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Meta: Llama 3.3 70B Instruct API Pricing

Meta: Llama 3.3 70B Instruct costs $0.100 per 1M input tokens and $0.320 per 1M output tokens. Context window: 131K. Prices verified 2026-10-21 · source

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Meta: Llama 3.1 8B Instruct
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Quality score: 6.9

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Price history
2026-08-23 → 2026-10-21 · 59 daily snapshots
inputoutput

Rebuilt daily from our snapshot archive — the chart appears once a model has 5+ snapshots of history.

Community adoption
227K
HF downloads
3K
Likes

High adoption means battle-tested weights, more fine-tunes/quants, and faster bug fixes. View on HuggingFace →

Pricing behavior
18 price changes in the last 59 daily snapshots
Changes roughly every 6.4 days
Last move (2026-10-16): input -83.1%

From our own daily snapshot archive — independent of provider announcements.

Intelligence & capability
via Artificial Analysis
7.7
Intelligence index · top 86% of 288 scored
11.9
Coding index
$0.155
Blended $/1M (3:1 in:out)

Intelligence measures reasoning/knowledge/coding benchmarks. A high score with a low blended price = value pick; check the value chart to see where this model lands.

Input / 1M tokens
$0.100
Output / 1M tokens
$0.320
Context window
131K
Max output
16K
Knowledge cutoff
2023-12-31

Training data ends here — newer events require retrieval or web search.

What would Meta: Llama 3.3 70B Instruct cost you per month?

WorkloadRequests/moAvg tokens (in/out)Est. monthly cost
Light chatbot10,000800 / 400$2.08
Coding agent5,0008,000 / 2,000$7.20
RAG pipeline30,0004,000 / 500$16.80
Doc summarizer2,00020,000 / 1,000$4.64

Estimates use list prices without prompt caching. Caching can cut input cost by up to 90% on repeated context.

About Meta: Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

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Frequently asked questions

How much does Meta: Llama 3.3 70B Instruct cost?

Meta: Llama 3.3 70B Instruct costs $0.100 per 1M input tokens and $0.320 per 1M output tokens. Verified 2026-10-21 — provider source.

What is the context window?

131K tokens, with up to 16K output tokens per response. That's roughly 164 pages of text in a single request.

Does it support prompt caching?

Prompt-caching pricing is not published for this model via OpenRouter.

What's a cheaper alternative?

ScaleDown: Summarize saves about 82% on a typical workload with comparable quality.

Official resources