DeepSeek-R1-Distill-Qwen-32B vs o3-pro vs Claude Opus 4.1
Too close to call on our weighted score (o3-pro 68, Claude Opus 4.1 65, DeepSeek-R1-Distill-Qwen-32B 47). The right pick depends on what you value most.
DeepSeekDeepSeek-R1-Distill-Qwen-32B
47/100- ECI137.4
- Price—
- Context131K
OpenAIo3-pro
68/100- ECI147.4
- Price$20.00 / $80.00
- Context200K
AnthropicClaude Opus 4.1
65/100- ECI144.1
- Price$15.00 / $75.00
- Context200K
Too close to call
It is close. Our weighted score puts them within 3 points (o3-pro 68/100, Claude Opus 4.1 65/100, DeepSeek-R1-Distill-Qwen-32B 47/100), so choose by what matters most for your work: o3-pro for raw capability and Claude Opus 4.1 on price. The score weighs capability 67%, inputs & features 20%, context window 13%.
- Capabilityo3-proCapabilities Index (ECI): o3-pro 147.4 · Claude Opus 4.1 144.1 · DeepSeek-R1-Distill-Qwen-32B 137.4
- Lowest priceClaude Opus 4.1Claude Opus 4.1 $30.00 · o3-pro $35.00 per 1M tokens (3:1 blend) · DeepSeek-R1-Distill-Qwen-32B unpriced
- Longest contexto3-pro and Claude Opus 4.1o3-pro 200,000 · Claude Opus 4.1 200,000 · DeepSeek-R1-Distill-Qwen-32B 131,072 tokens
- Widest inputsClaude Opus 4.1DeepSeek-R1-Distill-Qwen-32B: Text · o3-pro: Text, Images · Claude Opus 4.1: Text, Images, PDFs
- Self-hostingDeepSeek-R1-Distill-Qwen-32BPublishes downloadable weights
| Measure | Weight | DeepSeek-R1-Distill-Qwen-32B | o3-pro | Claude Opus 4.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 62 | 75 | 71 |
| Inputs & features | 20% | 10 | 70 | 70 |
| Context window | 13% | 24 | 32 | 32 |
| Overall | 100% | 47/100 | 68/100 | 65/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1-Distill-Qwen-32BDeepSeek | o3-proOpenAI | Claude Opus 4.1Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.4 | 147.4 (best) | 144.1 |
| ECI rank | #110 of 148 | #60 of 148 (best) | #81 of 148 |
| GPQA DiamondGraduate-level science questions | 64.1% | — | 77.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 12.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 55.6% | — | 68.9% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 73.4% |
| Price per million tokens | |||
| Input | — | $20.00 | $15.00 (best) |
| Output | — | $80.00 | $75.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | — | $35.00 | $30.00 (best) |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official OpenAI API | Median of 14 providers |
| Limits | |||
| Context window | 131,072 tokens | 200,000 tokens (best) | 200,000 tokens (best) |
| Max output | 32,768 tokens | 100,000 tokens (best) | 32,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yeslow · medium · high | Yes |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | — | o3-pro | — |
| API providers | — | 6 | 14 (best) |
| Released | Jan 20, 2025 | Jun 10, 2025 | Aug 5, 2025 |
| Knowledge cutoff | — | May 2024 | Mar 31, 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-Distill-Qwen-32B—
- o3-pro$360.00
- Claude Opus 4.1$300.00
Which should you choose?
Which is better: DeepSeek-R1-Distill-Qwen-32B, o3-pro or Claude Opus 4.1?
It is close. Our weighted score puts them within 3 points (o3-pro 68/100, Claude Opus 4.1 65/100, DeepSeek-R1-Distill-Qwen-32B 47/100), so choose by what matters most for your work: o3-pro for raw capability and Claude Opus 4.1 on price. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, DeepSeek-R1-Distill-Qwen-32B, o3-pro or Claude Opus 4.1?
Claude Opus 4.1 is cheaper at $15.00 input / $75.00 output per million tokens (median across 14 API providers). o3-pro costs $20.00 input / $80.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $30.00 per million tokens for Claude Opus 4.1 versus $35.00 for o3-pro (1.2× as much). DeepSeek-R1-Distill-Qwen-32B has no published per-token price.
Which scores higher on benchmarks?
o3-pro scores higher on the Capabilities Index (ECI): o3-pro 147.4 (#60 of 148), Claude Opus 4.1 144.1 (#81 of 148) and DeepSeek-R1-Distill-Qwen-32B 137.4 (#110 of 148). The confidence ranges of the top two overlap (145.8–149.7 vs 141.6–146.0), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1-Distill-Qwen-32B and o3-pro yet, so there is no like-for-like coding score. On overall capability, o3-pro leads, which tends to carry over to coding, but test on your own codebase. Note that DeepSeek-R1-Distill-Qwen-32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
o3-pro and Claude Opus 4.1 have the largest context windows (200,000 and 200,000 tokens), against 131,072 for DeepSeek-R1-Distill-Qwen-32B. Maximum output per response: DeepSeek-R1-Distill-Qwen-32B up to 32,768, o3-pro up to 100,000, Claude Opus 4.1 up to 32,000 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1-Distill-Qwen-32B accepts text; o3-pro accepts text and images; Claude Opus 4.1 accepts text, images and PDFs. Claude Opus 4.1 handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1-Distill-Qwen-32B publishes its weights and can be self-hosted; o3-pro and Claude Opus 4.1 is proprietary.
Which is newer?
Claude Opus 4.1 is the newest, released Aug 5, 2025. o3-pro came out Jun 10, 2025; DeepSeek-R1-Distill-Qwen-32B came out Jan 20, 2025. Knowledge cutoff: o3-pro May 2024, Claude Opus 4.1 Mar 31, 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.
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