DeepSeek-R1 vs Gemini 2.5 Pro vs Qwen3 32B
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.
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
- Our pick
GoogleGemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
Alibaba (Qwen)Qwen3 32B
51/100- ECI138.5
- Price$0.70 / $2.80
- Context131K
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 ProDeepSeek-R1: Text · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video · Qwen3 32B: Text
- Self-hostingDeepSeek-R1 and Qwen3 32BPublishes downloadable weights
| Measure | Weight | DeepSeek-R1 | Gemini 2.5 Pro | Qwen3 32B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 72 | 64 |
| Price | 25% | 47 | 24 | 46 |
| 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 | DeepSeek-R1DeepSeek | Gemini 2.5 ProGoogle | Qwen3 32BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.0 | 145.3 (best) | 138.5 |
| ECI rank | #104 of 148 | #78 of 148 (best) | #106 of 148 |
| GPQA DiamondGraduate-level science questions | 71.7% | 85.3% (best) | 65.7% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 24.6% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | 84.7% (best) | 66.9% |
| SWE-bench VerifiedFixing real GitHub issues | — | 57.6% | — |
| Price per million tokens | |||
| Input | $0.70 (best) | $1.25 | $0.70 (best) |
| Output | $2.60 (best) | $10.00 | $2.80 |
| Cached input | — | $0.125 | — |
| Blended (3:1) | $1.18 (best) | $3.44 | $1.23 |
| Long-context rate | Same rate | Over 200K: $2.50 / $15.00 | Same rate |
| Price source | Median of 11 providers | Official Google API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 1,048,576 tokens (best) | 131,072 tokens |
| Max output | 32,768 tokens | 65,536 tokens (best) | 16,384 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 | — | gemini-2.5-pro | qwen3-32b |
| API providers | 12 | 22 (best) | 14 |
| Released | Jan 20, 2025 | Jun 17, 2025 | Apr 29, 2025 |
| Knowledge cutoff | Jul 2024 | 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.
- DeepSeek-R1$12.20
- Gemini 2.5 Pro$32.50
- Qwen3 32B$12.60
Which should you choose?
Which is better: DeepSeek-R1, Gemini 2.5 Pro or Qwen3 32B?
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, DeepSeek-R1, Gemini 2.5 Pro or Qwen3 32B?
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 DeepSeek-R1 and Qwen3 32B 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: DeepSeek-R1 up to 32,768, Gemini 2.5 Pro up to 65,536, Qwen3 32B up to 16,384 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; Gemini 2.5 Pro accepts text, images, PDFs, audio and video; Qwen3 32B accepts text. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1 and Qwen3 32B 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: DeepSeek-R1 Jul 2024, Gemini 2.5 Pro 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.
