Mixtral 8x22B v0.1 vs Qwen3.5-Flash
Mixtral 8x22B v0.1 (2024) and Qwen3.5-Flash (2026) are compact production models from MistralAI and Alibaba. Mixtral 8x22B v0.1 ships a 64K-token context window, while Qwen3.5-Flash ships a 1M-token context window. On pricing, Qwen3.5-Flash costs $0.1/1M input tokens versus $0.3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-Flash is ~200% cheaper at $0.1/1M; pay for Mixtral 8x22B v0.1 only for provider fit.
Specs
| Released | 2024-04-17 | 2026-02-23 |
| Context window | 64K | 1M |
| Parameters | 8x22B | — |
| Architecture | mixture of experts | - |
| License | Apache 2.0 | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Mixtral 8x22B v0.1 | Qwen3.5-Flash | |
|---|---|---|
| Input price | $0.3/1M tokens | $0.1/1M tokens |
| Output price | $0.9/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Mixtral 8x22B v0.1 | Qwen3.5-Flash | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Qwen3.5-Flash. Both models share the core language-model surface, 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, Mixtral 8x22B v0.1 lists $0.3/1M input and $0.9/1M output tokens, while Qwen3.5-Flash lists $0.1/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-Flash lower by about $0.29 per million blended tokens. Availability is 8 providers versus 1, so concentration risk also matters.
Choose Mixtral 8x22B v0.1 when provider fit and broader provider choice are central to the workload. Choose Qwen3.5-Flash 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Mixtral 8x22B v0.1 or Qwen3.5-Flash?
Qwen3.5-Flash supports 1M tokens, while Mixtral 8x22B v0.1 supports 64K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Mixtral 8x22B v0.1 or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper on tracked token pricing. Mixtral 8x22B v0.1 costs $0.3/1M input and $0.9/1M output tokens. Qwen3.5-Flash costs $0.1/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x22B v0.1 or Qwen3.5-Flash open source?
Mixtral 8x22B v0.1 is listed under Apache 2.0. Qwen3.5-Flash is listed under Proprietary. 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 multimodal input, Mixtral 8x22B v0.1 or Qwen3.5-Flash?
Qwen3.5-Flash 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 Mixtral 8x22B v0.1 and Qwen3.5-Flash?
Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API, Fireworks AI, DeepInfra, and Baseten API. Qwen3.5-Flash is available on Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Mixtral 8x22B v0.1 over Qwen3.5-Flash?
Qwen3.5-Flash is ~200% cheaper at $0.1/1M; pay for Mixtral 8x22B v0.1 only for provider fit. If your workload also depends on provider fit, start with Mixtral 8x22B v0.1; if it depends on long-context analysis, run the same evaluation with Qwen3.5-Flash.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.