Mistral Large (Feb) offers a balance of cost and performance for general-purpose applications, though it struggles with complex reasoning.
Mistral Large (Feb) emerges as a foundational model designed for a broad spectrum of general text generation tasks. Available through Amazon Bedrock, this model positions itself as a workhorse for applications that require solid output speed and reasonable latency, without demanding cutting-edge reasoning capabilities. Its 33k token context window provides ample space for many common use cases, from content creation to summarization, making it a practical choice for developers operating within the AWS ecosystem.
Performance metrics for Mistral Large (Feb) reveal a model built for efficiency in standard operations. It boasts a median output speed of 36 tokens per second, which is a respectable rate for generating substantial text volumes. Coupled with a low latency of 0.40 seconds to first token, it demonstrates suitability for interactive applications where quick initial responses are crucial. These characteristics make it a viable option for scenarios where user experience benefits from prompt feedback, even if the subsequent generation isn't exceptionally complex.
However, the model's 'Large' designation might set expectations that its intelligence score does not fully meet. With an Artificial Analysis Intelligence Index score of 12, placing it at #51 out of 54 models benchmarked, Mistral Large (Feb) is categorized among the least intelligent. This indicates that while it can process and generate text fluently, it struggles significantly with tasks requiring deep understanding, complex problem-solving, or nuanced reasoning. Its pricing structure, at $4.00 per 1M input tokens and $12.00 per 1M output tokens, is also on the higher side for its intelligence class, suggesting that careful cost management is essential.
In essence, Mistral Large (Feb) is best utilized for straightforward, high-volume text processing where the primary goal is efficient content generation rather than sophisticated cognitive tasks. Its integration with Amazon Bedrock simplifies deployment for AWS users, but its limitations in intelligence and relatively high cost for its capabilities mean that it should be strategically applied to avoid unexpected expenses or unsatisfactory results in more demanding applications.
12 (#51 / 54)
36 tokens/s
$4.00 per 1M tokens
$12.00 per 1M tokens
N/A tokens from Intelligence Index
0.40 seconds
| Spec | Details |
|---|---|
| Model Name | Mistral Large (Feb) |
| Owner | Mistral |
| License | Proprietary |
| Context Window | 33k tokens |
| API Provider | Amazon Bedrock |
| Intelligence Index | 12 (Rank #51/54) |
| Median Output Speed | 36 tokens/s |
| Median Latency (TTFT) | 0.40 seconds |
| Input Token Price | $4.00 per 1M tokens |
| Output Token Price | $12.00 per 1M tokens |
| Blended Price (3:1) | $6.00 per 1M tokens |
| Model Type | General Purpose, Non-reasoning |
| Primary Use Case | Text generation, summarization, simple Q&A |
| Release Date | February 2024 |
When considering Mistral Large (Feb), the primary access point benchmarked is Amazon Bedrock. This integration offers specific advantages and tradeoffs that influence its suitability for various deployment strategies.
While direct access from Mistral might offer different terms or features, our analysis focuses on the performance and cost implications observed through the Amazon Bedrock API.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Cost Efficiency | Amazon Bedrock | Leverages existing AWS infrastructure and billing, potentially consolidating spend and utilizing AWS credits. | May incur AWS-specific overheads and potential vendor lock-in. |
| Ease of Integration | Amazon Bedrock | Familiarity with AWS SDKs and ecosystem simplifies integration into existing AWS-centric applications. | Requires some AWS-specific knowledge and configuration. |
| Performance Consistency | Amazon Bedrock | Benefits from AWS's managed service reliability and scalable infrastructure, ensuring consistent performance. | Less direct control over underlying model versions or specific hardware optimizations. |
| Managed Service Benefits | Amazon Bedrock | Handles infrastructure, scaling, and maintenance, reducing operational burden for developers. | Limited customization options compared to self-hosting or direct API access. |
Note: Benchmarking data for Mistral Large (Feb) was exclusively available for Amazon Bedrock at the time of this analysis.
Understanding the real-world cost of Mistral Large (Feb) involves looking beyond per-token prices and considering typical usage patterns. The following scenarios illustrate how costs can accumulate based on common application workloads.
These estimates assume average token counts for input and output, providing a practical perspective on operational expenses.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Email Draft (Short) | 1,000 tokens | 500 tokens | Generating a concise, routine email. | $0.01 |
| Blog Post Draft (Medium) | 2,000 tokens | 1,500 tokens | Drafting a standard blog article or social media post. | $0.026 |
| Document Summarization (Long) | 10,000 tokens | 1,000 tokens | Extracting key information from a lengthy report or article. | $0.052 |
| Basic Chatbot Response | 200 tokens | 100 tokens | Providing a quick, factual answer in an interactive chat. | $0.002 |
| Code Generation (Small Snippet) | 500 tokens | 300 tokens | Generating a simple function or script based on a prompt. | $0.0056 |
| Product Description (E-commerce) | 800 tokens | 400 tokens | Creating a detailed product description for an online store. | $0.008 |
While individual transaction costs appear low, the cumulative effect of Mistral Large (Feb)'s pricing, especially for output tokens, can become significant in high-volume applications. Strategic prompt engineering and output management are crucial for cost control.
Optimizing costs with Mistral Large (Feb) requires a proactive approach, particularly given its pricing relative to its intelligence. Implementing smart strategies can help mitigate expenses without compromising essential functionality.
Here are several actionable tactics to ensure you get the most value from your usage of Mistral Large (Feb).
Craft prompts that are as concise and direct as possible. Avoid unnecessary context or verbose instructions that inflate input token counts.
Implement client-side or server-side logic to truncate or filter model outputs. Since output tokens are more expensive, controlling their volume is key.
For non-interactive tasks, batching requests can improve efficiency. Additionally, caching frequently requested or static responses can eliminate redundant API calls.
Recognize Mistral Large (Feb)'s strengths and limitations. Use it for tasks where its speed and context window are beneficial, but consider cheaper, smaller models for simpler or highly repetitive tasks.
Regularly review your API usage and costs. Identify patterns of high consumption and areas where optimization can be applied.
Mistral Large (Feb) is a general-purpose language model released in February 2024 by Mistral. It is designed for a variety of text generation tasks and is primarily accessible through Amazon Bedrock.
With an Artificial Analysis Intelligence Index score of 12, Mistral Large (Feb) ranks among the lower-performing models in terms of intelligence (#51 out of 54). It is best suited for non-reasoning tasks and struggles with complex problem-solving or nuanced understanding.
Mistral Large (Feb) excels at general text generation, content creation, summarization, and basic question-answering. Its decent speed and context window make it suitable for applications requiring efficient, straightforward text output.
Its cost-effectiveness depends heavily on its application. While its per-token prices ($4.00 input, $12.00 output per 1M tokens) are moderate to high for its intelligence class, it can be cost-effective for high-volume, low-complexity tasks if output is carefully managed. For complex reasoning, it is not cost-effective.
Mistral Large (Feb) features a 33,000-token context window, allowing it to process and generate text based on a substantial amount of input information.
Currently, Mistral Large (Feb) is primarily available for use via Amazon Bedrock, integrating it into the broader AWS cloud ecosystem.
The model offers a median output speed of 36 tokens per second and a low latency of 0.40 seconds to first token. This makes it a good choice for applications requiring quick initial responses and solid text generation throughput.
Its primary limitation is its low intelligence score, meaning it struggles with complex reasoning, deep understanding, and highly accurate factual recall. It is not recommended for tasks demanding nuanced interpretation or sophisticated problem-solving.