Large Language ModelText GenerationMixtral FamilyEnriched

Mixtral 8x7B

Mixtral 8x7B is a sparse mixture-of-experts large language model by Mistral AI, combining eight 7B expert networks with conditional routing for high efficiency. It targets performance competitive with much larger dense models (around Llama 2 70B level) while remaining relatively lightweight and fully open source.

by Mistral AIReleased 2023-12-11Apache 2.0
Context Window
32K
Parameters
8x7B (MoE)
API Access
Available

Key Capabilities

  • +General-purpose text generation and chat
  • +Strong coding assistance across multiple languages
  • +Good math and logical reasoning for an open model
  • +Efficient deployment via sparse MoE (2 of 8 experts active per token)
  • +Multilingual understanding and generation (European languages in particular)
  • +Instruction-following via Instruct variants
  • +Open-weight deployment on-premise or in private clouds

Limitations

  • -Sparse MoE routing can lead to occasional instability or inconsistency across similar prompts
  • -Knowledge is frozen at training cutoff and may be outdated for recent events
  • -Weaker than top-tier frontier models on complex reasoning and long-horizon planning
  • -No native vision or multimodal capabilities
  • -Safety and alignment rely on external prompting/guardrails; base model may produce unsafe or biased content

Benchmark Performance

conversation

conversation

Chatbot Arena Elo

1114.0Elo
conversation

Multi-Turn Benchmark

8.5score

coding

coding

HumanEval

40.2%

math

math

Grade School Math 8K

74.4%

reasoning

reasoning

BIG-Bench Hard

69.7%
reasoning

HellaSwag

86.7%
reasoning

Massive Multitask Language Understanding

70.6%
reasoningsource

WinoGrande

84.0%

Alternatives & Comparisons

Dense 70B open model from Meta; stronger single-token capacity but less efficient than sparse MoE for some workloads.

Strengths
  • + Widely adopted and well-documented
  • + Good general performance
Weaknesses
  • - Heavier to serve than Mixtral 8x7B
  • - Older architecture vs newer open models

Proprietary OpenAI model with strong instruction-following and tooling ecosystem; accessed via API only.

Strengths
  • + Highly optimized for chat and tools
  • + Backed by robust infrastructure and monitoring
Weaknesses
  • - Closed weights; cannot self-host
  • - Quality can vary across updates