1.13 | $1.25 |
| Cached input | — | — | $0.02 |
| Blended (3:1) | $0.342 (best) | $0.422 | $0.463 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 15 providers | Official Alibaba API | Official OpenAI API |
| Limits | |||
| Context window | 128,000 tokens | 1,000,000 tokens (best) | 400,000 tokens |
| Max output | 64,000 tokens | 65,536 tokens | 128,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | OpenMIT License | Proprietary | Proprietary |
| API model ID | — | qwen3.6-flash | gpt-5.4-nano |
| API providers | 15 | 16 | 26 (best) |
| Released | Dec 1, 2025 | Apr 27, 2026 | Mar 17, 2026 |
| Knowledge cutoff | Jul 2024 | — | Aug 31, 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 V3.2$3.92
- Qwen3.6 Flash$4.13
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: DeepSeek V3.2, Qwen3.6 Flash or GPT-5.4 nano?
It is close. Our weighted score puts them within 2 points (Qwen3.6 Flash 70/100, GPT-5.4 nano 68/100, DeepSeek V3.2 64/100), so choose by what matters most for your work: DeepSeek V3.2 for raw capability and Qwen3.6 Flash for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek V3.2, Qwen3.6 Flash or GPT-5.4 nano?
DeepSeek V3.2 is cheaper at $0.296 input / $0.48 output per million tokens (median across 15 API providers). Qwen3.6 Flash costs $0.188 input / $1.13 output per million tokens (official Alibaba API price); GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI 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 $0.422 for Qwen3.6 Flash (1.2× as much) and $0.463 for GPT-5.4 nano (1.4× 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), GPT-5.4 nano 145.8 (#75 of 148) and Qwen3.6 Flash 143.3 (#85 of 148). The confidence ranges of the top two overlap (144.4–147.5 vs 143.2–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek V3.2 83.4%, Qwen3.6 Flash 83.3%, GPT-5.4 nano 78.5%; OTIS Mock AIME 2024–2025 — DeepSeek V3.2 87.8%, GPT-5.4 nano 87.8%, Qwen3.6 Flash 84.4%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek V3.2, Qwen3.6 Flash and GPT-5.4 nano 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?
Qwen3.6 Flash has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.4 nano and 128,000 for DeepSeek V3.2. Maximum output per response: DeepSeek V3.2 up to 64,000, Qwen3.6 Flash up to 65,536, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
DeepSeek V3.2 accepts text; Qwen3.6 Flash accepts text, images and video; GPT-5.4 nano accepts text and images. Qwen3.6 Flash handles the widest range of inputs.
Are any of these open source?
DeepSeek V3.2 publishes its weights (MIT License) and can be self-hosted; Qwen3.6 Flash and GPT-5.4 nano is proprietary.
Which is newer?
Qwen3.6 Flash is the newest, released Apr 27, 2026. GPT-5.4 nano came out Mar 17, 2026; DeepSeek V3.2 came out Dec 1, 2025. Knowledge cutoff: DeepSeek V3.2 Jul 2024, GPT-5.4 nano Aug 31, 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.
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