Llama 3.1 8B Instruct — Meta | Modeldex

Llama 3.1 8B Instruct

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Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...

Updated 4 days agoStructured data from Modeldex catalog

FastBudget

Knowledge cutoff

Dec 31, 2023(2.3 years ago)

API release

Jul 23, 2024(1.7 years ago)

Not enough benchmark coverage yet for an Intelligence Index — needs at least 3 results across 2 categories.

Overview

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...

History

Llama 3.1 8B Instruct became available via the Meta API on 2024-07-23.

Training & availability

Training data has a knowledge cutoff of 2023-12-31 — information about events after that date is unlikely to appear in the model's responses. Meta has not released the underlying model weights — access is via their hosted API only.

Capabilities

Context window: 16K tokens.
Input modalities: text.

Recommended for: fast, cheap.

Limitations

The knowledge cutoff is 27 months old — this model will not know about recent events, releases, or API changes.
The context window (16K tokens) is modest by 2026 standards — unsuitable for processing long documents in a single request.
Text-only — cannot process images, audio, or video inputs.

Pricing

Input: $0.0200 per 1M tokens
Output: $0.0500 per 1M tokens

Use the cost calculator above to estimate monthly spend for your workload.

Quick start

Minimal example using the OpenRouter API. Copy, paste, replace the key.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-...",
)
resp = client.chat.completions.create(
    model="meta/llama-3-1-8b-instruct",
    messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}],
)
print(resp.choices[0].message.content)

Cost calculator

Estimate your monthly bill. Presets are typical workload sizes.

Input tokens / month5.0M

@ $0.02/1M

Output tokens / month2.0M

@ $0.05/1M

Input cost

$0.1

5.0M × $0.02/1M

Output cost

$0.1

2.0M × $0.05/1M

Total / month

$0.2

$2.4 / year

Providers & performance

8 providers

Multi-provider inference routes for this model — sorted by throughput. Latency is time-to-first-token; throughput is output tokens per second. Data from OpenRouter, measured over the last 30 minutes.

Provider	Throughput	Latency (TTFT)	Input $ / 1M	Output $ / 1M	Context	Quant	Supports
Groq	257tok/s	157ms	$0.05	$0.08	131K	—	tools · json
Friendli	170tok/s	108ms	$0.1	$0.1	131K	—	tools · json
Cerebras	129tok/s

Popularity

Signals from open-source communities — not a quality measure, but useful for gauging adoption among developers.

🤗

HuggingFace downloads

9.4M/ month

HuggingFace likes

5.7K

Integrations & tooling support

Tool calling: Not supported
Structured outputs: Not supported

Price vs quality

Budget pricing

Priced low — good for high-volume tasks. Quality tier pending more benchmark coverage.