AI Model Intelligence

Meta-Llama-3-70B-Instruct

meta/llama-3-70b-instruct

By Meta · family: llama · released 2024-04-18 · knowledge: 2023-12

$0.510
Input / 1M tokens
$0.740
Output / 1M tokens
8K
Context window
2K
Max output

Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.

Capabilities

✗ Tool calling✗ Reasoning? Structured output✗ Attachments✓ Open weights✓ Temperature control
Modalities: input text · output text

Model fit scores

0–100 · higher is better

These scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.

Coding6
  • Tool calling0/40
  • Structured output0/20
  • Reasoning0/10
  • Context window (100K → 1M)0/20
  • Provider availability6/10
Agents6
  • Tool calling0/35
  • Structured output0/25
  • Reasoning0/15
  • Output token limit0/15
  • Provider availability6/10
JSON / structured output28
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control10/10
  • Price-friendly for high-volume18/20
Cost efficiency60
  • Headline price (log-scaled)60/95
  • Has prompt-cache pricing0/5
Long context0
  • Context ≥ 100K0/100
Production-readiness81
  • Number of independent providers30/40
  • Has published per-token price20/20
  • Context window ≥ 8K8/15
  • No data inconsistencies across providers8/10
  • Official model (not derivative)15/15

Cost Efficiency Index

Open full calculator →

Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.

ScenarioCostAssumption
RAG answer
per 1,000 RAG answers
$2.92
< $0.01 per request
5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot.
Support ticket triage
per 10,000 tickets
$5.84
< $0.01 per request
1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support.
Data extraction
per 1,000 documents
$1.39
< $0.01 per request
2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines.
Code review
per 1,000 PRs
$4.82
< $0.01 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$6.56
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

Pricing detail

Recommended pricing from novita-ai · meta-llama/llama-3-70b-instruct

$0.510
Input
$0.740
Output

Cheapest provider: github-models · Unknown input + Unknown output

Available on 6 providers

ProviderProvider model idInput / 1MOutput / 1MContextReleased
Azure
azure
meta-llama-3-70b-instruct$2.68$3.548K2024-04-18
NovitaAI
novita-ai
meta-llama/llama-3-70b-instruct$0.510$0.7408K2024-04-25
Kilo Gateway
kilo
meta-llama/llama-3-70b-instruct$0.510$0.7408K2024-07-23
Azure Cognitive Services
azure-cognitive-services
meta-llama-3-70b-instruct$2.68$3.548K2024-04-18
LLM Gateway
llmgateway
llama-3-70b-instruct$0.510$0.7408K2024-04-18
GitHub Models
github-models
meta/meta-llama-3-70b-instructUnknownUnknown8K2024-04-18

Data inconsistencies across providers

  • release_date varies (span 96d): 2024-04-18, 2024-04-25, 2024-07-23

Different providers report different values for this model. Quick facts above use the representative provider; consult the table for per-provider truth.

Frequently asked questions

How much does Meta-Llama-3-70B-Instruct cost?

Meta-Llama-3-70B-Instruct costs $0.510 per 1M input tokens and $0.740 per 1M output tokens, sourced from novita-ai. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of Meta-Llama-3-70B-Instruct?

Meta-Llama-3-70B-Instruct has a context window of 8K tokens, with a max output of 2K tokens per reply. This is the total combined size of prompt + completion.

Does Meta-Llama-3-70B-Instruct support tool calling?

No. Meta-Llama-3-70B-Instruct does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does Meta-Llama-3-70B-Instruct support structured output / JSON mode?

Support for structured output / JSON-schema-constrained decoding is not reported for Meta-Llama-3-70B-Instruct in our data source. Verify with Meta's official documentation before relying on it in production.

Can Meta-Llama-3-70B-Instruct accept image input?

No. Meta-Llama-3-70B-Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is Meta-Llama-3-70B-Instruct open-weight?

Yes. Meta-Llama-3-70B-Instruct's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.

What are the best alternatives to Meta-Llama-3-70B-Instruct?

If Meta-Llama-3-70B-Instruct doesn't fit, consider Meta-Llama-3.1-8B-Instruct, Llama-3.3-70B-Instruct, Llama 4 Maverick 17B 128E Instruct FP8. Each one targets the same use case — see the Related links below for direct head-to-head pages.

Where does this data come from?

All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.

Explore more

More Meta models

Capability lists this model is in

See also

Last updated:

Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.