Mixtral 8x7B
Mistral Mixtral Generally Available Dec 2023
Mistral AI's original sparse MoE model — 46.7B total / 12.9B active params, Apache 2.0 licence, 32K context. Fast, cost-efficient, and one of the most widely deployed open-weight models.
Context
33K
tokens
Input
$0.54
per MTok
Output
$0.60
per MTok
Modalities
Input
Text Code
Output
Text Code
Code Examples
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistralai/Mixtral-8x7B-v0.1",
"messages": [
{ "role": "user", "content": "Explain quantum entanglement in one sentence." }
]
}' API Parameters
| Name | Type | Description |
|---|---|---|
frequency_penalty | number | Penalize tokens by their frequency so far. Positive values reduce repetition. |
max_completion_tokens | integer | Maximum number of tokens the model may generate in the response. |
presence_penalty | number | Penalize tokens that have appeared at all so far. Positive values encourage new topics. |
response_format | one of | Constrain output to a JSON schema or an enum (structured outputs). |
seed | integer | Deterministic seed for sampling. Same seed + same prompt produces identical output. |
stop | array | Up to 4 sequences where the API will stop generating tokens. |
stream | boolean | Stream partial responses as Server-Sent Events. |
temperature | number | Sampling temperature; higher values produce more random output. 0 is deterministic. |
tool_choice | one of | Controls which (if any) tool is called: "none", "auto", "required", or a specific tool. |
tools | array | List of tools (functions) the model may call. |
top_p | number | Nucleus sampling: consider only tokens whose cumulative probability ≥ top_p. |
Standard OpenAI-compatible parameters. Consult the provider docs for model-specific behaviour.
Benchmark Scores
| Benchmark | Score |
|---|---|
| MMLU | 70.6% |
| HumanEval | 40.2% |
| MATH | 28.4% |
Performance
140
tok / sec
output speed
Source: Mixtral paper + Artificial Analysis, April 2026
Strengths & Limitations
Best For
Apache 2.0 — fully open weights
MoE efficiency at low inference cost
Strong multilingual (5 languages)
109K+ HuggingFace downloads
Limitations
32K context window
Outperformed by newer Mistral and Llama 4 models
Older training data
Tags
Open WeightsMoEFastCodingMultilingual