A highly optimized open-source model, Llama 3 8B Instruct excels in cost-efficiency and speed for a wide range of non-reasoning generative tasks.
The Llama 3 8B Instruct model, developed by Meta, represents a significant advancement in the landscape of open-source language models. Designed for efficiency and performance, this 8-billion parameter model is instruction-tuned, making it particularly adept at following directives and generating coherent, contextually relevant responses. While it may not lead in complex reasoning benchmarks, its strength lies in its ability to deliver reliable output at a highly competitive price point and impressive speeds, making it an attractive option for a myriad of practical applications.
Our comprehensive analysis of Llama 3 8B Instruct across various API providers—including Novita, Amazon Bedrock, Replicate, and Deepinfra—reveals a nuanced picture of its real-world performance. We've benchmarked key metrics such as time to first token (latency), output speed (tokens per second), and pricing structures (blended, input, and output token costs). This detailed evaluation helps to identify optimal providers based on specific operational priorities, from minimizing latency to maximizing throughput or achieving the lowest overall cost.
Despite its position at the lower end of the Artificial Analysis Intelligence Index, scoring 7 out of 55, Llama 3 8B Instruct distinguishes itself through its exceptional value proposition. It is particularly well-suited for tasks that do not demand deep reasoning or extensive knowledge recall beyond its February 2023 cutoff, such as content generation, summarization, and basic conversational AI. Its 8k token context window further supports a broad array of use cases where moderate input and output lengths are common.
The model's average output speed of 68.1 tokens per second, while slightly below the overall average for all models benchmarked, is highly competitive within its class. Coupled with its moderately priced token costs—$0.04 per 1 million input tokens and $0.15 per 1 million output tokens—Llama 3 8B Instruct stands out as a pragmatic choice for developers and businesses looking to deploy powerful AI capabilities without incurring prohibitive expenses. The ongoing evolution of Llama 3 endpoints, with some providers transitioning to Llama 3.1, underscores the dynamic nature of this ecosystem and the continuous pursuit of improved performance.
7 (#47 / 55 / 8B Parameters)
68.1 tokens/s
$0.04 per 1M tokens
$0.15 per 1M tokens
N/A
0.31s s (Deepinfra)
| Spec | Details |
|---|---|
| Model Name | Llama 3 8B Instruct |
| Developer | Meta |
| License | Open |
| Parameters | 8 Billion |
| Model Type | Instruction-Tuned |
| Context Window | 8k tokens |
| Knowledge Cutoff | February 2023 |
| Intelligence Index Score | 7 / 55 |
| Average Output Speed | 68.1 tokens/s |
| Average Input Price | $0.04 / 1M tokens |
| Average Output Price | $0.15 / 1M tokens |
| Primary Use Cases | Content Generation, Summarization, Chatbots, Code Assistance |
Selecting the right API provider for Llama 3 8B Instruct can significantly impact your application's performance and cost-effectiveness. Our benchmarks highlight distinct advantages for each provider across key metrics.
Consider your primary objective—whether it's raw speed, minimal latency, or the lowest possible cost—to make an informed decision.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Latency | Deepinfra | Consistently delivers the fastest Time To First Token (TTFT) at 0.31s, crucial for interactive applications. | Output speed (48 t/s) is lower than Amazon or Novita. |
| Highest Throughput | Amazon Bedrock | Offers the highest output speed at 84 tokens/s, ideal for batch processing and high-volume generation. | Significantly higher blended price ($0.38/M tokens) compared to other providers. |
| Best Blended Value | Deepinfra / Novita | Both offer the lowest blended price at $0.04/M tokens, making them highly cost-effective. | Deepinfra has lower output speed; Novita has higher latency. Choose based on your speed/latency priority. |
| Balanced Performance | Novita | Good balance of output speed (73 t/s) and very low blended price ($0.04/M tokens), with moderate latency. | Latency (0.73s) is higher than Deepinfra and Amazon. |
| Developer-Friendly | Replicate | Offers a competitive output speed (66 t/s) and moderate latency (0.43s), often favored for ease of integration. | Higher blended price ($0.10/M tokens) than Deepinfra or Novita. |
Note: Some providers are actively deprecating Llama 3 endpoints in favor of Llama 3.1. Always verify endpoint availability and pricing directly with the provider.
Understanding the real-world cost of using Llama 3 8B Instruct involves more than just looking at per-token prices. Here are several common scenarios with estimated costs, based on the model's average input price of $0.04/M tokens and output price of $0.15/M tokens.
These examples illustrate how the model's efficiency translates into tangible savings for various generative tasks.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Short Email Draft | 200 tokens | 150 tokens | Generating a concise, professional email response. | $0.0000305 |
| Blog Post Outline | 500 tokens | 300 tokens | Creating a structured outline for a blog article. | $0.000065 |
| Customer Service Response | 300 tokens | 200 tokens | Automating a standard reply to a customer inquiry. | $0.000042 |
| Code Snippet Generation | 100 tokens | 100 tokens | Producing a small, functional block of code. | $0.000019 |
| Product Description | 400 tokens | 250 tokens | Crafting a compelling description for an e-commerce item. | $0.0000535 |
| Summarize Article (Medium) | 2000 tokens | 200 tokens | Condensing a medium-length article into key points. | $0.00011 |
These examples demonstrate that Llama 3 8B Instruct offers extremely low per-use costs for typical generative tasks. Even with thousands of requests, the total expenditure remains highly manageable, making it an excellent choice for scaling applications.
Optimizing the cost of using Llama 3 8B Instruct involves strategic choices in prompt design, provider selection, and usage patterns. Here are key strategies to maximize efficiency and minimize expenditure.
Since both input and output tokens incur costs, crafting concise yet effective prompts is paramount. Avoid unnecessary verbosity in your instructions and guide the model to produce equally brief, high-value outputs.
As our benchmarks show, provider pricing varies significantly. Align your choice with your primary cost driver (input vs. output tokens) and overall budget.
For non-interactive tasks, batching multiple requests can improve overall throughput and potentially reduce per-request overhead, depending on the provider's API.
For frequently requested or static content, caching model responses can drastically reduce API calls and associated costs.
If the model tends to be verbose, implement post-processing to truncate or filter outputs to the desired length, ensuring you only pay for what you truly need.
Llama 3 8B Instruct is an 8-billion parameter language model developed by Meta. It is specifically instruction-tuned, meaning it's optimized to follow human instructions and generate helpful, relevant responses, making it suitable for a wide array of generative AI tasks.
The 8B variant is the smallest in the Llama 3 family, designed for efficiency and speed. While it may not match the reasoning capabilities or extensive knowledge of larger models like Llama 3 70B, it offers a superior balance of performance and cost for tasks that don't require deep, complex reasoning.
No, Llama 3 8B Instruct is not primarily designed for complex reasoning. Our Intelligence Index score of 7/55 places it at the lower end for such capabilities. It excels in generative tasks like content creation, summarization, and basic Q&A where direct instruction following is key, rather than intricate logical deduction.
Llama 3 8B Instruct has an 8,000-token context window. This means it can process and generate text based on approximately 8,000 tokens of input and previous conversation history, which is sufficient for many common applications.
The model's training data includes information up to February 2023. It will not have knowledge of events or developments that occurred after this date, unless supplemented by external data through techniques like Retrieval Augmented Generation (RAG).
Yes, Llama 3 8B is released under an open license by Meta. This allows developers and organizations to use, modify, and deploy the model for a wide range of applications, fostering innovation within the AI community.
Based on our benchmarks, Deepinfra offers the lowest Time To First Token (TTFT) for Llama 3 8B at 0.31 seconds. If low latency is critical for your application, Deepinfra would be the recommended provider.
Yes, some API providers are beginning to deprecate their Llama 3 endpoints in favor of newer Llama 3.1 endpoints. It is always advisable to check with your chosen provider for the latest information regarding model availability and recommended versions to ensure long-term compatibility and access to the most up-to-date models.