Llama 3 8B Instruct
ActiveMeta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...
Overview
Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases. It has demonstrated strong...
History
Llama 3 8B Instruct became available via the Meta API on 2024-04-18.
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: 8K tokens.
-
Input modalities: text.
Recommended for: cheap.
Limitations
-
The knowledge cutoff is 27 months old — this model will not know about recent events, releases, or API changes.
-
The context window (8K 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: $0.0300 per 1M tokens
- Output: $0.0400 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-3-8b-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.
Providers & performance
4 providersMulti-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 |
|---|---|---|---|---|---|---|---|
| Together | 80tok/s | 244ms | $0.1 | $0.1 | 8K | int4 | — |
| DeepInfra | 26tok/s | 299ms | $0.03 | $0.04 | 8K | bf16 | tools · json |
| Novita | 25tok/s |
Popularity
Signals from open-source communities — not a quality measure, but useful for gauging adoption among developers.
Integrations & tooling support
- Tool calling
- Not supported
- Structured outputs
- Not supported
Price vs quality
Priced low — good for high-volume tasks. Quality tier pending more benchmark coverage.
- Quality percentile
- —
- Effective price
- $0.0375/1M
- Pricing breakdown
- $0.03/1M in
$0.04/1M out
Community ratings
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Comments
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