Mixtral 8x22B v0.1 vs Qwen3.5-397B-A17B
Mixtral 8x22B v0.1 (2024) and Qwen3.5-397B-A17B (2026) are compact production models from MistralAI and Alibaba. Mixtral 8x22B v0.1 ships a 64K-token context window, while Qwen3.5-397B-A17B ships a 262K-token context window. On Google-Proof Q&A, Qwen3.5-397B-A17B leads by 29.2 pts. On pricing, Mixtral 8x22B v0.1 costs $0.3/1M input tokens versus $0.39/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3.5-397B-A17B fits 4x more tokens; pick it for long-context work and Mixtral 8x22B v0.1 for tighter calls.
Specs
| Released | 2024-04-17 | 2026-02-16 |
| Context window | 64K | 262K |
| Parameters | 8x22B | 397B |
| Architecture | mixture of experts | MoE |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Mixtral 8x22B v0.1 | Qwen3.5-397B-A17B | |
|---|---|---|
| Input price | $0.3/1M tokens | $0.39/1M tokens |
| Output price | $0.9/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| Mixtral 8x22B v0.1 | Qwen3.5-397B-A17B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mixtral 8x22B v0.1 | Qwen3.5-397B-A17B |
|---|---|---|
| Google-Proof Q&A | 60.1 | 89.3 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Mixtral 8x22B v0.1 at 60.1 and Qwen3.5-397B-A17B at 89.3, with Qwen3.5-397B-A17B ahead by 29.2 points. The largest visible gap is 29.2 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 multimodal input: Qwen3.5-397B-A17B and structured outputs: Qwen3.5-397B-A17B. 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 Qwen3.5-397B-A17B lists $0.39/1M input and $2.34/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x22B v0.1 lower by about $0.49 per million blended tokens. Availability is 8 providers versus 1, so concentration risk also matters.
Choose Mixtral 8x22B v0.1 when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.5-397B-A17B when long-context analysis and larger context windows 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 Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B 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 Qwen3.5-397B-A17B?
Mixtral 8x22B v0.1 is cheaper on tracked token pricing. Mixtral 8x22B v0.1 costs $0.3/1M input and $0.9/1M output tokens. Qwen3.5-397B-A17B costs $0.39/1M input and $2.34/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x22B v0.1 or Qwen3.5-397B-A17B open source?
Mixtral 8x22B v0.1 is listed under Apache 2.0. Qwen3.5-397B-A17B 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 multimodal input, Mixtral 8x22B v0.1 or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B 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.
Which is better for structured outputs, Mixtral 8x22B v0.1 or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B has the clearer documented structured outputs signal in this comparison. If structured outputs 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 Qwen3.5-397B-A17B?
Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API, Fireworks AI, DeepInfra, and Baseten API. Qwen3.5-397B-A17B 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.