Meta's large open-source model, offering high conciseness but facing challenges in speed and cost-effectiveness compared to peers.
Llama 3.1 405B, Meta's latest large open-source model, positions itself with a substantial 405 billion parameters and a generous 128k token context window. While it offers impressive conciseness in its outputs, our analysis reveals significant trade-offs in terms of intelligence, speed, and cost. This model is designed for a broad range of applications, but its performance profile suggests it may not be the most efficient choice for every workload, particularly those sensitive to budget or requiring rapid, complex reasoning.
On the Artificial Analysis Intelligence Index, Llama 3.1 405B scores 28 out of 30, placing it below the average of comparable models. This indicates that for tasks demanding advanced reasoning or nuanced understanding, it may not perform as effectively as some of its counterparts. Interestingly, despite its lower intelligence score, the model exhibits remarkable conciseness, generating only 7.1 million tokens during evaluation compared to an average of 11 million. This characteristic can be a double-edged sword: it reduces output token costs but might necessitate more elaborate prompting to extract desired detail.
Performance-wise, Llama 3.1 405B operates at an average output speed of approximately 25 tokens per second, which is notably slower than the average of 45 tokens per second observed across similar models. This slower generation rate can impact the responsiveness of real-time applications and increase overall processing times for large-scale content generation. Furthermore, its pricing structure is on the higher side, with input tokens costing $3.75 per million and output tokens $6.75 per million, significantly above the respective averages of $0.56 and $1.67. This makes Llama 3.1 405B a particularly expensive option, especially for high-volume use cases.
The model's knowledge cutoff extends to November 2023, ensuring it has access to relatively recent information. Its open-source nature provides flexibility and allows for community-driven enhancements and deployments across various platforms. However, users must carefully weigh the benefits of its large context window and conciseness against its higher operational costs and slower performance, especially when considering its intelligence capabilities relative to its price point.
28 (#19 / 30 / 405B Parameters)
24.8 tokens/s
$3.75 per 1M tokens
$6.75 per 1M tokens
7.1M tokens
0.38 seconds (TTFT)
| Spec | Details |
|---|---|
| Owner | Meta |
| License | Open |
| Model Size | 405B Parameters |
| Model Type | Large Language Model (LLM) |
| Architecture | Transformer-based |
| Context Window | 128k tokens |
| Knowledge Cutoff | November 2023 |
| Fine-tuning | Instruct-tuned |
| Intelligence Index Score | 28 / 30 |
| Average Output Speed | ~25 tokens/s |
| Input Price | $3.75 / 1M tokens |
| Output Price | $6.75 / 1M tokens |
| Conciseness (Intelligence Index) | 7.1M tokens generated |
Selecting the right API provider for Llama 3.1 405B is crucial, as performance and pricing vary significantly across platforms. Our analysis highlights key trade-offs to consider based on your primary objectives, whether it's raw speed, minimal latency, or cost efficiency.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Output Speed | Amazon Latency Optimized | Highest observed output speed (70 t/s), ideal for high-throughput generation. | Higher latency and blended price compared to other options. |
| Latency (TTFT) | Google Vertex | Fastest Time To First Token (0.38s), critical for interactive applications. | Output speed is not the absolute fastest, and blended price is moderate. |
| Blended Price | Amazon Standard | Most cost-effective overall ($2.40/M tokens) for combined input/output usage. | Slower output speed (24 t/s) and moderate latency (0.47s). |
| Input Price | Amazon Standard | Lowest input token cost ($2.40/M tokens), beneficial for prompt-heavy applications. | Slower output speed and moderate latency. |
| Output Price | Amazon Standard | Lowest output token cost ($2.40/M tokens), best for verbose generation tasks. | Slower output speed and moderate latency. |
| Balanced Performance | Together.ai Turbo | Good balance of latency (0.52s) and competitive blended price ($3.50/M tokens). | Output speed (not listed, but generally competitive) and not the absolute cheapest. |
Note: Prices and performance are subject to change and may vary based on region, specific usage patterns, and provider updates.
To illustrate the real-world cost implications of Llama 3.1 405B, let's examine a few common scenarios. These estimates use the model's average pricing ($3.75/M input, $6.75/M output) and do not account for provider-specific optimizations or discounts.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Summarizing a Long Document | 10,000 tokens | 2,000 tokens | Condensing a detailed report or article. | $0.0510 |
| Chatbot Interaction | 500 tokens | 500 tokens | A single turn in a conversational AI application. | $0.0053 |
| Code Generation | 1,000 tokens | 3,000 tokens | Generating a function or script based on a prompt. | $0.0240 |
| Creative Content Creation | 2,000 tokens | 8,000 tokens | Drafting a blog post or marketing copy. | $0.0615 |
| Data Extraction from Text | 15,000 tokens | 1,000 tokens | Identifying key entities or facts from a large body of text. | $0.0630 |
These examples highlight that while individual interactions might seem inexpensive, costs can quickly accumulate for high-volume or verbose applications due to Llama 3.1 405B's premium pricing. Strategic planning and optimization are essential to manage expenditure effectively.
Given Llama 3.1 405B's pricing structure and performance characteristics, strategic cost management is essential to maximize its value. Implementing a thoughtful approach can significantly reduce your operational expenses without compromising on quality.
Leverage Llama 3.1 405B's natural conciseness by crafting prompts that are direct and efficient. Avoid unnecessary preamble or overly verbose instructions that consume input tokens without adding value.
Different API providers offer varying price points and performance characteristics. Match your provider selection to your primary workload requirements.
While Llama 3.1 405B is inherently concise, you can further manage output token usage, which is the more expensive component.
For tasks that don't require immediate, real-time responses, consider batching multiple requests. This can help amortize the overhead associated with individual API calls and potentially improve overall throughput, especially for a model with slower generation speeds.
Given the model's premium pricing, continuous monitoring of API usage and costs is critical to prevent unexpected expenses. Set up alerts and dashboards to track consumption patterns.
Llama 3.1 405B is a large, open-source language model developed by Meta. It features 405 billion parameters and a 128k token context window, designed for a wide range of natural language processing tasks.
Llama 3.1 405B scored 28 out of 30 on the Artificial Analysis Intelligence Index, placing it below the average for comparable models. This suggests it may be less effective for complex reasoning tasks compared to top-tier alternatives.
Compared to the average, Llama 3.1 405B is considered expensive. Its input price is $3.75/M tokens and output price is $6.75/M tokens, significantly higher than the average. This makes it less cost-effective for high-volume or general-purpose applications.
Its primary strengths include a very large 128k token context window, high output conciseness (generating fewer tokens for similar information), and its open-source license, which offers flexibility and community support. It also achieves competitive low latency with optimized providers.
Its main weaknesses are its below-average intelligence score, slower output speed (around 25 tokens/s), and its high per-token pricing, which can lead to elevated operational costs.
The best provider depends on your priority: Google Vertex offers the lowest latency (0.38s), Amazon Latency Optimized provides the highest output speed (70 t/s), and Amazon Standard offers the most cost-effective blended pricing ($2.40/M tokens).
Llama 3.1 405B has a 128k token context window, allowing it to process very long inputs. Its knowledge cutoff is November 2023, meaning it has information up to that date.
While it can achieve very low Time To First Token (TTFT) with optimized providers, its slower average output speed (25 tokens/s) might make it less ideal for real-time applications requiring rapid, extensive generation. For interactive use cases, careful provider selection and prompt engineering are crucial.