GPT OSS 20B vs DeepSeek-R1-Distill-Qwen-32B vs Qwen3.5 9B
Qwen3.5 9B comes out ahead, 64 to 54 and 47 on our weighted score, and it is the cheaper option too.
OpenAIGPT OSS 20B
54/100- ECI137.8
- Price$0.07 / $0.295
- Context131K
DeepSeekDeepSeek-R1-Distill-Qwen-32B
47/100- ECI137.4
- Price—
- Context131K
- Our pick
Alibaba (Qwen)Qwen3.5 9B
64/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
Qwen3.5 9B is our pick
Qwen3.5 9B is the better all-round choice, scoring 64/100 against GPT OSS 20B (54) and DeepSeek-R1-Distill-Qwen-32B (47). It leads on capability, inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityQwen3.5 9BCapabilities Index (ECI): Qwen3.5 9B 139.5 · GPT OSS 20B 137.8 · DeepSeek-R1-Distill-Qwen-32B 137.4
- Lowest priceQwen3.5 9BQwen3.5 9B $0.113 · GPT OSS 20B $0.126 per 1M tokens (3:1 blend) · DeepSeek-R1-Distill-Qwen-32B unpriced
- Longest contextQwen3.5 9BQwen3.5 9B 262,144 · GPT OSS 20B 131,072 · DeepSeek-R1-Distill-Qwen-32B 131,072 tokens
- Widest inputsQwen3.5 9BGPT OSS 20B: Text · DeepSeek-R1-Distill-Qwen-32B: Text · Qwen3.5 9B: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GPT OSS 20B | DeepSeek-R1-Distill-Qwen-32B | Qwen3.5 9B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 63 | 62 | 65 |
| Inputs & features | 20% | 45 | 10 | 80 |
| Context window | 13% | 24 | 24 | 37 |
| Overall | 100% | 54/100 | 47/100 | 64/100 |
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 | GPT OSS 20BOpenAI | DeepSeek-R1-Distill-Qwen-32BDeepSeek | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.8 | 137.4 | 139.5 (best) |
| ECI rank | #108 of 148 | #110 of 148 | #101 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 60.8% | 64.1% | 79.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 65.3% (best) | 55.6% | 61.7% |
| Price per million tokens | |||
| Input | $0.07 (best) | — | $0.10 |
| Output | $0.295 | — | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.126 | — | $0.113 (best) |
| Long-context rate | Same rate | — | Same rate |
| Price source | Median of 18 providers | — | Median of 14 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens | 32,768 tokens | 65,536 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | — | — | — |
| API providers | 19 (best) | — | 15 |
| Released | Aug 5, 2025 | Jan 20, 2025 | Feb 23, 2026 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT OSS 20B$1.29
- DeepSeek-R1-Distill-Qwen-32B—
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: GPT OSS 20B, DeepSeek-R1-Distill-Qwen-32B or Qwen3.5 9B?
Qwen3.5 9B is the better all-round choice, scoring 64/100 against GPT OSS 20B (54) and DeepSeek-R1-Distill-Qwen-32B (47). It leads on capability, inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, GPT OSS 20B, DeepSeek-R1-Distill-Qwen-32B or Qwen3.5 9B?
Qwen3.5 9B is cheaper at $0.10 input / $0.15 output per million tokens (median across 14 API providers). GPT OSS 20B costs $0.07 input / $0.295 output per million tokens (median across 18 API providers). At a typical mix of three input tokens to one output token, that is $0.113 per million tokens for Qwen3.5 9B versus $0.126 for GPT OSS 20B (1.1× as much). DeepSeek-R1-Distill-Qwen-32B has no published per-token price.
Which scores higher on benchmarks?
Qwen3.5 9B scores higher on the Capabilities Index (ECI): Qwen3.5 9B 139.5 (#101 of 148), GPT OSS 20B 137.8 (#108 of 148) and DeepSeek-R1-Distill-Qwen-32B 137.4 (#110 of 148). The confidence ranges of the top two overlap (136.5–141.3 vs 133.0–139.6), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.5 9B 79.0%, DeepSeek-R1-Distill-Qwen-32B 64.1%, GPT OSS 20B 60.8%; OTIS Mock AIME 2024–2025 — GPT OSS 20B 65.3%, Qwen3.5 9B 61.7%, DeepSeek-R1-Distill-Qwen-32B 55.6%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT OSS 20B, DeepSeek-R1-Distill-Qwen-32B and Qwen3.5 9B yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 9B 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?
Qwen3.5 9B has the largest context window at 262,144 tokens, against 131,072 for GPT OSS 20B and 131,072 for DeepSeek-R1-Distill-Qwen-32B. Maximum output per response: GPT OSS 20B up to 32,768, DeepSeek-R1-Distill-Qwen-32B up to 32,768, Qwen3.5 9B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT OSS 20B accepts text; DeepSeek-R1-Distill-Qwen-32B accepts text; Qwen3.5 9B accepts text, images and video. Qwen3.5 9B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen3.5 9B is the newest, released Feb 23, 2026. GPT OSS 20B came out Aug 5, 2025; DeepSeek-R1-Distill-Qwen-32B came out Jan 20, 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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