Last refreshed 2026-10-17. Next refresh: weekly.
Why use Llama 3.3 70B Instruct on Fireworks AI?
Fireworks AI offers Llama 3.3 70B Instruct with pay-as-you-go pricing at $0.90/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Compare Llama 3.3 70B Instruct across 13 providers to find the best fit for your use caseInput / 1M
$0.90
Output / 1M
$0.90
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install openaiAuth
export FIREWORKS_API_KEY=...Call
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],Model ID
accounts/fireworks/models/llama-v3p3-70b-instructRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p3-70b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/llama-v3p3-70b-instruct", not the LLMReference slug "llama-3.3-70b-instruct".
- Fireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
- The examples expect FIREWORKS_API_KEY; rename it only if your application config maps the new variable.
Compare Llama 3.3 70B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | $0.29 | $2.25 |
| Fireworks AI | $0.90 | $0.90 |
| NVIDIA NIM | — | — |
| GroqCloud | $0.59 | $0.79 |
| Together AI | $1.04 | $1.04 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.90 |
| Output tokens | $0.90 |
Capabilities
Structured Outputs
About Llama 3.3 70B Instruct
Llama 3.3 70B Instruct is Meta's text-only, instruction-tuned 70B Llama 3.3 model for multilingual assistant-style chat, with a 128K-token context window and openly downloadable weights.
Get Started
Model Specs
Released2024-12-06
Parameters70B
Context128K
ArchitectureDecoder Only
Knowledge cutoff2023-12