Qwen3 32B vs Claude Sonnet 3.5 v2 vs DeepSeek-R1
Too close to call on our weighted score (DeepSeek-R1 51, Qwen3 32B 51, Claude Sonnet 3.5 v2 44). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 32B
51/100- ECI138.5
- Price$0.70 / $2.80
- Context131K
AnthropicClaude Sonnet 3.5 v2
44/100- ECI133.5
- Price$3.00 / $15.00
- Context200K
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (DeepSeek-R1 51/100, Qwen3 32B 51/100, Claude Sonnet 3.5 v2 44/100), so choose by what matters most for your work: DeepSeek-R1 for raw capability and Claude Sonnet 3.5 v2 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-R1Capabilities Index (ECI): DeepSeek-R1 139.0 · Qwen3 32B 138.5 · Claude Sonnet 3.5 v2 133.5
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Qwen3 32B $1.23 · Claude Sonnet 3.5 v2 $6.00 per 1M tokens (3:1 blend)
- Longest contextClaude Sonnet 3.5 v2Claude Sonnet 3.5 v2 200,000 · Qwen3 32B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsClaude Sonnet 3.5 v2Qwen3 32B: Text · Claude Sonnet 3.5 v2: Text, Images, PDFs · DeepSeek-R1: Text
- Self-hostingQwen3 32B and DeepSeek-R1Publishes downloadable weights
| Measure | Weight | Qwen3 32B | Claude Sonnet 3.5 v2 | DeepSeek-R1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 57 | 64 |
| Price | 25% | 46 | 13 | 47 |
| Inputs & features | 15% | 35 | 60 | 35 |
| Context window | 10% | 24 | 32 | 24 |
| Overall | 100% | 51/100 | 44/100 | 51/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 32BAlibaba (Qwen) | Claude Sonnet 3.5 v2Anthropic | DeepSeek-R1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 138.5 | 133.5 | 139.0 (best) |
| ECI rank | #106 of 148 | #119 of 148 | #104 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 65.7% | 55.3% | 71.7% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.9% (best) | 8.5% | 53.3% |
| Price per million tokens | |||
| Input | $0.70 (best) | $3.00 | $0.70 (best) |
| Output | $2.80 | $15.00 | $2.60 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $1.23 | $6.00 | $1.18 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 1 providers | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens | 200,000 tokens (best) | 128,000 tokens |
| Max output | 16,384 tokens | 8,192 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | qwen3-32b | — | — |
| API providers | 14 (best) | 1 | 12 |
| Released | Apr 29, 2025 | Oct 22, 2024 | Jan 20, 2025 |
| Knowledge cutoff | Apr 2025 | Apr 30, 2024 | 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
- Claude Sonnet 3.5 v2$60.00
- DeepSeek-R1$12.20
Which should you choose?
Which is better: Qwen3 32B, Claude Sonnet 3.5 v2 or DeepSeek-R1?
It is close. Our weighted score puts them within a point (DeepSeek-R1 51/100, Qwen3 32B 51/100, Claude Sonnet 3.5 v2 44/100), so choose by what matters most for your work: DeepSeek-R1 for raw capability and Claude Sonnet 3.5 v2 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 32B, Claude Sonnet 3.5 v2 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); Claude Sonnet 3.5 v2 costs $3.00 input / $15.00 output per million tokens (median across 1 API provider). 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 $6.00 for Claude Sonnet 3.5 v2 (5.1× as much).
Which scores higher on benchmarks?
DeepSeek-R1 scores higher on the Capabilities Index (ECI): DeepSeek-R1 139.0 (#104 of 148), Qwen3 32B 138.5 (#106 of 148) and Claude Sonnet 3.5 v2 133.5 (#119 of 148). The confidence ranges of the top two overlap (136.2–140.4 vs 135.1–140.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek-R1 71.7%, Qwen3 32B 65.7%, Claude Sonnet 3.5 v2 55.3%; OTIS Mock AIME 2024–2025 — Qwen3 32B 66.9%, DeepSeek-R1 53.3%, Claude Sonnet 3.5 v2 8.5%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 32B, Claude Sonnet 3.5 v2 and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, DeepSeek-R1 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?
Claude Sonnet 3.5 v2 has the largest context window at 200,000 tokens, against 131,072 for Qwen3 32B and 128,000 for DeepSeek-R1. Maximum output per response: Qwen3 32B up to 16,384, Claude Sonnet 3.5 v2 up to 8,192, DeepSeek-R1 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 32B accepts text; Claude Sonnet 3.5 v2 accepts text, images and PDFs; DeepSeek-R1 accepts text. Claude Sonnet 3.5 v2 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; Claude Sonnet 3.5 v2 is proprietary.
Which is newer?
Qwen3 32B is the newest, released Apr 29, 2025. DeepSeek-R1 came out Jan 20, 2025; Claude Sonnet 3.5 v2 came out Oct 22, 2024. Knowledge cutoff: Qwen3 32B Apr 2025, Claude Sonnet 3.5 v2 Apr 30, 2024, 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.
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