DeepSeek R1 0528 vs Qwen3.5-122B-A10B
DeepSeek R1 0528 (2025) and Qwen3.5-122B-A10B (2026) are frontier-tier reasoning models from DeepSeek and Alibaba. DeepSeek R1 0528 ships a 130k-token context window, while Qwen3.5-122B-A10B ships a 262k-token context window. On MMLU PRO, Qwen3.5-122B-A10B leads by 1.7 pts. On pricing, Qwen3.5-122B-A10B costs $0.26/1M input tokens versus $0.50/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen3.5-122B-A10B is ~92% cheaper at $0.26/1M; pay for DeepSeek R1 0528 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | DeepSeek R1 0528 | Qwen3.5-122B-A10B |
|---|---|---|
| Best for | reasoning-heavy apps and provider-routed production | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 130k | 262k |
| Cheapest output | $2.15/1M tokens | $2.08/1M tokens |
| Provider routes | 6 tracked | 3 tracked |
| Shared benchmarks | 4 rows | MMLU PRO leader |
Decision tradeoffs
- DeepSeek R1 0528 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek R1 0528 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek R1 0528 for Coding, RAG, and Agents.
- Qwen3.5-122B-A10B holds a shared-benchmark lead on MMLU PRO, ahead by 1.7 points.
- Qwen3.5-122B-A10B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-122B-A10B has the lower cheapest tracked output price at $2.08/1M tokens.
- Qwen3.5-122B-A10B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Qwen3.5-122B-A10B for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek R1 0528
$938
Cheapest tracked route/tier: OpenRouter
Qwen3.5-122B-A10B
$728
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $210. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Novita AI; start route-level A/B tests there.
- Qwen3.5-122B-A10B is $0.07/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Code execution before moving production traffic.
- Qwen3.5-122B-A10B adds Vision, Multimodal, and Function calling in local capability data.
- Provider overlap exists on Novita AI and OpenRouter; start route-level A/B tests there.
- DeepSeek R1 0528 is $0.07/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
- DeepSeek R1 0528 adds Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-05-28 | 2026-02-24 |
| Context window | 130k | 262k |
| Parameters | 685B total, 37B active (MoE) | 122B |
| Architecture | decoder only | mixture of experts |
| License | MIT(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | DeepSeek R1 0528 | Qwen3.5-122B-A10B |
|---|---|---|
| Input price | $0.50/1M tokens | $0.26/1M tokens |
| Output price | $2.15/1M tokens | $2.08/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek R1 0528 | Qwen3.5-122B-A10B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek R1 0528 | Qwen3.5-122B-A10B |
|---|---|---|
| MMLU PRO | 85.0 | 86.7 |
| SWE-bench Verified | 57.6 | 72.0 |
| Google-Proof Q&A | 81.0 | 85.7 |
| LiveCodeBench | 73.3 | 78.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek R1 0528 at 85 and Qwen3.5-122B-A10B at 86.7, with Qwen3.5-122B-A10B ahead by 1.7 points; SWE-bench Verified has DeepSeek R1 0528 at 57.6 and Qwen3.5-122B-A10B at 72, with Qwen3.5-122B-A10B ahead by 14.4 points; Google-Proof Q&A has DeepSeek R1 0528 at 81 and Qwen3.5-122B-A10B at 85.7, with Qwen3.5-122B-A10B ahead by 4.7 points. The largest visible gap is 14.4 points on SWE-bench Verified, 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 vision: Qwen3.5-122B-A10B, multimodal input: Qwen3.5-122B-A10B, function calling: Qwen3.5-122B-A10B, tool use: Qwen3.5-122B-A10B, and code execution: DeepSeek R1 0528. Both models share reasoning mode and 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 $0.50/1M input and $2.15/1M output tokens on the cheapest tracked provider, while Qwen3.5-122B-A10B lists $0.26/1M input and $2.08/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-122B-A10B lower by about $0.19 per million blended tokens. Availability is 6 providers versus 3, so concentration risk also matters.
Choose DeepSeek R1 0528 when coding workflow support and broader provider choice are central to the workload. Choose Qwen3.5-122B-A10B when long-context analysis, larger context windows, and lower input-token cost 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 Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B supports 262k tokens, while DeepSeek R1 0528 supports 130k 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 Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B is cheaper on tracked token pricing. DeepSeek R1 0528 costs $0.50/1M input and $2.15/1M output tokens. Qwen3.5-122B-A10B costs $0.26/1M input and $2.08/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek R1 0528 or Qwen3.5-122B-A10B open source?
DeepSeek R1 0528 is listed under MIT. Qwen3.5-122B-A10B 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 vision, DeepSeek R1 0528 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, DeepSeek R1 0528 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek R1 0528 and Qwen3.5-122B-A10B?
DeepSeek R1 0528 is available on Together AI, Fireworks AI, GCP Vertex AI, Novita AI, and OpenRouter. Qwen3.5-122B-A10B is available on OpenRouter, Alibaba Cloud PAI-EAS, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.