DeepSeek-R1 vs Qwen3 32B vs Qwen3 Max
Too close to call on our weighted score (DeepSeek-R1 51, Qwen3 32B 51, Qwen3 Max 50). The right pick depends on what you value most.
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
Alibaba (Qwen)Qwen3 32B
51/100- ECI138.5
- Price$0.70 / $2.80
- Context131K
- Alibaba (Qwen)
Qwen3 Max
50/100- ECI142.4
- Price$1.20 / $6.00
- Context262K
Too close to call
It is close. Our weighted score puts them within a point (DeepSeek-R1 51/100, Qwen3 32B 51/100, Qwen3 Max 50/100), so choose by what matters most for your work: Qwen3 Max for raw capability and DeepSeek-R1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 MaxCapabilities Index (ECI): Qwen3 Max 142.4 · DeepSeek-R1 139.0 · Qwen3 32B 138.5
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Qwen3 32B $1.23 · Qwen3 Max $2.40 per 1M tokens (3:1 blend)
- Longest contextQwen3 MaxQwen3 Max 262,144 · Qwen3 32B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsDeepSeek-R1: Text · Qwen3 32B: Text · Qwen3 Max: Text
- Self-hostingDeepSeek-R1 and Qwen3 32BPublishes downloadable weights
| Measure | Weight | DeepSeek-R1 | Qwen3 32B | Qwen3 Max |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 64 | 68 |
| Price | 25% | 47 | 46 | 32 |
| Inputs & features | 15% | 35 | 35 | 25 |
| Context window | 10% | 24 | 24 | 37 |
| Overall | 100% | 51/100 | 51/100 | 50/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1DeepSeek | Qwen3 32BAlibaba (Qwen) | Qwen3 MaxAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.0 | 138.5 | 142.4 (best) |
| ECI rank | #104 of 148 | #106 of 148 | #91 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 71.7% | 65.7% | 72.6% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 19.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | 66.9% | 73.3% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 48.8% |
| Price per million tokens | |||
| Input | $0.70 (best) | $0.70 (best) | $1.20 |
| Output | $2.60 (best) | $2.80 | $6.00 |
| Cached input | — | — | — |
| Blended (3:1) | $1.18 (best) | $1.23 | $2.40 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 16,384 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | — | qwen3-32b | qwen3-max |
| API providers | 12 | 14 | 16 (best) |
| Released | Jan 20, 2025 | Apr 29, 2025 | Sep 23, 2025 |
| Knowledge cutoff | Jul 2024 | Apr 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
- Qwen3 32B$12.60
- Qwen3 Max$24.00
Which should you choose?
Which is better: DeepSeek-R1, Qwen3 32B or Qwen3 Max?
It is close. Our weighted score puts them within a point (DeepSeek-R1 51/100, Qwen3 32B 51/100, Qwen3 Max 50/100), so choose by what matters most for your work: Qwen3 Max for raw capability and DeepSeek-R1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-R1, Qwen3 32B or Qwen3 Max?
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); Qwen3 Max costs $1.20 input / $6.00 output per million tokens (official Alibaba 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 $2.40 for Qwen3 Max (2× as much).
Which scores higher on benchmarks?
Qwen3 Max scores higher on the Capabilities Index (ECI): Qwen3 Max 142.4 (#91 of 148), DeepSeek-R1 139.0 (#104 of 148) and Qwen3 32B 138.5 (#106 of 148). The confidence ranges of the top two overlap (140.0–144.6 vs 136.2–140.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3 Max 72.6%, DeepSeek-R1 71.7%, Qwen3 32B 65.7%; OTIS Mock AIME 2024–2025 — Qwen3 Max 73.3%, Qwen3 32B 66.9%, DeepSeek-R1 53.3%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1, Qwen3 32B and Qwen3 Max yet, so there is no like-for-like coding score. On overall capability, Qwen3 Max 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?
Qwen3 Max has the largest context window at 262,144 tokens, against 131,072 for Qwen3 32B and 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, Qwen3 32B up to 16,384, Qwen3 Max up to 65,536 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; Qwen3 32B accepts text; Qwen3 Max accepts text. They handle the same number of input types.
Are any of these open source?
DeepSeek-R1 and Qwen3 32B publishes its weights and can be self-hosted; Qwen3 Max is proprietary.
Which is newer?
Qwen3 Max is the newest, released Sep 23, 2025. Qwen3 32B came out Apr 29, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, Qwen3 32B Apr 2025, Qwen3 Max 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.
