Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 2 13B Chat on Fireworks AI?
Fireworks AI offers Llama 2 13B Chat with pay-as-you-go pricing at $0.20/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Compare Llama 2 13B Chat across 11 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport FIREWORKS_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],accounts/fireworks/models/llama-v2-13b-chatRequest 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-v2-13b-chat",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/llama-v2-13b-chat", not the LLMReference slug "llama2-13b-chat".
- 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 2 13B Chat Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Alibaba Cloud PAI-EAS | — | — |
| AWS Bedrock | $0.75 | $1.00 |
| Microsoft Foundry | $0.81 | $0.94 |
| GCP Vertex AI | $0.16 | $0.48 |
| DeepInfra | $0.13 | $0.13 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About Llama 2 13B Chat
The Llama 2 13B Chat model is a 13 billion parameter generative text model developed by Meta, optimized for conversational applications. Released on July 18, 2023, it's part of the Llama 2 family and excels in dialogue scenarios. The model leverages supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to generate coherent and contextually relevant responses. Trained on 2 trillion tokens from diverse public sources, it outperforms many open-source chat models and matches popular closed-source models in helpfulness and safety. This model is ideal for AI engineers working on chatbots, virtual assistants, and customer service automation.