Qwen3 32B vs Gemini 2.5 Pro vs DeepSeek-R1
Gemini 2.5 Pro comes out ahead, 63 to 51 and 51 on our weighted score, though DeepSeek-R1 is 2.9× cheaper per token.
Alibaba (Qwen)Qwen3 32B
51/100- ECI138.5
- Price$0.70 / $2.80
- Context131K
- Our pick
GoogleGemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
Gemini 2.5 Pro is our pick
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against DeepSeek-R1 (51) and Qwen3 32B (51). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGemini 2.5 ProCapabilities Index (ECI): Gemini 2.5 Pro 145.3 · DeepSeek-R1 139.0 · Qwen3 32B 138.5
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Qwen3 32B $1.23 · Gemini 2.5 Pro $3.44 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · Qwen3 32B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsGemini 2.5 ProQwen3 32B: Text · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video · DeepSeek-R1: Text
- Self-hostingQwen3 32B and DeepSeek-R1Publishes downloadable weights
| Measure | Weight | Qwen3 32B | Gemini 2.5 Pro | DeepSeek-R1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 72 | 64 |
| Price | 25% | 46 | 24 | 47 |
| Inputs & features | 15% | 35 | 100 | 35 |
| Context window | 10% | 24 | 61 | 24 |
| Overall | 100% | 51/100 | 63/100 | 51/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 32BAlibaba (Qwen) | Gemini 2.5 ProGoogle | DeepSeek-R1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 138.5 | 145.3 (best) | 139.0 |
| ECI rank | #106 of 148 | #78 of 148 (best) | #104 of 148 |
| GPQA DiamondGraduate-level science questions | 65.7% | 85.3% (best) | 71.7% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 24.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.9% | 84.7% (best) | 53.3% |
| SWE-bench VerifiedFixing real GitHub issues | — | 57.6% | — |
| Price per million tokens | |||
| Input | $0.70 (best) | $1.25 | $0.70 (best) |
| Output | $2.80 | $10.00 | $2.60 (best) |
| Cached input | — | $0.125 | — |
| Blended (3:1) | $1.23 | $3.44 | $1.18 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Official Alibaba API | Official Google API | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens | 1,048,576 tokens (best) | 128,000 tokens |
| Max output | 16,384 tokens | 65,536 tokens (best) | 32,768 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 | Open | Proprietary | Open |
| API model ID | qwen3-32b | gemini-2.5-pro | — |
| API providers | 14 | 22 (best) | 12 |
| Released | Apr 29, 2025 | Jun 17, 2025 | Jan 20, 2025 |
| Knowledge cutoff | Apr 2025 | Jan 2025 | Jul 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3 32B$12.60
- Gemini 2.5 Pro$32.50
- DeepSeek-R1$12.20
Which should you choose?
Which is better: Qwen3 32B, Gemini 2.5 Pro or DeepSeek-R1?
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against DeepSeek-R1 (51) and Qwen3 32B (51). It leads on capability, inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 32B, Gemini 2.5 Pro or DeepSeek-R1?
DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). Qwen3 32B costs $0.70 input / $2.80 output per million tokens (official Alibaba API price); Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $1.18 per million tokens for DeepSeek-R1 versus $1.23 for Qwen3 32B (1× as much) and $3.44 for Gemini 2.5 Pro (2.9× as much).
Which scores higher on benchmarks?
Gemini 2.5 Pro scores higher on the Capabilities Index (ECI): Gemini 2.5 Pro 145.3 (#78 of 148), DeepSeek-R1 139.0 (#104 of 148) and Qwen3 32B 138.5 (#106 of 148). Their confidence ranges do not overlap (143.6–146.9 vs 136.2–140.4), so the gap is a real one. On individual benchmarks: GPQA Diamond — Gemini 2.5 Pro 85.3%, DeepSeek-R1 71.7%, Qwen3 32B 65.7%; OTIS Mock AIME 2024–2025 — Gemini 2.5 Pro 84.7%, Qwen3 32B 66.9%, DeepSeek-R1 53.3%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 32B and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, Gemini 2.5 Pro 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 Pro has the largest context window at 1,048,576 tokens, against 131,072 for Qwen3 32B and 128,000 for DeepSeek-R1. Maximum output per response: Qwen3 32B up to 16,384, Gemini 2.5 Pro up to 65,536, DeepSeek-R1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 32B accepts text; Gemini 2.5 Pro accepts text, images, PDFs, audio and video; DeepSeek-R1 accepts text. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
Qwen3 32B and DeepSeek-R1 publishes its weights and can be self-hosted; Gemini 2.5 Pro is proprietary.
Which is newer?
Gemini 2.5 Pro is the newest, released Jun 17, 2025. Qwen3 32B came out Apr 29, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Qwen3 32B Apr 2025, Gemini 2.5 Pro Jan 2025, DeepSeek-R1 Jul 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.
