โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

Mixtral 8x22B vs Qwen3.5-27B

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 Qwen3.5-27B.

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.

View Full Specs
Alibaba

Qwen3.5-27B

The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of...

View Full Specs

Technical Specifications

SpecificationMixtral 8x22BQwen3.5-27B
ProviderMistralAlibaba
Context Window65,536 tokens262,144 tokens
Agent Suitability87/100N/A
Time to First Token (TTFT)320 msN/A
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-12-112026-02-25

API Pricing Comparison

Input Price per Million Tokens

Mixtral 8x22B

$0.50

Qwen3.5-27B

$0.20

Output Price per Million Tokens

Mixtral 8x22B

$1.00

Qwen3.5-27B

$1.56

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

Qwen3.5-27B Quirks & Gotchas

No developer gotchas reported.