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Mixtral 8x22B vs Qwen2.5 VL 72B Instruct

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 Qwen2.5 VL 72B Instruct.

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

Qwen2.5 VL 72B Instruct

Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.

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Technical Specifications

SpecificationMixtral 8x22BQwen2.5 VL 72B Instruct
ProviderMistralAlibaba
Context Window65,536 tokens131,072 tokens
Agent Suitability87/100N/A
Time to First Token (TTFT)320 msN/A
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-12-112025-02-01

API Pricing Comparison

Input Price per Million Tokens

Mixtral 8x22B

$0.50

Qwen2.5 VL 72B Instruct

$0.80

Output Price per Million Tokens

Mixtral 8x22B

$1.00

Qwen2.5 VL 72B Instruct

$1.00

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

Qwen2.5 VL 72B Instruct Quirks & Gotchas

No developer gotchas reported.