Is Llama 3.1 70B Instruct a chat model?

Yes—Llama 3.1 70B Instruct is exposed as a chat/completions-style endpoint on LLM.API.

What is the context length for Llama 3.1 70B Instruct?

Reported context for Llama 3.1 70B Instruct is 128K tokens. Always verify the active provider row if multiple providers are listed.

What modalities does Llama 3.1 70B Instruct support?

Llama 3.1 70B Instruct accepts text and produces text according to its architecture metadata on LLM.API.

When should I choose Llama 3.1 70B Instruct?

You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need Llama 3.1 70B Instruct.

How do I call Llama 3.1 70B Instruct via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "llama-3.1-70b-instruct" and your LLM.API key. See the code snippet on this page.

What are limitations of Llama 3.1 70B Instruct?

Like other API models, Llama 3.1 70B Instruct can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

How is Llama 3.1 70B Instruct priced on LLM.API?

Listed pricing metadata shows: In $0.72 / 1M tokens · Out $0.72 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

Can I use tools or structured outputs with Llama 3.1 70B Instruct?

Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

Which providers serve Llama 3.1 70B Instruct?

LLM.API currently lists: aws-bedrock. Availability can vary by region and account.

Does Llama 3.1 70B Instruct support streaming?

Yes—at least one listed provider advertises streaming.

Where is the canonical page for Llama 3.1 70B Instruct?

https://llmapi.ai/models/meta-llama-llama-3-1-70b-instruct/

Which endpoint do I call for Llama 3.1 70B Instruct?

POST https://api.llmapi.ai/v1/chat/completions with model "llama-3.1-70b-instruct" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Llama 3.1 70B Instruct on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does Llama 3.1 70B Instruct support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format.

Llama 3.1 70B Instruct: Self-Hosting & Deployment Guide

Call Llama 3.1 70B Instruct through LLM.API when you want Meta-family text generation with unified auth, provider choice, and production-friendly defaults.

Start Using API

What is Llama 3.1 70B Instruct?

Llama 3.1 70B Instruct is a Meta chat model exposed on LLM.API under id `llama-3.1-70b-instruct`. llama-3.1-70b-instruct provided by aws-bedrock. It is wired for API access through LLM.API with OpenAI-compatible patterns. Teams use it when they need reliable text generation with text inputs and text outputs over an OpenAI-compatible API.

How to run Llama 3.1 70B Instruct yourself

Open weights (llama3.1), 70.6B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

Llama 3.1 70B Instruct VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	141.1 GB	Estimate	70.6B params × 2 bytes
FP8	72.7 GB	File size	RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8
AWQ 4-bit	39.8 GB	File size	hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4
MLX 4-bit	39.7 GB	File size	mlx-community/Meta-Llama-3.1-70B-Instruct-4bit

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy Llama 3.1 70B Instruct on your hardware

Datacenter GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit
NVIDIA H200141 GB	2×	1×	1×	1×
NVIDIA H10080 GB	3×	2×	1×	1×
NVIDIA A100 80GB80 GB	3×	2×	1×	1×
NVIDIA L40S48 GB	4×	2×	1×	1×

GPUs needed per variant.

Hardware	MLX 4-bit
Mac, 512 GBM3 Ultra	Fits
Mac, 192 GBM2 Ultra	Fits
Mac, 128 GBM4 Max / M3 Max	Fits
Mac, 64 GBM4 Pro / Max	Fits

Assumes ~75% of unified memory is usable by the GPU.

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	Fits	Fits	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	Fits	Fits	Fits

Whole-instance GPU memory; public instance specs.

Serve it yourself

vLLMSGLangMLX (Mac)

pip install -U vllm
vllm serve meta-llama/Llama-3.1-70B-Instruct

pip install 'sglang[all]'
python -m sglang.launch_server --model-path meta-llama/Llama-3.1-70B-Instruct

pip install -U mlx-lm
mlx_lm.generate --model mlx-community/Meta-Llama-3.1-70B-Instruct-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, with custom volume discounts on request.

Get API key See API code ↓

Providers

LLM.API routes Llama 3.1 70B Instruct to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

aws-bedrock$0.72 in

$0.72 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
aws-bedrock	in $0.72; out $0.72 per 1M tokens	128K tokens	tools, streaming, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test Llama 3.1 70B Instruct right here — free to start.

Llama 3.1 70B Instruct

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Llama 3.1 70B Instruct through the OpenAI-compatible API — POST /v1/chat/completions.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to llama-3.1-70b-instruct and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.1-70b-instruct",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="llama-3.1-70b-instruct",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "llama-3.1-70b-instruct",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="llama-3.1-70b-instruct",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "llama-3.1-70b-instruct",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID llama-3.1-70b-instruct · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Llama 3.1 70B Instruct in production

Best workloadsLLM.API features

What the model is good at.

Multilingual drafting
Drafts and translates professional content across major business languages.
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows. Reflects Meta positioning for this endpoint.
Instruction following
Follows detailed system and user instructions with strong adherence to format and tone. Relevant for `llama-3.1-70b-instruct` workloads on LLM.API.
Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully. Grounded in the model's chat role rather than generic chat claims.
Safety-aware replies
Supports product policies with refusals and cautious handling of sensitive topics. Reflects Meta positioning for this endpoint.

What you get when you call Llama 3.1 70B Instruct through LLM.API.

Unified AI Routing
Practical: Reach Llama 3.1 70B Instruct and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Practical: Compare provider price points and keep spend visible as you scale Llama 3.1 70B Instruct.
More performance, less spend.
Reliability Layer
Production: Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Practical: Trace prompts, tokens, and errors for Llama 3.1 70B Instruct alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Swap Llama 3.1 70B Instruct for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

You want OpenAI-compatible chat completions through a single LLM.API key (Llama 3.1 70B Instruct)
You need provider failover options exposed for this model id (Llama 3.1 70B Instruct)
You need a general-purpose text model for assistants, agents, or content workflows (Llama 3.1 70B Instruct)
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Llama 3.1 70B Instruct)

Avoid if...

You need guaranteed real-time hard latency SLAs without benchmarking the provider
You require on-prem only deployment with no cloud inference
You need pure embedding, OCR, or media generation instead of chat
Your use case depends on unpublished proprietary benchmarks not listed here

LLM API rating for Llama 3.1 70B Instruct

★ 3.6/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena

4.7Production adoptionMost-downloaded open-weight family

2.0Benchmark qualityLMArena text rank #283, Elo 1261

3.0Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.6Coding 3.6Tool & function calling 4.3

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about Llama models

LMArena#283 · Elo 1261Arena coding#282 · Elo 1260Speed—Sources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Llama 3.1 70B Instruct.

Independent testers were underwhelmed by Llama 4 coding performance relative to the launch messaging around its very long context window.
The architecture shift to mixture-of-experts and native multimodality is seen as the real story for teams self-hosting open weights.
Most reviewers position Llama as an integration and deployment choice rather than a benchmark leader.

SOURCES

Frequently Asked Questions

What is Llama 3.1 70B Instruct?
llama-3.1-70b-instruct provided by aws-bedrock. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `llama-3.1-70b-instruct`.
Is Llama 3.1 70B Instruct a chat model?
Yes—Llama 3.1 70B Instruct is exposed as a chat/completions-style endpoint on LLM.API.
What is the context length for Llama 3.1 70B Instruct?
Reported context for Llama 3.1 70B Instruct is 128K tokens. Always verify the active provider row if multiple providers are listed.
What modalities does Llama 3.1 70B Instruct support?
Llama 3.1 70B Instruct accepts text and produces text according to its architecture metadata on LLM.API.
When should I choose Llama 3.1 70B Instruct?
You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need Llama 3.1 70B Instruct.
How do I call Llama 3.1 70B Instruct via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "llama-3.1-70b-instruct" and your LLM.API key. See the code snippet on this page.
What are limitations of Llama 3.1 70B Instruct?
Like other API models, Llama 3.1 70B Instruct can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
How is Llama 3.1 70B Instruct priced on LLM.API?
Listed pricing metadata shows: In $0.72 / 1M tokens · Out $0.72 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Can I use tools or structured outputs with Llama 3.1 70B Instruct?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
Which providers serve Llama 3.1 70B Instruct?
LLM.API currently lists: aws-bedrock. Availability can vary by region and account.
Does Llama 3.1 70B Instruct support streaming?
Yes—at least one listed provider advertises streaming.
Where is the canonical page for Llama 3.1 70B Instruct?
https://llmapi.ai/models/meta-llama-llama-3-1-70b-instruct/

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Llama 3.1 70B Instruct: Self-Hosting & Deployment Guide

What is Llama 3.1 70B Instruct?

How to run Llama 3.1 70B Instruct yourself

Llama 3.1 70B Instruct VRAM requirements

GPU requirements: deploy Llama 3.1 70B Instruct on your hardware

Serve it yourself

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Llama 3.1 70B Instruct in production

Multilingual drafting

Long-context synthesis

Instruction following

Structured outputs

Safety-aware replies

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth