Mixtral 8x7B — Mistral AI | Modeldex

Mixtral 8x7B

Active

Open-weights mixture-of-experts model with GPT-3.5 class performance.

Updated 3 days agoStructured data from Modeldex catalog

AgenticOpen source

Not enough benchmark coverage yet for an Intelligence Index — needs at least 3 results across 2 categories.

Overview

Mixtral 8x7B uses a sparse mixture-of-experts architecture (8 experts, 2 active per token) to deliver GPT-3.5 class performance at lower inference cost than a dense 45B model. Apache-2.0 licensed.

History

Mixtral 8x7B was released on 2023-12-11.

Training & availability

Weights are publicly available under the Apache-2.0 license, making this an open-weight model suitable for on-prem deployment and fine-tuning.

Capabilities

Context window: 32K tokens.
Max output: 8K tokens.
Input modalities: text.

Recommended for: agentic, open-source.

Limitations

The context window (32K tokens) is modest by 2026 standards — unsuitable for processing long documents in a single request.
Text-only — cannot process images, audio, or video inputs.

Quick start

Minimal example using the OpenRouter API. Copy, paste, replace the key.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-...",
)
resp = client.chat.completions.create(
    model="mistral/mixtral-8x7b",
    messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}],
)
print(resp.choices[0].message.content)

Cost calculator

Estimate your monthly bill. Presets are typical workload sizes.

Input tokens / month5.0M

@ $0.54/1M

Output tokens / month2.0M

@ $0.54/1M

Input cost

$2.7

5.0M × $0.54/1M

Output cost

$1.08

2.0M × $0.54/1M

Total / month

$3.78

$45.36 / year

Integrations & tooling support

Tool calling: Supported
Structured outputs: Not supported

Price vs quality

Budget pricing

Priced low — good for high-volume tasks. Quality tier pending more benchmark coverage.