Mistral Large 2 (Jul) offers a substantial context window but positions itself as a below-average performer in intelligence with a premium price tag, particularly for a non-reasoning model.
Mistral Large 2 (Jul) emerges as a significant offering from Mistral AI, characterized by its expansive 128k token context window. Released in July 2024, this model is positioned for general-purpose applications, particularly those requiring the processing of large volumes of text. However, initial benchmarks reveal a nuanced performance profile, placing it below average in intelligence compared to its peers, while simultaneously presenting a notably high cost structure.
The model's intelligence, as measured by the Artificial Analysis Intelligence Index, scores 22 out of a possible 100, ranking it 17th among 33 models evaluated. This places Mistral Large 2 (Jul) in the lower half of the intelligence spectrum, suggesting it may not be the optimal choice for highly complex reasoning tasks. Despite this, its substantial context window could make it suitable for tasks like extensive document summarization, information extraction from long texts, or handling multi-turn conversations where the breadth of information is more critical than deep analytical capabilities.
From a cost perspective, Mistral Large 2 (Jul) is notably expensive. With an input token price of $2.00 per 1M tokens and an output token price of $6.00 per 1M tokens on Amazon Bedrock, it significantly exceeds the average pricing for comparable models. This high cost, coupled with its below-average intelligence, necessitates careful consideration for budget-conscious applications. The blended price, calculated at a 3:1 input-to-output token ratio, stands at $3.00 per 1M tokens, reinforcing its premium positioning.
Performance metrics indicate a median output speed of 29 tokens per second and a latency (time to first token) of 0.46 seconds when deployed on Amazon. While these speeds are respectable, they must be weighed against the model's intelligence and cost. Organizations considering Mistral Large 2 (Jul) should evaluate whether its large context window and moderate speed justify the higher expenditure, especially when alternative models might offer better intelligence-to-cost ratios for specific use cases.
22 (#17 / 33 / General Purpose Models)
29 tokens/s
$2.00 per 1M tokens
$6.00 per 1M tokens
N/A tokens
0.46 seconds
| Spec | Details |
|---|---|
| Owner | Mistral |
| License | Open |
| Context Window | 128k tokens |
| Model Type | Non-Reasoning |
| Median Output Speed | 29 tokens/s |
| Latency (TTFT) | 0.46 seconds |
| Input Token Price | $2.00 per 1M tokens |
| Output Token Price | $6.00 per 1M tokens |
| Blended Price (3:1) | $3.00 per 1M tokens |
| Intelligence Index Score | 22 |
| Intelligence Rank | #17 / 33 |
Mistral Large 2 (Jul) is currently benchmarked and available on Amazon Bedrock. Given its specific performance and pricing profile, strategic provider selection is key to optimizing its use.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| General Use & High Context | Amazon Bedrock | Leverage Amazon's robust infrastructure and security for deploying the model, especially for applications requiring its large context window. | Higher cost per token compared to alternatives, requiring careful budget management. |
| Cost-Sensitive Projects | Consider alternatives | For projects where budget is a primary concern and deep reasoning isn't critical, explore other models with better intelligence-to-cost ratios. | May require re-evaluation of model capabilities and potential trade-offs in context window size. |
| Specific Mistral Features | Amazon Bedrock | If your application specifically benefits from Mistral's architectural strengths or future ecosystem features, Bedrock provides direct access. | Still subject to the model's inherent pricing and intelligence limitations. |
Note: Provider recommendations are based on current benchmark data and model availability. Always consider your specific application requirements and conduct your own testing.
Understanding the real-world cost implications of Mistral Large 2 (Jul) requires examining various common LLM workloads. The high per-token prices, especially for output, can quickly accumulate, making cost-efficient design crucial.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Scenario | Input | Output | What it represents | Estimated Cost |
| Short Q&A | 200 tokens | 100 tokens | Answering a concise question based on a short prompt. | $0.00046 |
| Article Summarization | 10,000 tokens | 500 tokens | Condensing a medium-length article into a summary. | $0.0230 |
| Content Generation | 500 tokens | 1,500 tokens | Generating a blog post or marketing copy from a detailed prompt. | $0.0100 |
| Long Document Analysis | 50,000 tokens | 1,000 tokens | Extracting key insights or data points from an extensive report. | $0.1060 |
| Chatbot Interaction (Multi-turn) | 2,000 tokens | 800 tokens | A typical multi-turn conversation with a chatbot. | $0.0098 |
| Code Generation (Small) | 1,000 tokens | 300 tokens | Generating a small function or script from a prompt. | $0.0038 |
These examples highlight that while Mistral Large 2 (Jul) can handle diverse tasks, its high token prices mean that even moderately sized workloads can incur significant costs. Output-heavy applications, in particular, will see costs escalate rapidly.
To effectively manage costs when using Mistral Large 2 (Jul), a strategic approach is essential. Given its pricing and intelligence profile, optimizing prompts and output generation is paramount.
Since output tokens are three times more expensive than input tokens, focus on generating only the necessary information. Use precise instructions to guide the model to produce concise, relevant responses.
While the context window is large, feeding the model only essential information can reduce input token count and improve relevance, potentially leading to more concise outputs.
Given its below-average intelligence for the cost, reserve Mistral Large 2 (Jul) for tasks where its large context window is a distinct advantage, and where deep reasoning is not the primary requirement.
For frequently asked questions or common prompts, cache responses to avoid repeatedly incurring inference costs. This is especially effective for static or semi-static content generation.
Mistral Large 2 (Jul) is best suited for applications requiring a very large context window, such as summarizing extensive documents, analyzing long reports, or managing complex, multi-turn conversations where the breadth of information is more critical than deep analytical reasoning. Its general-purpose nature allows it to handle a variety of NLP tasks.
Mistral Large 2 (Jul) scores 22 on the Artificial Analysis Intelligence Index, placing it below average among comparable models. This suggests it may not be the optimal choice for highly complex reasoning, problem-solving, or nuanced understanding tasks where other models might offer superior performance.
No, Mistral Large 2 (Jul) is considered expensive, with input tokens at $2.00/1M and output tokens at $6.00/1M. Its blended price is $3.00/1M tokens. This high cost, especially for output, means that applications generating significant amounts of text or requiring frequent inferences will incur substantial expenses.
Mistral Large 2 (Jul) features an impressive 128k token context window. This allows it to process and retain a vast amount of information within a single interaction, making it highly capable for tasks involving very long inputs or maintaining extensive conversational history.
On Amazon Bedrock, Mistral Large 2 (Jul) exhibits a median output speed of 29 tokens per second and a latency (time to first token) of 0.46 seconds. These metrics indicate a reasonably responsive model capable of consistent generation throughput.
Mistral Large 2 (Jul) is owned by Mistral AI. The model is available under an Open license, which typically implies more permissive usage terms, though specific details should always be verified with the official Mistral AI documentation.