Qwen3 235B-A22B vs DeepSeek-R1

It is close. Our weighted score puts them within a point (Qwen3 235B-A22B 51/100, DeepSeek-R1 51/100), so choose by what matters most for your work: Qwen3 235B-A22B for raw capability and DeepSeek-R1 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). Qwen3 235B-A22B costs $0.70 input / $2.80 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 235B-A22B (1× as much).

Qwen3 235B-A22B scores higher on the Capabilities Index (ECI): Qwen3 235B-A22B 139.4 (#103 of 148) and DeepSeek-R1 139.0 (#104 of 148). The confidence ranges of the top two overlap (135.2–140.8 vs 136.2–140.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — DeepSeek-R1 71.7%, Qwen3 235B-A22B 70.7%.

There are no published SWE-bench Verified results for Qwen3 235B-A22B and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, Qwen3 235B-A22B leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Qwen3 235B-A22B has the largest context window at 131,072 tokens, against 128,000 for DeepSeek-R1. Maximum output per response: Qwen3 235B-A22B up to 16,384, DeepSeek-R1 up to 32,768 tokens.

Qwen3 235B-A22B accepts text; DeepSeek-R1 accepts text. They handle the same number of input types.

Yes, both publish their weights, so you can self-host them.

Qwen3 235B-A22B is the newest, released Apr 28, 2025. DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Qwen3 235B-A22B Apr 2025, DeepSeek-R1 Jul 2024.

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.