Mixtral 8x22B v0.1 vs Qwen3.5-27B
Mixtral 8x22B v0.1 (2024) and Qwen3.5-27B (2026) are frontier reasoning models from MistralAI and Alibaba. Mixtral 8x22B v0.1 ships a 64K-token context window, while Qwen3.5-27B ships a 262K-token context window. On Google-Proof Q&A, Qwen3.5-27B leads by 25.7 pts. On pricing, Qwen3.5-27B costs $0.2/1M input tokens versus $0.3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3.5-27B is ~54% cheaper at $0.2/1M; pay for Mixtral 8x22B v0.1 only for provider fit.
Specs
| Released | 2024-04-17 | 2026-02-24 |
| Context window | 64K | 262K |
| Parameters | 8x22B | 27B |
| Architecture | mixture of experts | decoder only |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Mixtral 8x22B v0.1 | Qwen3.5-27B | |
|---|---|---|
| Input price | $0.3/1M tokens | $0.2/1M tokens |
| Output price | $0.9/1M tokens | $1.56/1M tokens |
| Providers |
Capabilities
| Mixtral 8x22B v0.1 | Qwen3.5-27B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mixtral 8x22B v0.1 | Qwen3.5-27B |
|---|---|---|
| Google-Proof Q&A | 60.1 | 85.8 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Mixtral 8x22B v0.1 at 60.1 and Qwen3.5-27B at 85.8, with Qwen3.5-27B ahead by 25.7 points. The largest visible gap is 25.7 points on Google-Proof Q&A, 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: Qwen3.5-27B, function calling: Qwen3.5-27B, tool use: Qwen3.5-27B, and structured outputs: Qwen3.5-27B. 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 Qwen3.5-27B lists $0.2/1M input and $1.56/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x22B v0.1 lower by about $0.12 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 Qwen3.5-27B when reasoning depth, 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, Mixtral 8x22B v0.1 or Qwen3.5-27B?
Qwen3.5-27B supports 262K 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 Qwen3.5-27B?
Qwen3.5-27B is cheaper on tracked token pricing. Mixtral 8x22B v0.1 costs $0.3/1M input and $0.9/1M output tokens. Qwen3.5-27B costs $0.2/1M input and $1.56/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x22B v0.1 or Qwen3.5-27B open source?
Mixtral 8x22B v0.1 is listed under Apache 2.0. Qwen3.5-27B 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, Mixtral 8x22B v0.1 or Qwen3.5-27B?
Qwen3.5-27B 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 function calling, Mixtral 8x22B v0.1 or Qwen3.5-27B?
Qwen3.5-27B has the clearer documented function calling signal in this comparison. If function calling 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 Qwen3.5-27B?
Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API, Fireworks AI, DeepInfra, and Baseten API. Qwen3.5-27B is available on 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.