QwQ 32B vs DeepSeek-V3.1 vs GPT-4.1 mini
GPT-4.1 mini comes out ahead, 60 to 55 and 53 on our weighted score, though DeepSeek-V3.1 is 14% cheaper per token.
Alibaba (Qwen)QwQ 32B
53/100- ECI137.6
- Price$0.66 / $1.00
- Context131K
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
- Our pick
OpenAIGPT-4.1 mini
60/100- ECI135.0
- Price$0.40 / $1.60
- Context1.05M
GPT-4.1 mini is our pick
GPT-4.1 mini is the better all-round choice, scoring 60/100 against DeepSeek-V3.1 (55) and QwQ 32B (53). It leads on inputs & features and context window. DeepSeek-V3.1 wins on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · QwQ 32B 137.6 · GPT-4.1 mini 135.0
- Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · GPT-4.1 mini $0.70 · QwQ 32B $0.745 per 1M tokens (3:1 blend)
- Longest contextGPT-4.1 miniGPT-4.1 mini 1,047,576 · QwQ 32B 131,072 · DeepSeek-V3.1 131,072 tokens
- Widest inputsGPT-4.1 miniQwQ 32B: Text · DeepSeek-V3.1: Text · GPT-4.1 mini: Text, Images, PDFs
- Self-hostingQwQ 32B and DeepSeek-V3.1Publishes downloadable weights (MIT License)
| Measure | Weight | QwQ 32B | DeepSeek-V3.1 | GPT-4.1 mini |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 62 | 65 | 59 |
| Price | 25% | 56 | 60 | 57 |
| Inputs & features | 15% | 35 | 35 | 70 |
| Context window | 10% | 24 | 24 | 61 |
| Overall | 100% | 53/100 | 55/100 | 60/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | QwQ 32BAlibaba (Qwen) | DeepSeek-V3.1DeepSeek | GPT-4.1 miniOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.6 | 139.9 (best) | 135.0 |
| ECI rank | #109 of 148 | #100 of 148 (best) | #115 of 148 |
| GPQA DiamondGraduate-level science questions | 65.3% | — | 65.9% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 6.7% |
| OTIS Mock AIME 2024–2025Competition mathematics | 59.2% (best) | — | 44.7% |
| SimpleQA VerifiedShort factual questions | — | — | 12.7% |
| Price per million tokens | |||
| Input | $0.66 | $0.385 (best) | $0.40 |
| Output | $1.00 (best) | $1.25 | $1.60 |
| Cached input | — | — | $0.10 |
| Blended (3:1) | $0.745 | $0.601 (best) | $0.70 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 8 providers | Official OpenAI API |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 1,047,576 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | OpenMIT License | Proprietary |
| API model ID | — | — | gpt-4.1-mini |
| API providers | 1 | 8 | 24 (best) |
| Released | Mar 5, 2025 | Aug 21, 2025 | Apr 14, 2025 |
| Knowledge cutoff | Apr 2024 | — | Apr 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- QwQ 32B$8.60
- DeepSeek-V3.1$6.35
- GPT-4.1 mini$7.20
Which should you choose?
Which is better: QwQ 32B, DeepSeek-V3.1 or GPT-4.1 mini?
GPT-4.1 mini is the better all-round choice, scoring 60/100 against DeepSeek-V3.1 (55) and QwQ 32B (53). It leads on inputs & features and context window. DeepSeek-V3.1 wins on capability and price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, QwQ 32B, DeepSeek-V3.1 or GPT-4.1 mini?
DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). GPT-4.1 mini costs $0.40 input / $1.60 output per million tokens (official OpenAI API price); QwQ 32B costs $0.66 input / $1.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.601 per million tokens for DeepSeek-V3.1 versus $0.70 for GPT-4.1 mini (1.2× as much) and $0.745 for QwQ 32B (1.2× 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), QwQ 32B 137.6 (#109 of 148) and GPT-4.1 mini 135.0 (#115 of 148). The confidence ranges of the top two overlap (136.1–143.3 vs 133.1–141.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for QwQ 32B, DeepSeek-V3.1 and GPT-4.1 mini 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-4.1 mini has the largest context window at 1,047,576 tokens, against 131,072 for QwQ 32B and 131,072 for DeepSeek-V3.1. Maximum output per response: QwQ 32B up to 8,192, DeepSeek-V3.1 up to 8,192, GPT-4.1 mini up to 32,768 tokens.
Which can read images, PDFs, audio or video?
QwQ 32B accepts text; DeepSeek-V3.1 accepts text; GPT-4.1 mini accepts text, images and PDFs. GPT-4.1 mini handles the widest range of inputs.
Are any of these open source?
QwQ 32B and DeepSeek-V3.1 publishes its weights (MIT License) and can be self-hosted; GPT-4.1 mini is proprietary.
Which is newer?
DeepSeek-V3.1 is the newest, released Aug 21, 2025. GPT-4.1 mini came out Apr 14, 2025; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: QwQ 32B Apr 2024, GPT-4.1 mini Apr 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.
