Mixtral 8x7B vs Qwen3.5-35B-A3B
Mixtral 8x7B (2023) and Qwen3.5-35B-A3B (2026) are frontier reasoning models from MistralAI and Alibaba. Mixtral 8x7B ships a 32K-token context window, while Qwen3.5-35B-A3B ships a 262K-token context window. On Google-Proof Q&A, Qwen3.5-35B-A3B leads by 29.7 pts. On pricing, Mixtral 8x7B costs $0.15/1M input tokens versus $0.16/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Pick Qwen3.5-35B-A3B for reasoning; Mixtral 8x7B is better when provider fit matters more.
Specs
| Released | 2023-12-11 | 2026-02-24 |
| Context window | 32K | 262K |
| Parameters | 8x7B | 35B |
| Architecture | mixture of experts | mixture of experts |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | 2023-12 | - |
Pricing and availability
| Mixtral 8x7B | Qwen3.5-35B-A3B | |
|---|---|---|
| Input price | $0.15/1M tokens | $0.16/1M tokens |
| Output price | $0.45/1M tokens | $1.3/1M tokens |
| Providers |
Capabilities
| Mixtral 8x7B | Qwen3.5-35B-A3B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mixtral 8x7B | Qwen3.5-35B-A3B |
|---|---|---|
| Google-Proof Q&A | 54.8 | 84.5 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Mixtral 8x7B at 54.8 and Qwen3.5-35B-A3B at 84.5, with Qwen3.5-35B-A3B ahead by 29.7 points. The largest visible gap is 29.7 points on Google-Proof Q&A, 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 reasoning mode: Qwen3.5-35B-A3B, function calling: Qwen3.5-35B-A3B, tool use: Qwen3.5-35B-A3B, and structured outputs: Qwen3.5-35B-A3B. 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 Qwen3.5-35B-A3B lists $0.16/1M input and $1.3/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.26 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 Qwen3.5-35B-A3B when reasoning depth 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 Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B supports 262K 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 Qwen3.5-35B-A3B?
Mixtral 8x7B is cheaper on tracked token pricing. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Qwen3.5-35B-A3B costs $0.16/1M input and $1.3/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x7B or Qwen3.5-35B-A3B open source?
Mixtral 8x7B is listed under Apache 2.0. Qwen3.5-35B-A3B 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 reasoning mode, Mixtral 8x7B or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Mixtral 8x7B or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the clearer documented function calling signal in this comparison. If function calling 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 Qwen3.5-35B-A3B?
Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API. Qwen3.5-35B-A3B 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.