Magistral Medium 1

Balanced performance with competitive output pricing

Magistral Medium 1

Magistral Medium 1 offers a compelling blend of competitive output pricing and a substantial context window, making it suitable for verbose text generation tasks despite its below-average intelligence score.

Text-to-Text40k ContextProprietaryMistralCost-Effective OutputHigh Verbosity

Magistral Medium 1 emerges as a notable contender in the landscape of general-purpose language models, particularly for applications where output cost efficiency and a generous context window are paramount. Developed by Mistral, this proprietary model distinguishes itself through its highly competitive output pricing and an impressive 40,000-token context window, enabling it to handle extensive input and generate comprehensive responses.

While its Artificial Analysis Intelligence Index score of 33 places it below the average of 44 for comparable models, Magistral Medium 1 compensates with its economic output. This makes it an attractive option for tasks that require generating large volumes of text, such as content creation, summarization of lengthy documents, or detailed report generation, where the sheer quantity of output tokens can quickly accumulate costs with other models.

A key characteristic of Magistral Medium 1 is its exceptional verbosity. During the Intelligence Index evaluation, it produced 150 million tokens, significantly surpassing the average of 28 million. This high verbosity, while contributing to its lower intelligence ranking (as it often generates more text to answer a query), can be leveraged in scenarios where detailed, expansive outputs are desired. Users should be mindful of this trait, as it directly impacts the total token count and, consequently, the overall cost, even with its favorable output pricing.

The model's input pricing at $2.00 per 1M tokens is somewhat above the average of $1.60, but this is largely offset by its output pricing of $5.00 per 1M tokens, which is half the average of $10.00. This pricing structure suggests that Magistral Medium 1 is optimized for workloads with a higher output-to-input token ratio, positioning it as a strategic choice for specific use cases where generating extensive content is a primary requirement.

Scoreboard

Intelligence

33 (69 / 101)

Below average intelligence (average 44), but highly verbose, contributing to its ranking.

Output speed

N/A tokens/sec

Output speed data was not available for this benchmark.

Input price

$2.00 USD per 1M tokens

Somewhat expensive compared to the average of $1.60.

Output price

$5.00 USD per 1M tokens

Competitively priced, significantly below the average of $10.00.

Verbosity signal

150M Output tokens

Extremely high verbosity, generating 150M tokens compared to an average of 28M.

Provider latency

N/A ms

Latency data was not available for this benchmark.

Technical specifications

Spec	Details
Owner	Mistral
License	Proprietary
Context Window	40,000 tokens
Input Type	Text
Output Type	Text
Intelligence Index Score	33 (out of 100)
Intelligence Index Rank	#69 / 101
Input Price	$2.00 / 1M tokens
Output Price	$5.00 / 1M tokens
Verbosity (Intelligence Index)	150M tokens generated
Evaluation Cost	$793.78 (for Intelligence Index)

What stands out beyond the scoreboard

Where this model wins

Cost-Effective Output: With an output price of $5.00 per 1M tokens, it's significantly cheaper than the average, making it ideal for high-volume text generation.
Generous Context Window: A 40,000-token context window allows for processing and generating long, complex documents and conversations.
High Verbosity for Detailed Content: Its tendency to generate extensive output is a strength for tasks requiring comprehensive explanations, detailed reports, or creative writing.
Balanced Pricing Strategy: Despite slightly higher input costs, the low output price creates an overall cost advantage for output-heavy applications.
Reliable for Text Generation: A solid choice for tasks where the primary goal is to produce large amounts of text, even if the absolute 'intelligence' isn't top-tier.

Where costs sneak up

Below-Average Intelligence: A score of 33 means it may struggle with highly complex reasoning, nuanced understanding, or tasks requiring deep analytical capabilities.
Higher Input Price: At $2.00 per 1M tokens, its input cost is above average, which can add up for applications with frequent, large inputs but minimal output.
Excessive Verbosity: While a strength for some, its high verbosity can lead to unnecessary token consumption and higher costs if not managed, especially for concise answer requirements.
Unknown Speed Metrics: The lack of output speed data makes it difficult to assess its real-time performance for latency-sensitive applications.
Proprietary Lock-in: Being a proprietary model from Mistral, users are tied to a single vendor's ecosystem and terms.

Provider pick

Choosing the right API provider for Magistral Medium 1 involves balancing its unique cost structure and performance characteristics with your specific application needs. Given its competitive output pricing and high verbosity, the optimal provider strategy will focus on maximizing these strengths while mitigating its lower intelligence score.

Since Magistral Medium 1 is a proprietary model from Mistral, the primary provider will be Mistral's own API. However, understanding its cost profile helps in making strategic decisions about its deployment and integration within your broader AI stack.

Priority	Pick	Why	Tradeoff to accept
Primary API Provider	Mistral API	Direct access to the model, optimized for its architecture. Best for leveraging its competitive output pricing and large context window.	Reliance on a single vendor; potential for higher input costs if not carefully managed.
Cost-Optimized Workloads	Mistral API (with token management)	Ideal for applications where output volume is high and cost is a major concern. Focus on minimizing input tokens and maximizing output utility.	Requires careful prompt engineering to avoid excessive verbosity leading to unnecessary costs.
Integration with Orchestration	Mistral API via LangChain/LlamaIndex	Integrate Magistral Medium 1 into complex workflows, leveraging its strengths for specific steps like content generation or summarization.	Adds an abstraction layer, potentially introducing minor overhead; requires careful management of context and token usage across steps.
Hybrid AI Architectures	Mistral API alongside specialized models	Pair Magistral Medium 1 with more intelligent or specialized models (e.g., for reasoning or data extraction) to create a robust, cost-effective solution.	Increased complexity in system design and data flow; requires careful routing of tasks to the appropriate model.

Note: As Magistral Medium 1 is a proprietary model by Mistral, the primary API provider is Mistral itself. The 'Provider Pick' focuses on strategic deployment considerations rather than alternative vendors.

Real workloads cost table

Magistral Medium 1's distinct profile—competitive output pricing, high verbosity, and a substantial context window—makes it particularly well-suited for specific real-world applications. Understanding how its characteristics translate into practical scenarios can help in estimating costs and optimizing its use.

Below are several common workloads, illustrating how Magistral Medium 1 might perform and what the associated costs could look like, based on its benchmarked pricing.

Scenario	Input	Output	What it represents	Estimated cost
Scenario	Input	Output	What it represents	Estimated Cost
Long-form Content Generation	5,000 tokens (detailed brief)	50,000 tokens (article draft)	Generating a comprehensive blog post, report, or creative story from a detailed outline. Leverages high verbosity and low output cost.	$0.10 (Input) + $0.25 (Output) = $0.35
Document Summarization	30,000 tokens (research paper)	10,000 tokens (executive summary)	Condensing a lengthy document into a concise summary. Benefits from large context window and competitive output pricing.	$0.60 (Input) + $0.05 (Output) = $0.65
Customer Support Response Generation	1,000 tokens (customer query + history)	3,000 tokens (detailed response)	Automating detailed responses to customer inquiries, providing comprehensive information. High verbosity can be useful here.	$0.02 (Input) + $0.015 (Output) = $0.035
Code Documentation	8,000 tokens (codebase snippet)	15,000 tokens (detailed documentation)	Generating extensive comments, explanations, or API documentation for software code. Utilizes context and verbosity.	$0.16 (Input) + $0.075 (Output) = $0.235
Legal Contract Analysis (Drafting)	20,000 tokens (contract terms)	40,000 tokens (redlined draft/suggestions)	Assisting in drafting or redlining legal documents, where detailed suggestions and explanations are required.	$0.40 (Input) + $0.20 (Output) = $0.60
Educational Explanations	2,000 tokens (complex concept)	8,000 tokens (in-depth explanation)	Creating detailed educational content or explanations for students. High verbosity supports thoroughness.	$0.04 (Input) + $0.04 (Output) = $0.08

Magistral Medium 1 shines in scenarios demanding extensive text output and large context handling, where its competitive output pricing can lead to significant cost savings. However, users must be mindful of its higher input cost and inherent verbosity, which necessitate careful prompt engineering to ensure efficiency and avoid generating superfluous tokens.

How to control cost (a practical playbook)

Effectively managing costs with Magistral Medium 1 requires a strategic approach that leverages its strengths while mitigating its weaknesses. Given its unique pricing model and high verbosity, optimizing token usage is paramount. Here's a playbook for maximizing value.

Prioritize Output-Heavy Workloads

Magistral Medium 1's most significant cost advantage lies in its output pricing. Focus its deployment on tasks where the ratio of output tokens to input tokens is high.

Content Generation: Ideal for drafting articles, marketing copy, creative stories, or detailed reports where extensive output is expected.
Summarization of Long Documents: Use its large context window to ingest lengthy texts and generate comprehensive summaries.
Detailed Explanations: Leverage its verbosity for educational content, technical documentation, or in-depth customer support responses.

Strategic Prompt Engineering for Verbosity

While high verbosity can be a strength, it can also lead to unnecessary token consumption. Implement prompt engineering techniques to guide the model's output length and focus.

Specify Length Constraints: Include explicit instructions like "Generate a 500-word summary" or "Provide a concise answer in 3 sentences."
Use Clear Directives: Guide the model to focus on key information and avoid tangential details.
Iterative Refinement: For critical applications, consider a two-step process: generate verbose output, then use a more concise model or a second prompt to refine and shorten.

Optimize Input Token Usage

Magistral Medium 1 has a slightly higher input price. Minimize input tokens where possible without sacrificing necessary context.

Pre-process Inputs: Remove irrelevant information, boilerplate text, or redundant data before sending it to the model.
Contextual Compression: Use techniques like RAG (Retrieval Augmented Generation) to fetch only the most relevant chunks of information rather than sending entire documents.
Batch Processing: For similar tasks, consider batching inputs to reduce API call overhead, though this doesn't directly reduce token cost, it can improve overall efficiency.

Monitor and Analyze Token Consumption

Regularly track your token usage for both input and output to identify patterns and areas for optimization. Most API providers offer dashboards or logging capabilities for this.

Set Usage Alerts: Configure alerts to notify you when token consumption approaches predefined thresholds.
A/B Test Prompts: Experiment with different prompts and observe their impact on output length and quality to find the most cost-effective approach.
Cost Attribution: If using the model across multiple applications, attribute costs to specific features or teams to understand where spending is concentrated.

Consider Hybrid Model Architectures

For tasks requiring higher intelligence or very concise answers, consider using Magistral Medium 1 in conjunction with other models.

Task Routing: Route simple, verbose generation tasks to Magistral Medium 1, while sending complex reasoning or highly precise tasks to more capable, potentially more expensive models.
Multi-stage Pipelines: Use Magistral Medium 1 for initial drafting or brainstorming, then pass the output to a smaller, more focused model for refinement or summarization.

FAQ

What is Magistral Medium 1's primary strength?

Magistral Medium 1's primary strength lies in its highly competitive output pricing and a substantial 40,000-token context window. This makes it exceptionally cost-effective for applications requiring the generation of large volumes of text, such as content creation, detailed summarization, or extensive report drafting.

How does its intelligence score compare to other models?

Magistral Medium 1 scored 33 on the Artificial Analysis Intelligence Index, which is below the average of 44 for comparable models. This indicates it may not perform as well on tasks requiring complex reasoning, nuanced understanding, or highly precise answers compared to top-tier models.

Is Magistral Medium 1 suitable for tasks requiring concise answers?

While capable of generating concise answers with careful prompting, Magistral Medium 1 is inherently verbose, having generated 150 million tokens during its evaluation compared to an average of 28 million. This means it tends to produce more text than average, which can lead to higher costs if not managed with specific length constraints in prompts.

What is the cost difference between input and output tokens?

Magistral Medium 1 has an input price of $2.00 per 1M tokens, which is slightly above the average. However, its output price is $5.00 per 1M tokens, significantly below the average of $10.00. This pricing structure favors workloads with a high output-to-input token ratio.

What is the context window size for Magistral Medium 1?

Magistral Medium 1 features a generous context window of 40,000 tokens. This allows the model to process and retain a significant amount of information from the input, making it suitable for tasks involving long documents, extended conversations, or complex data sets.

Who is the owner and what is the license of Magistral Medium 1?

Magistral Medium 1 is owned by Mistral and operates under a proprietary license. This means its usage is governed by Mistral's terms and conditions, and it is not open-source.

How can I optimize costs when using Magistral Medium 1?

To optimize costs, focus on output-heavy tasks, use strategic prompt engineering to control verbosity, pre-process inputs to minimize token count, and continuously monitor your token usage. For tasks requiring higher intelligence, consider a hybrid approach by pairing it with other models.

Magistral Medium 1