Llama 3.1 8B: token counter & pricing
Meta · approximate, within ±3% of reference · pricing as of 2026-04-26.
- Provider
- Meta
- API model ID
meta-llama/llama-3.1-8b-instruct- Context window
- 128,000 tokens
- Input price
- $0.18 per 1M tokens
- Output price
- $0.18 per 1M tokens
- Tokenizer accuracy
- approximate, within ±3% of reference
- Pricing as of
- 2026-04-26
Open the counter to count tokens for Llama 3.1 8B in real time.
What is Llama 3.1 8B?
Llama 3.1 8B is Meta's small open-weights model — the cheapest and fastest member of the Llama 3.1 family. 128k context, runs on consumer hardware (a single 24GB GPU is enough), and competitively priced when hosted.
How tokens are counted here
Llama 8B uses the same SentencePiece BPE tokenizer as 70B and 405B. Browser approximation, accurate to ~±3% of the reference tokenizer. Marked ≈±3% in the results.
When to use Llama 8B
- Self-hosted small workloads. Runs on a laptop GPU; no API bill at all.
- High-volume classification, extraction, routing. Same use cases as GPT-4o mini and Gemini Flash, with the openness benefit.
- Edge / on-device inference when you need an open model in a constrained environment.
- Fine-tuning experiments where 8B is small enough to iterate fast.
When not to use 8B:
- Anything requiring multi-step reasoning. The capability gap to 70B is real.
- Long-context retrieval tasks. Quality drops sharply past ~16k tokens in practice.
Pricing notes
At ~$0.18 per million tokens (input and output, indicative via Together), Llama 8B is in the same price bracket as Gemini 2.5 Flash ($0.075/$0.30) and GPT-4o mini ($0.15/$0.60).
The honest comparison: for most price-sensitive cloud workloads, Gemini Flash beats Llama 8B on quality at similar price. Llama 8B's edge is self-hosting — running it locally costs only your hardware.
Common questions
What hardware do I need to run Llama 8B?
Quantized (4-bit): 6GB VRAM. Unquantized (16-bit): 16GB VRAM. A consumer RTX 3090 or 4090 is comfortable. Apple M-series with 16GB+ RAM also works via Ollama or LM Studio.
Is 8B good enough to replace GPT-4o mini in production?
Sometimes. Run a labeled eval set on your specific task — 8B can match mini on routine extraction and classification, and trail it badly on anything requiring careful reasoning.
What's the difference between Llama 3.1 8B and Llama 3.2 3B?
3.2 introduced smaller (1B, 3B) and larger multimodal (11B vision, 90B vision) models. 3.1 8B remains the default text-only small Llama for most workloads — 3.2 3B is for tighter hardware constraints, accepting a quality drop.
Compare Llama 3.1 8B to other models
- Llama 3.1 405B (Meta, $3.50/$3.50)
- Llama 3.1 70B (Meta, $0.59/$0.79)
- GPT-4o mini (OpenAI, $0.15/$0.60)
- DeepSeek V3 (DeepSeek, $0.27/$1.10)
- Gemini 2.5 Flash (Google, $0.07/$0.30)