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Mixtral 8x22B vs Qwen3.5 397B A17B

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Mixtral 8x22B and Qwen3.5 397B A17B.

Mistral

Mixtral 8x22B

Mixtral 8x22B is Mistral AI's open-weight Mixture-of-Experts model, activating only 39B of its 141B total parameters per token to deliver frontier-level performance at inference costs comparable to a much smaller dense model. Released under the Apache 2.0 license, Mixtral 8x22B is one of the most capable fully open-weight models available, with strong multilingual performance, robust coding ability, and efficient fine-tuning via LoRA. It is widely deployed across self-hosted infrastructure, including Ollama, vLLM, and Hugging Face TGI.

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Alibaba

Qwen3.5 397B A17B

The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...

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Technical Specifications

SpecificationMixtral 8x22BQwen3.5 397B A17B
ProviderMistralAlibaba
Context Window65,536 tokens256,000 tokens
Agent Suitability87/100N/A
Time to First Token (TTFT)320 msN/A
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-12-112026-02-16

API Pricing Comparison

Input Price per Million Tokens

Mixtral 8x22B

$0.50

Qwen3.5 397B A17B

$0.39

Output Price per Million Tokens

Mixtral 8x22B

$1.00

Qwen3.5 397B A17B

$2.45

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

Mixtral 8x22B Quirks & Gotchas

  • โ–ธMoE architecture โ€” efficient inference for its capability tier
  • โ–ธRequires ~90GB VRAM at FP16 โ€” 4-bit quantization recommended for single-GPU deployment

Qwen3.5 397B A17B Quirks & Gotchas

No developer gotchas reported.