Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 3.3 70B Instruct (free) on Together AI?
Together AI offers Llama 3.3 70B Instruct (free) with pay-as-you-go pricing at $1.04/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Llama 3.3 70B Instruct (free) across 11 providers to find the best fit for your use caseInput / 1M
$1.04
Output / 1M
$1.04
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install togetherAuth
export TOGETHER_API_KEY=...Call
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",Model ID
meta-llama/Llama-3.3-70B-Instruct-TurboRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "meta-llama/Llama-3.3-70B-Instruct-Turbo", not the LLMReference slug "llama-3.3-70b-instruct".
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_API_KEY; rename it only if your application config maps the new variable.
Compare Llama 3.3 70B Instruct (free) Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | $0.29 | $2.25 |
| NVIDIA NIM | — | — |
| GroqCloud | $0.59 | $0.79 |
| Together AI | $1.04 | $1.04 |
| Arcee AI | $0.60 | $1.80 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.04 |
| Output tokens | $1.04 |
Capabilities
Structured Outputs
About Llama 3.3 70B Instruct (free)
Meta: Llama 3.3 70B Instruct (free) available via OpenRouter. Pricing: $null/1M input, $null/1M output.
Get Started
Model Specs
Released2024-12-06
Parameters70B
Context66k
ArchitectureDecoder Only
Knowledge cutoff2023-12