DeepSeek R1 0528 vs Qwen3-235B-A22B
DeepSeek R1 0528 (2025) and Qwen3-235B-A22B (2025) are frontier reasoning models from DeepSeek and Alibaba. DeepSeek R1 0528 ships a 160K-token context window, while Qwen3-235B-A22B ships a 128K-token context window. On Google-Proof Q&A, Qwen3-235B-A22B leads by 5.1 pts. On pricing, DeepSeek R1 0528 costs $0.1/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
DeepSeek R1 0528 is ~300% cheaper at $0.1/1M; pay for Qwen3-235B-A22B only for provider fit.
Specs
| Released | 2025-01-01 | 2025-01-01 |
| Context window | 160K | 128K |
| Parameters | 671B | 235B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| DeepSeek R1 0528 | Qwen3-235B-A22B | |
|---|---|---|
| Input price | $0.1/1M tokens | $0.4/1M tokens |
| Output price | $0.3/1M tokens | $1.2/1M tokens |
| Providers |
Capabilities
| DeepSeek R1 0528 | Qwen3-235B-A22B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek R1 0528 | Qwen3-235B-A22B |
|---|---|---|
| Google-Proof Q&A | 81.0 | 86.1 |
| Aider Polyglot | 71.4 | 59.6 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has DeepSeek R1 0528 at 81 and Qwen3-235B-A22B at 86.1, with Qwen3-235B-A22B ahead by 5.1 points; Aider Polyglot has DeepSeek R1 0528 at 71.4 and Qwen3-235B-A22B at 59.6, with DeepSeek R1 0528 ahead by 11.8 points. The largest visible gap is 11.8 points on Aider Polyglot, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: DeepSeek R1 0528 and code execution: DeepSeek R1 0528. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, DeepSeek R1 0528 lists $0.1/1M input and $0.3/1M output tokens, while Qwen3-235B-A22B lists $0.4/1M input and $1.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek R1 0528 lower by about $0.48 per million blended tokens. Availability is 5 providers versus 3, so concentration risk also matters.
Choose DeepSeek R1 0528 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Qwen3-235B-A22B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek R1 0528 or Qwen3-235B-A22B?
DeepSeek R1 0528 supports 160K tokens, while Qwen3-235B-A22B supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek R1 0528 or Qwen3-235B-A22B?
DeepSeek R1 0528 is cheaper on tracked token pricing. DeepSeek R1 0528 costs $0.1/1M input and $0.3/1M output tokens. Qwen3-235B-A22B costs $0.4/1M input and $1.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 0528 or Qwen3-235B-A22B open source?
DeepSeek R1 0528 is listed under Open Source. Qwen3-235B-A22B is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, DeepSeek R1 0528 or Qwen3-235B-A22B?
DeepSeek R1 0528 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, DeepSeek R1 0528 or Qwen3-235B-A22B?
Both DeepSeek R1 0528 and Qwen3-235B-A22B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run DeepSeek R1 0528 and Qwen3-235B-A22B?
DeepSeek R1 0528 is available on Together AI, Fireworks AI, GCP Vertex AI, Novita AI, and OpenRouter. Qwen3-235B-A22B is available on Fireworks AI, AWS Bedrock, and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.