Mixtral 8x7B vs Qwen3-Max
Mixtral 8x7B (2023) and Qwen3-Max (2026) are compact production models from MistralAI and Alibaba. Mixtral 8x7B ships a 32K-token context window, while Qwen3-Max ships a 128K-token context window. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.78/1M 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.
Mixtral 8x7B is ~420% cheaper at $0.15/1M; pay for Qwen3-Max only for long-context analysis.
Specs
| Released | 2023-12-11 | 2026-01-15 |
| Context window | 32K | 128K |
| Parameters | 8x7B | — |
| Architecture | mixture of experts | decoder only |
| License | Apache 2.0 | Proprietary |
| Knowledge cutoff | 2023-12 | 2025-12 |
Pricing and availability
| Mixtral 8x7B | Qwen3-Max | |
|---|---|---|
| Input price | $0.15/1M tokens | $0.78/1M tokens |
| Output price | $0.45/1M tokens | $3.9/1M tokens |
| Providers |
Capabilities
| Mixtral 8x7B | Qwen3-Max | |
|---|---|---|
| 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 vision: Qwen3-Max, multimodal input: Qwen3-Max, function calling: Qwen3-Max, tool use: Qwen3-Max, and structured outputs: Qwen3-Max. 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 Qwen3-Max lists $0.78/1M input and $3.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $1.48 per million blended tokens. Availability is 18 providers versus 1, 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 Qwen3-Max 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. 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.
FAQ
Which has a larger context window, Mixtral 8x7B or Qwen3-Max?
Qwen3-Max 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 Qwen3-Max?
Mixtral 8x7B is cheaper on tracked token pricing. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Qwen3-Max costs $0.78/1M input and $3.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x7B or Qwen3-Max open source?
Mixtral 8x7B is listed under Apache 2.0. Qwen3-Max is listed under Proprietary. 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 vision, Mixtral 8x7B or Qwen3-Max?
Qwen3-Max has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, Mixtral 8x7B or Qwen3-Max?
Qwen3-Max 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.
Where can I run Mixtral 8x7B and Qwen3-Max?
Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API. Qwen3-Max 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.