Meta · language model
Llama 3.1 8B Instruct: pricing, context and benchmarks
Llama 3.1 8B Instruct is Meta's model released on July 23, 2024. It costs $0.05 per million input tokens and $0.08 per million output tokens, with a 131.1K-token context window. No independent score has been published for it so far.
| Price | 29/30 | |
|---|---|---|
| Context | 4/15 | |
| Features | 12/15 |
What you need to know about Llama 3.1 8B Instruct
Meta, based in United States, put Llama 3.1 8B Instruct online on July 23, 2024. It accepts text as input and produces text. Its context window reaches 131,072 tokens, larger than 29 % of the models in the registry, and it can write up to 117,964 tokens in one go.
Released more than 2 years ago, it belongs to a generation most vendors have since superseded.
Llama 3.1 8B Instruct pricing
On OpenRouter, Llama 3.1 8B Instruct costs $0.05 per million input tokens and $0.08 per million output tokens. Counting three tokens read for every token written, its average price is $0.058 per million, more expensive than 3 % of paid models (13.1 times cheaper than the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.0001. At 1,000 tasks a day, budget $4 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.025 per million.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Llama 3.1 8B Instruct. We therefore show no made-up performance rating: only price, context and features count towards its score.
Scores published by Epoch AI on the benchmarks the site tracks:
| Benchmark | What it measures | Score | Date |
|---|---|---|---|
| GPQA Diamond | PhD-level science questions | 27 % | Jul 23, 2024 |
| OTIS Mock AIME | maths competition problems | 1.7 % | Jul 23, 2024 |
Features, privacy and local use
- It supports tool calling, so it can drive an AI agent, call APIs or MCP servers.
- It can answer in an enforced JSON format (structured outputs).
- Its knowledge stops at December 2023.
Its weights are open and downloadable from Hugging Face: you can run it on your own machines without sending your data to a third party.
Other Meta models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| Llama Guard 4 12B | Apr 30, 2025 | $0.18 | — |
| Llama 4 Maverick | Apr 5, 2025 | $0.3 | 132.2 |
| Llama 4 Scout | Apr 5, 2025 | $0.15 | 129.6 |
| Llama 3.3 70B Instruct | Dec 6, 2024 | $0.16 | — |
| Llama 3.2 1B Instruct | Sep 25, 2024 | $0.07 | — |
| Llama 3.2 3B Instruct | Sep 25, 2024 | $0.12 | — |
| Llama 3.1 70B Instruct | Jul 23, 2024 | $0.4 | — |
Verdict
Computed score: 75/100, from public data (performance, price, context, features).
Strengths
- Low price: cheaper than 97 % of paid models.
- Open weights: self-hostable, your data stays with you.
- Agent-ready: tool calling and structured JSON output.
Limitations
- No independent score published: performance cannot be verified.
- Older generation, often replaced by a newer model from the same vendor.
- Text only: reads neither images nor files.
Alternatives
- Gemma 3 4B$0.04
- gpt-oss-20b$0.04
- Gemma 3 12B$0.08
- Mistral Nemo$0.03
- Ling 3.0 Flash VL$0.09
- Ministral 3 3B 2512$0.1
Comparisons and tools
Learn more
Frequently asked questions
How much does Llama 3.1 8B Instruct cost?
$0.05 per million input tokens and $0.08 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0001.
How big is Llama 3.1 8B Instruct's context window?
131,072 tokens, roughly 98,000 words, with a maximum of 117,964 output tokens.
Can Llama 3.1 8B Instruct run locally?
Yes, its weights are published.
Is Llama 3.1 8B Instruct suitable for an AI agent?
Technically yes: it supports tool calling and structured outputs. Then check its cost for your volume.
What are the alternatives to Llama 3.1 8B Instruct?
The closest models on price and performance are Gemma 3 4B, gpt-oss-20b, and Gemma 3 12B.
Related articles
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- LLM benchmarks: how to read model rankingsOctober 24, 2026
- Running a local LLM: hardware, software, and limitationsOctober 24, 2026
- Open-source or closed models for your AI agents?October 5, 2026
Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score