Mixtral 8x7B vs Qwen2-72B
Mixtral 8x7B (2023) and Qwen2-72B (2024) are compact production models from MistralAI and Alibaba. Mixtral 8x7B ships a 32K-token context window, while Qwen2-72B ships a 128K-token context window. On HumanEval, Mixtral 8x7B leads by 13.4 pts. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.45/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Mixtral 8x7B is ~200% cheaper at $0.15/1M; pay for Qwen2-72B only for long-context analysis.
Specs
| Released | 2023-12-11 | 2024-06-05 |
| Context window | 32K | 128K |
| Parameters | 8x7B | 72.71B |
| Architecture | mixture of experts | decoder only |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Mixtral 8x7B | Qwen2-72B | |
|---|---|---|
| Input price | $0.15/1M tokens | $0.45/1M tokens |
| Output price | $0.45/1M tokens | $0.65/1M tokens |
| Providers |
Capabilities
| Mixtral 8x7B | Qwen2-72B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mixtral 8x7B | Qwen2-72B |
|---|---|---|
| HumanEval | 80.5 | 67.1 |
| Massive Multitask Language Understanding | 80.2 | 84.2 |
Deep dive
On shared benchmark coverage, HumanEval has Mixtral 8x7B at 80.5 and Qwen2-72B at 67.1, with Mixtral 8x7B ahead by 13.4 points; Massive Multitask Language Understanding has Mixtral 8x7B at 80.2 and Qwen2-72B at 84.2, with Qwen2-72B ahead by 4 points. The largest visible gap is 13.4 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 8x7B lists $0.15/1M input and $0.45/1M 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 8x7B lower by about $0.27 per million blended tokens. Availability is 18 providers versus 4, so concentration risk also matters.
Choose Mixtral 8x7B 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 8x7B or Qwen2-72B?
Qwen2-72B supports 128K tokens, while Mixtral 8x7B supports 32K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, Mixtral 8x7B or Qwen2-72B?
Mixtral 8x7B is cheaper on tracked token pricing. Mixtral 8x7B costs $0.15/1M input and $0.45/1M 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 8x7B or Qwen2-72B open source?
Mixtral 8x7B 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 8x7B 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 8x7B and Qwen2-72B?
Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI 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 8x7B over Qwen2-72B?
Mixtral 8x7B is ~200% cheaper at $0.15/1M; pay for Qwen2-72B only for long-context analysis. If your workload also depends on provider fit, start with Mixtral 8x7B; 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.