Qwen2.5-72B-Instruct vs Together AI Mixtral-8x7B-Instruct-v0.1
Qwen2.5-72B-Instruct (2024) and Together AI Mixtral-8x7B-Instruct-v0.1 (2023) are compact production models from Alibaba and MistralAI. Qwen2.5-72B-Instruct ships a 128K-token context window, while Together AI Mixtral-8x7B-Instruct-v0.1 ships a 33K-token context window. On pricing, Qwen2.5-72B-Instruct costs $0.12/1M input tokens versus $0.4/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen2.5-72B-Instruct is ~233% cheaper at $0.12/1M; pay for Together AI Mixtral-8x7B-Instruct-v0.1 only for provider fit.
Specs
| Released | 2024-06-07 | 2023-12-10 |
| Context window | 128K | 33K |
| Parameters | 72.7B | 56B |
| Architecture | decoder only | decoder only |
| License | Apache 2.0 | Open Source |
| Knowledge cutoff | - | 2023-12 |
Pricing and availability
| Qwen2.5-72B-Instruct | Together AI Mixtral-8x7B-Instruct-v0.1 | |
|---|---|---|
| Input price | $0.12/1M tokens | $0.4/1M tokens |
| Output price | $0.39/1M tokens | $0.4/1M tokens |
| Providers |
Capabilities
| Qwen2.5-72B-Instruct | Together AI Mixtral-8x7B-Instruct-v0.1 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
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, Qwen2.5-72B-Instruct lists $0.12/1M input and $0.39/1M output tokens, while Together AI Mixtral-8x7B-Instruct-v0.1 lists $0.4/1M input and $0.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $0.2 per million blended tokens. Availability is 7 providers versus 1, so concentration risk also matters.
Choose Qwen2.5-72B-Instruct when long-context analysis, larger context windows, and lower input-token cost are central to the workload. Choose Together AI Mixtral-8x7B-Instruct-v0.1 when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Qwen2.5-72B-Instruct or Together AI Mixtral-8x7B-Instruct-v0.1?
Qwen2.5-72B-Instruct supports 128K tokens, while Together AI Mixtral-8x7B-Instruct-v0.1 supports 33K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Qwen2.5-72B-Instruct or Together AI Mixtral-8x7B-Instruct-v0.1?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. Qwen2.5-72B-Instruct costs $0.12/1M input and $0.39/1M output tokens. Together AI Mixtral-8x7B-Instruct-v0.1 costs $0.4/1M input and $0.4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Qwen2.5-72B-Instruct or Together AI Mixtral-8x7B-Instruct-v0.1 open source?
Qwen2.5-72B-Instruct is listed under Apache 2.0. Together AI Mixtral-8x7B-Instruct-v0.1 is listed under Open Source. 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, Qwen2.5-72B-Instruct or Together AI Mixtral-8x7B-Instruct-v0.1?
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 Qwen2.5-72B-Instruct and Together AI Mixtral-8x7B-Instruct-v0.1?
Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. Together AI Mixtral-8x7B-Instruct-v0.1 is available on Together AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Qwen2.5-72B-Instruct over Together AI Mixtral-8x7B-Instruct-v0.1?
Qwen2.5-72B-Instruct is ~233% cheaper at $0.12/1M; pay for Together AI Mixtral-8x7B-Instruct-v0.1 only for provider fit. If your workload also depends on long-context analysis, start with Qwen2.5-72B-Instruct; if it depends on provider fit, run the same evaluation with Together AI Mixtral-8x7B-Instruct-v0.1.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.