Meta: Llama 3.1 70B Instruct
Llama 3.1 70B Instruct by Meta-llama is designed for text processing with a context length of 131,072 tokens and supports up to 16,384 completion tokens. It can handle text inputs but lacks reasoning capabilities; structured output support is not provided. This model also integrates tools for enhanced functionality. For those needing robust text-based applications within budget constraints, Llama 3.1 stands out with a blended benchmark score of 9.5 across independent evaluations. At $0.4 per million input and output tokens, it offers competitive pricing, making it suitable for projects requiring cost-effective yet capable AI solutions.
Benchmark results
Independent, published benchmarks. Blended score 9.5 across 1 benchmark, last refreshed 2026-08-01. How scoring works →
| Benchmark | Measures | Score |
|---|---|---|
| AI Index | broad capability composite | 11.1 |
- Model ID
- meta-llama/llama-3.1-70b-instruct
- Vendor
- meta-llama
- Released
- July 2024
- Tokenizer
- Llama3
- Input Modalities
- text
- Output Modalities
- text
- Max Output
- 16,384 tokens
- Tool Calling
- ✓ supported
- Structured Output
- ✓ supported
- Reasoning Mode
- not supported
- Vision
- text only
- Audio
- no
- Moderated
- no
What it costs in practice
Computed from the current $0.40/M input and $0.40/M output rates. Run your own numbers →
| Job | Tokens | Cost |
|---|---|---|
| Summarize a 50-page report | 30k in / 1.5k out | $0.01 |
| Classify 1,000 customer emails | 500k in / 50k out | $0.22 |
| A month of a busy support chatbot | 5M in / 2M out | $2.80 |
Similar models
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Meta: Llama 3.2 3B Instruct
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Quick answers
- How much does Meta: Llama 3.1 70B Instruct cost?
- $0.40 per million input tokens and $0.40 per million output tokens.
- What is Meta: Llama 3.1 70B Instruct's context window?
- 131,072 tokens, roughly 196 pages of text in a single request.
- Does Meta: Llama 3.1 70B Instruct support tool calling?
- It supports tool calling, structured output.
- Can Meta: Llama 3.1 70B Instruct process images?
- No, it is text-only on the input side.