Mixtral 8x22B v0.1 vs Qwen2-72B
Mixtral 8x22B v0.1 (2024) and Qwen2-72B (2024) are compact production models from MistralAI and Alibaba. Mixtral 8x22B v0.1 ships a 64K-token context window, while Qwen2-72B ships a 128K-token context window. On HumanEval, Mixtral 8x22B v0.1 leads by 19.1 pts. On pricing, Mixtral 8x22B v0.1 costs $0.3/1M input tokens versus $0.45/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mixtral 8x22B v0.1 is ~50% cheaper at $0.3/1M; pay for Qwen2-72B only for long-context analysis.
Specs
| Released | 2024-04-17 | 2024-06-05 |
| Context window | 64K | 128K |
| Parameters | 8x22B | 72.71B |
| Architecture | mixture of experts | decoder only |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Mixtral 8x22B v0.1 | Qwen2-72B | |
|---|---|---|
| Input price | $0.3/1M tokens | $0.45/1M tokens |
| Output price | $0.9/1M tokens | $0.65/1M tokens |
| Providers |
Capabilities
| Mixtral 8x22B v0.1 | Qwen2-72B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mixtral 8x22B v0.1 | Qwen2-72B |
|---|---|---|
| HumanEval | 86.2 | 67.1 |
| Massive Multitask Language Understanding | 84.5 | 84.2 |
Deep dive
On shared benchmark coverage, HumanEval has Mixtral 8x22B v0.1 at 86.2 and Qwen2-72B at 67.1, with Mixtral 8x22B v0.1 ahead by 19.1 points; Massive Multitask Language Understanding has Mixtral 8x22B v0.1 at 84.5 and Qwen2-72B at 84.2, with Mixtral 8x22B v0.1 ahead by 0.3 points. The largest visible gap is 19.1 points on HumanEval, 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 structured outputs: Qwen2-72B. 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 Qwen2-72B lists $0.45/1M input and $0.65/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x22B v0.1 lower by about $0.03 per million blended tokens. Availability is 8 providers versus 4, 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 Qwen2-72B 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 Qwen2-72B?
Qwen2-72B supports 128K 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 Qwen2-72B?
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. Qwen2-72B costs $0.45/1M input and $0.65/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x22B v0.1 or Qwen2-72B open source?
Mixtral 8x22B v0.1 is listed under Apache 2.0. Qwen2-72B 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 structured outputs, Mixtral 8x22B v0.1 or Qwen2-72B?
Qwen2-72B 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 Qwen2-72B?
Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API, Fireworks AI, DeepInfra, and Baseten API. Qwen2-72B is available on Fireworks AI, DeepInfra, Together AI, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Mixtral 8x22B v0.1 over Qwen2-72B?
Mixtral 8x22B v0.1 is ~50% cheaper at $0.3/1M; pay for Qwen2-72B only for long-context analysis. 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 Qwen2-72B.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.