Gemini 2.5 Flash vs Qwen3 32B vs DeepSeek-V3.1
Gemini 2.5 Flash comes out ahead, 68 to 55 and 51 on our weighted score, though DeepSeek-V3.1 is 29% cheaper per token.
- Our pick
GoogleGemini 2.5 Flash
68/100- ECI140.8
- Price$0.30 / $2.50
- Context1.05M
Alibaba (Qwen)Qwen3 32B
51/100- ECI138.5
- Price$0.70 / $2.80
- Context131K
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- 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 Qwen3 32B (51). 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 · Qwen3 32B 138.5
- Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Gemini 2.5 Flash $0.85 · Qwen3 32B $1.23 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · Qwen3 32B 131,072 · DeepSeek-V3.1 131,072 tokens
- Widest inputsGemini 2.5 FlashGemini 2.5 Flash: Text, Images, PDFs, Audio, Video · Qwen3 32B: Text · DeepSeek-V3.1: Text
- Self-hostingQwen3 32B and DeepSeek-V3.1Publishes downloadable weights (MIT License)
| Measure | Weight | Gemini 2.5 Flash | Qwen3 32B | DeepSeek-V3.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 67 | 64 | 65 |
| Price | 25% | 53 | 46 | 60 |
| Inputs & features | 15% | 100 | 35 | 35 |
| Context window | 10% | 61 | 24 | 24 |
| Overall | 100% | 68/100 | 51/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.5 FlashGoogle | Qwen3 32BAlibaba (Qwen) | DeepSeek-V3.1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 140.8 (best) | 138.5 | 139.9 |
| ECI rank | #97 of 148 (best) | #106 of 148 | #100 of 148 |
| GPQA DiamondGraduate-level science questions | — | 65.7% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 66.9% | — |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.70 | $0.385 |
| Output | $2.50 | $2.80 | $1.25 (best) |
| Cached input | $0.03 | — | — |
| Blended (3:1) | $0.85 | $1.23 | $0.601 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Google API | Official Alibaba API | Median of 8 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 131,072 tokens | 131,072 tokens |
| Max output | 65,536 tokens (best) | 16,384 tokens | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | Yes | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenMIT License |
| API model ID | gemini-2.5-flash | qwen3-32b | — |
| API providers | 22 (best) | 14 | 8 |
| Released | Jun 17, 2025 | Apr 29, 2025 | Aug 21, 2025 |
| Knowledge cutoff | Jan 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.
- Gemini 2.5 Flash$8.00
- Qwen3 32B$12.60
- DeepSeek-V3.1$6.35
Which should you choose?
Which is better: Gemini 2.5 Flash, Qwen3 32B or DeepSeek-V3.1?
Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and Qwen3 32B (51). 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, Gemini 2.5 Flash, Qwen3 32B or DeepSeek-V3.1?
DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Gemini 2.5 Flash costs $0.30 input / $2.50 output per million tokens (official Google API price); Qwen3 32B costs $0.70 input / $2.80 output per million tokens (official Alibaba 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 $0.85 for Gemini 2.5 Flash (1.4× as much) and $1.23 for Qwen3 32B (2× 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 Qwen3 32B 138.5 (#106 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 Gemini 2.5 Flash, Qwen3 32B and DeepSeek-V3.1 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 Qwen3 32B and 131,072 for DeepSeek-V3.1. Maximum output per response: Gemini 2.5 Flash up to 65,536, Qwen3 32B up to 16,384, DeepSeek-V3.1 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.5 Flash accepts text, images, PDFs, audio and video; Qwen3 32B accepts text; DeepSeek-V3.1 accepts text. Gemini 2.5 Flash handles the widest range of inputs.
Are any of these open source?
Qwen3 32B and DeepSeek-V3.1 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; Qwen3 32B came out Apr 29, 2025. Knowledge cutoff: Gemini 2.5 Flash Jan 2025, Qwen3 32B 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.
