Mistral AI • LLM
Mixtral 8x7B Instruct is a pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion...
Context Window
33K tokens
Input Price/1M
$0.45
Output Price/1M
$0.70
Parameters
—
Max Output
16K tokens
Mistral: Mixtral 8x7B Instruct results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 7.0 | 100.0 | Artificial Analysis official API |
| SciCode | 3.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 39.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 29.9 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 38.7 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 29.0 | 100.0 | Artificial Analysis official API |
| HLE | 5.0 | 100.0 | — |
Mistral: Mixtral 8x7B Instruct is an AI model developed by Mistral AI, classified as a large language model (LLM). It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 33K tokens, it is suitable for processing medium-sized documents like articles, reports, and code sections.
Mistral: Mixtral 8x7B Instruct is usage-based, priced at $0.45/1M input tokens and $0.7/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Mistral: Mixtral 8x7B Instruct was evaluated on 8 different benchmarks, covering categories like Coding, Knowledge, Math, overall, Reasoning. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Mistral: Mixtral 8x7B Instruct is suitable for a wide range of AI applications: automation with tool calling (API integration, databases, external systems), high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, Mistral: Mixtral 8x7B Instruct competes directly with similarly capable models. Mistral AI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Mixtral 8x7B Instruct is a pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion...
Mistral: Mixtral 8x7B Instruct costs $0.45/1M input tokens and $0.7/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Mistral: Mixtral 8x7B Instruct scored: LiveCodeBench: 7/100, SciCode: 3/100, MMLU-Pro: 39/100. See the full table above for a detailed comparison.
Yes, Mistral: Mixtral 8x7B Instruct is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.
Mistral: Mixtral 8x7B Instruct excels at general-purpose language tasks. It supports tool calling for API integrations and automation.
Last updated: June 01, 2026 • View methodology →