DeepSeek-V3.1 vs Gemini 2.5 Flash vs QwQ 32B
Gemini 2.5 Flash comes out ahead, 68 to 55 and 53 on our weighted score, though DeepSeek-V3.1 is 29% cheaper per token.
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
- Our pick
GoogleGemini 2.5 Flash
68/100- ECI140.8
- Price$0.30 / $2.50
- Context1.05M
Alibaba (Qwen)QwQ 32B
53/100- ECI137.6
- Price$0.66 / $1.00
- Context131K
Gemini 2.5 Flash is our pick
Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and QwQ 32B (53). It leads on inputs & features and context window. DeepSeek-V3.1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 2.5 FlashCapabilities Index (ECI): Gemini 2.5 Flash 140.8 · DeepSeek-V3.1 139.9 · QwQ 32B 137.6
- Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · QwQ 32B $0.745 · Gemini 2.5 Flash $0.85 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · DeepSeek-V3.1 131,072 · QwQ 32B 131,072 tokens
- Widest inputsGemini 2.5 FlashDeepSeek-V3.1: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video · QwQ 32B: Text
- Self-hostingDeepSeek-V3.1 and QwQ 32BPublishes downloadable weights (MIT License)
| Measure | Weight | DeepSeek-V3.1 | Gemini 2.5 Flash | QwQ 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 67 | 62 |
| Price | 25% | 60 | 53 | 56 |
| Inputs & features | 15% | 35 | 100 | 35 |
| Context window | 10% | 24 | 61 | 24 |
| Overall | 100% | 55/100 | 68/100 | 53/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-V3.1DeepSeek | Gemini 2.5 FlashGoogle | QwQ 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.9 | 140.8 (best) | 137.6 |
| ECI rank | #100 of 148 | #97 of 148 (best) | #109 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 65.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 59.2% |
| Price per million tokens | |||
| Input | $0.385 | $0.30 (best) | $0.66 |
| Output | $1.25 | $2.50 | $1.00 (best) |
| Cached input | — | $0.03 | — |
| Blended (3:1) | $0.601 (best) | $0.85 | $0.745 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 8 providers | Official Google API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens | 1,048,576 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 65,536 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | Yes | No |
| Video | No | Yes | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenMIT License | Proprietary | Open |
| API model ID | — | gemini-2.5-flash | — |
| API providers | 8 | 22 (best) | 1 |
| Released | Aug 21, 2025 | Jun 17, 2025 | Mar 5, 2025 |
| Knowledge cutoff | — | Jan 2025 | 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.
- DeepSeek-V3.1$6.35
- Gemini 2.5 Flash$8.00
- QwQ 32B$8.60
Which should you choose?
Which is better: DeepSeek-V3.1, Gemini 2.5 Flash or QwQ 32B?
Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and QwQ 32B (53). It leads on inputs & features and context window. DeepSeek-V3.1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-V3.1, Gemini 2.5 Flash or QwQ 32B?
DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). QwQ 32B costs $0.66 input / $1.00 output per million tokens (median across 1 API provider); Gemini 2.5 Flash costs $0.30 input / $2.50 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.601 per million tokens for DeepSeek-V3.1 versus $0.745 for QwQ 32B (1.2× as much) and $0.85 for Gemini 2.5 Flash (1.4× as much).
Which scores higher on benchmarks?
Gemini 2.5 Flash scores higher on the Capabilities Index (ECI): Gemini 2.5 Flash 140.8 (#97 of 148), DeepSeek-V3.1 139.9 (#100 of 148) and QwQ 32B 137.6 (#109 of 148). The confidence ranges of the top two overlap (138.5–142.3 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, Gemini 2.5 Flash and QwQ 32B yet, so there is no like-for-like coding score. On overall capability, Gemini 2.5 Flash 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?
Gemini 2.5 Flash has the largest context window at 1,048,576 tokens, against 131,072 for DeepSeek-V3.1 and 131,072 for QwQ 32B. Maximum output per response: DeepSeek-V3.1 up to 8,192, Gemini 2.5 Flash up to 65,536, QwQ 32B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-V3.1 accepts text; Gemini 2.5 Flash accepts text, images, PDFs, audio and video; QwQ 32B accepts text. Gemini 2.5 Flash handles the widest range of inputs.
Are any of these open source?
DeepSeek-V3.1 and QwQ 32B publishes its weights (MIT License) and can be self-hosted; Gemini 2.5 Flash is proprietary.
Which is newer?
DeepSeek-V3.1 is the newest, released Aug 21, 2025. Gemini 2.5 Flash came out Jun 17, 2025; QwQ 32B came out Mar 5, 2025. Knowledge cutoff: Gemini 2.5 Flash Jan 2025, QwQ 32B 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.
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