C.W.K.
Stream
Lesson 06 of 11 · published

Mistral Family: Efficient European Open Weights

~10 min · mistral, mixtral, moe

Level 0Token
0 XP0/94 lessons0/10 achievements
0/120 XP to next level120 XP to go0% complete

Mistral AI, founded by ex-Meta and ex-Google researchers, focuses on efficient open-weight models. The lineage is short but consequential.

ModelTotal paramsActive paramsArchitectureContext
Mistral 7B (2023)7B7BDense + sliding-window attention32K
Mixtral 8×7B (2024)47B13BMoE, top-2 of 8 experts32K
Mixtral 8×22B (2024)141B39BMoE, top-2 of 8 experts64K
Mistral Small 3 (24B)24B24BDense32K
Mistral Large 3 (2024)~675B~41BMoE
Mistral Small 4 (2025)119B6BMoE, 128 experts top-4256K

Mixtral 8×22B specifics: 56 layers, d_model=6144, 48 Q heads with 8 KV heads (GQA), SwiGLU, RoPE, multilingual (English, French, Italian, German, Spanish), Apache 2.0 license. The 8×22B and Mistral Small 4 specifically demonstrated that aggressive MoE (low active / high total) could match much larger dense models.

Code

Inspect Mixtral 8×22B's MoE config·python
from transformers import AutoConfig
cfg = AutoConfig.from_pretrained("mistralai/Mixtral-8x22B-v0.1")
print("num_experts:", cfg.num_local_experts)        # 8
print("experts per token:", cfg.num_experts_per_tok) # 2
print("hidden:", cfg.hidden_size)                    # 6144
print("intermediate:", cfg.intermediate_size)        # 16384
print("num_q_heads:", cfg.num_attention_heads)       # 48
print("num_kv_heads:", cfg.num_key_value_heads)      # 8 (GQA)

External links

Exercise

Run the same prompt through Mistral 7B and Mixtral 8x7B (e.g., via together.ai or replicate). Compare quality, speed, and cost. Where does the MoE's larger total knowledge show up? Where does its 13B active param count show up in latency?

Progress

Progress is local-only — sign in to sync across devices.
Spotted a bug or have feedback on this page?Report an Issue

Comments 0

🔔 Reply notifications (sign in)
Sign inPlease sign in to comment.

No comments yet — be the first.