Mixtral 8x7B vs Qwen2.5-72B-Instruct
Mixtral 8x7B (2023) and Qwen2.5-72B-Instruct (2024) are compact production models from MistralAI and Alibaba. Mixtral 8x7B ships a 32K-token context window, while Qwen2.5-72B-Instruct ships a 128K-token context window. On Google-Proof Q&A, Qwen2.5-72B-Instruct leads by 10.6 pts. On pricing, Qwen2.5-72B-Instruct costs $0.12/1M input tokens versus $0.15/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen2.5-72B-Instruct fits 4x more tokens; pick it for long-context work and Mixtral 8x7B for tighter calls.
Specs
| Released | 2023-12-11 | 2024-06-07 |
| Context window | 32K | 128K |
| Parameters | 8x7B | 72.7B |
| Architecture | mixture of experts | decoder only |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Mixtral 8x7B | Qwen2.5-72B-Instruct | |
|---|---|---|
| Input price | $0.15/1M tokens | $0.12/1M tokens |
| Output price | $0.45/1M tokens | $0.39/1M tokens |
| Providers |
Capabilities
| Mixtral 8x7B | Qwen2.5-72B-Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mixtral 8x7B | Qwen2.5-72B-Instruct |
|---|---|---|
| Google-Proof Q&A | 54.8 | 65.4 |
| HumanEval | 80.5 | 92.7 |
| Massive Multitask Language Understanding | 80.2 | 88.2 |
| HellaSwag | 90.9 | 95.6 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Mixtral 8x7B at 54.8 and Qwen2.5-72B-Instruct at 65.4, with Qwen2.5-72B-Instruct ahead by 10.6 points; HumanEval has Mixtral 8x7B at 80.5 and Qwen2.5-72B-Instruct at 92.7, with Qwen2.5-72B-Instruct ahead by 12.2 points; Massive Multitask Language Understanding has Mixtral 8x7B at 80.2 and Qwen2.5-72B-Instruct at 88.2, with Qwen2.5-72B-Instruct ahead by 8 points. The largest visible gap is 12.2 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: Qwen2.5-72B-Instruct. 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 Qwen2.5-72B-Instruct lists $0.12/1M input and $0.39/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $0.04 per million blended tokens. Availability is 18 providers versus 7, so concentration risk also matters.
Choose Mixtral 8x7B when provider fit and broader provider choice are central to the workload. Choose Qwen2.5-72B-Instruct 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.
FAQ
Which has a larger context window, Mixtral 8x7B or Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct 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 Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Qwen2.5-72B-Instruct costs $0.12/1M input and $0.39/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x7B or Qwen2.5-72B-Instruct open source?
Mixtral 8x7B is listed under Apache 2.0. Qwen2.5-72B-Instruct 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 Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct 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 Qwen2.5-72B-Instruct?
Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API. Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Mixtral 8x7B over Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct fits 4x more tokens; pick it for long-context work and Mixtral 8x7B for tighter calls. If your workload also depends on provider fit, start with Mixtral 8x7B; if it depends on long-context analysis, run the same evaluation with Qwen2.5-72B-Instruct.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.