DeepSeek R1 0528 vs Gemini 2.5 Flash vs QwQ 32B
Gemini 2.5 Flash comes out ahead, 66 to 53 and 53 on our weighted score, though QwQ 32B is 33% cheaper per token.
DeepSeekDeepSeek R1 0528
53/100- ECI141.2
- Price$0.512 / $2.19
- Context164K
- Our pick
GoogleGemini 2.5 Flash
66/100- ECI140.6
- Price$0.30 / $2.50
- Context1.05M
Alibaba (Qwen)QwQ 32B
53/100- ECI137.5
- Price$0.53 / $0.70
- Context131K
Gemini 2.5 Flash is our pick
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against DeepSeek R1 0528 (53) and QwQ 32B (53). It leads on inputs & features and context window. QwQ 32B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek R1 0528Capabilities Index (ECI): DeepSeek R1 0528 141.2 · Gemini 2.5 Flash 140.6 · QwQ 32B 137.5
- Lowest priceQwQ 32BQwQ 32B $0.573 · Gemini 2.5 Flash $0.85 · DeepSeek R1 0528 $0.932 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · DeepSeek R1 0528 163,840 · QwQ 32B 131,072 tokens
- Widest inputsGemini 2.5 FlashDeepSeek R1 0528: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video · QwQ 32B: Text
- Self-hostingDeepSeek R1 0528 and QwQ 32BPublishes downloadable weights (MIT)
| Measure | Weight | DeepSeek R1 0528 | Gemini 2.5 Flash | QwQ 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 66 | 65 | 61 |
| Price | 25% | 49 | 51 | 59 |
| Inputs & features | 15% | 35 | 100 | 35 |
| Context window | 10% | 28 | 61 | 25 |
| Overall | 100% | 53/100 | 66/100 | 53/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek R1 0528DeepSeek | Gemini 2.5 FlashGoogle | QwQ 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 141.2 (best) | 140.6 | 137.5 |
| ECI rank | #108 of 165 (best) | #110 of 165 | #123 of 165 |
| GPQA DiamondGraduate-level science questions | 76.3% (best) | — | 65.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% (best) | — | 59.2% |
| Price per million tokens | |||
| Input | $0.512 | $0.30 (best) | $0.53 |
| Output | $2.19 | $2.50 | $0.70 (best) |
| Cached input | — | $0.03 | — |
| Blended (3:1) | $0.932 | $0.85 | $0.573 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 16 providers | Official Google API | Median of 2 providers |
| Limits | |||
| Context window | 163,840 tokens | 1,048,576 tokens (best) | 131,072 tokens |
| Max output | 32,768 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 | Proprietary | Open |
| API model ID | — | gemini-2.5-flash | — |
| API providers | 16 | 32 (best) | 2 |
| Released | May 28, 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 R1 0528$9.50
- Gemini 2.5 Flash$8.00
- QwQ 32B$6.70
Which should you choose?
Which is better: DeepSeek R1 0528, Gemini 2.5 Flash or QwQ 32B?
Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against DeepSeek R1 0528 (53) and QwQ 32B (53). It leads on inputs & features and context window. QwQ 32B wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek R1 0528, Gemini 2.5 Flash or QwQ 32B?
QwQ 32B is cheaper at $0.53 input / $0.70 output per million tokens (median across 2 API providers). Gemini 2.5 Flash costs $0.30 input / $2.50 output per million tokens (official Google API price); DeepSeek R1 0528 costs $0.512 input / $2.19 output per million tokens (median across 16 API providers). At a typical mix of three input tokens to one output token, that is $0.573 per million tokens for QwQ 32B versus $0.85 for Gemini 2.5 Flash (1.5× as much) and $0.932 for DeepSeek R1 0528 (1.6× as much).
Which scores higher on benchmarks?
DeepSeek R1 0528 scores higher on the Capabilities Index (ECI): DeepSeek R1 0528 141.2 (#108 of 165), Gemini 2.5 Flash 140.6 (#110 of 165) and QwQ 32B 137.5 (#123 of 165). The confidence ranges of the top two overlap (139.0–142.9 vs 138.4–142.1), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek R1 0528, Gemini 2.5 Flash and QwQ 32B yet, so there is no like-for-like coding score. On overall capability, DeepSeek R1 0528 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 163,840 for DeepSeek R1 0528 and 131,072 for QwQ 32B. Maximum output per response: DeepSeek R1 0528 up to 32,768, Gemini 2.5 Flash up to 65,536, QwQ 32B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek R1 0528 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 R1 0528 and QwQ 32B publishes its weights (MIT) and can be self-hosted; Gemini 2.5 Flash is proprietary.
Which is newer?
Gemini 2.5 Flash is the newest, released Jun 17, 2025. DeepSeek R1 0528 came out May 28, 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 Nov 2, 2026.
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