Mixtral 8x22B vs Qwen3 30B A3B Instruct 2507
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 30B A3B Instruct 2507.
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.
Qwen3 30B A3B Instruct 2507
Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...
Technical Specifications
| Specification | Mixtral 8x22B | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Provider | Mistral | Alibaba |
| Context Window | 65,536 tokens | 131,072 tokens |
| Agent Suitability | 87/100 | N/A |
| Time to First Token (TTFT) | 320 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2024-12-11 | 2025-07-29 |
API Pricing Comparison
Input Price per Million Tokens
Mixtral 8x22B
$0.50
Qwen3 30B A3B Instruct 2507
$0.05
Output Price per Million Tokens
Mixtral 8x22B
$1.00
Qwen3 30B A3B Instruct 2507
$0.19
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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
- ▸MoE architecture — efficient inference for its capability tier
- ▸Requires ~90GB VRAM at FP16 — 4-bit quantization recommended for single-GPU deployment
Qwen3 30B A3B Instruct 2507 Quirks & Gotchas
No developer gotchas reported.