Meta • LLM
Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...
Context Window
8K tokens
Input Price/1M
$0.65
Output Price/1M
$2.75
Parameters
—
Speed
46 tok/s
Latency (TTFT)
669ms
Max Output
8K tokens
Llama 3 70B Instruct results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 1.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 20.0 | 100.0 | — |
| SciCode | 19.0 | 100.0 | — |
| AA Coding Index | 6.8 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 57.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 0.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| HF Average | 36.9 | 100.0 | HuggingFace Open LLM Leaderboard v2 |
| AA Intelligence Index | 8.9 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 38.0 | 100.0 | — |
| IFBench | 37.0 | 100.0 | — |
| HLE | 4.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 0.0 | 100.0 | — |
Llama 3 70B Instruct is an AI model developed by Meta, classified as a large language model (LLM). It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 8K tokens, it is suitable for processing short documents and direct prompts.
Llama 3 70B Instruct is usage-based, priced at $0.65/1M input tokens and $2.75/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Llama 3 70B Instruct was evaluated on 12 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Llama 3 70B Instruct is suitable for a wide range of AI applications: high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, Llama 3 70B Instruct competes directly with similarly capable models. As an open source model, it competes with Qwen (Alibaba), Mistral, and DeepSeek, as well as proprietary models like GPT, Claude, and Gemini. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...
Llama 3 70B Instruct costs $0.65/1M input tokens and $2.75/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Llama 3 70B Instruct scored: Terminal-Bench Hard: 1/100, LiveCodeBench: 20/100, SciCode: 19/100. See the full table above for a detailed comparison.
Yes, Llama 3 70B Instruct is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.
Llama 3 70B Instruct excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →