Qwen 3 235B-A22B

Overview

Alibaba's flagship MoE — 235B total, 22B active per token across 128 experts. Hits 85.7 on AIME 2024 and 70.7 on LiveCodeBench, putting it in frontier-open territory.

When to pick this model

• You're running multi-GPU or a high-memory Apple Silicon machine and want frontier-open performance
• You need top-tier math and code reasoning under an Apache license
• You want MoE-class throughput (22B active) rather than dense 200B+ latency
• You're evaluating against closed frontier models and need a serious local baseline

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	142 GB
Q5_K_M	170 GB
Q8_0	250 GB
FP16 (no quantization)	470 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
AIME 2024	85.7
AIME 2025	81.5
LiveCodeBench v5	70.7

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• Frontier-open scores on AIME 2024 (85.7) and LiveCodeBench (70.7)
• Only 22B active parameters — fast for its total size
• Instruct-2507 and Thinking-2507 variants available
• Apache 2.0

Limitations

• ~142GB at Q4 — needs multi-GPU or a 192GB+ Apple Silicon host
• Not realistic for laptop or single-GPU deployment

Architecture & training

Architecture: MoE · 128 experts, 8 active · 94 layers · GQA 64Q/4KV

Training: 36T tokens. Instruct-2507 and Thinking-2507 variants (July 2025).

Quick start

ollama run qwen3:235b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.