Last refreshed 2026-04-19. Next refresh: weekly.
Why use StarCoder2 15B on Fireworks AI?
Fireworks AI offers StarCoder2 15B 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 StarCoder2 15B across 3 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"],starcoder2-15bRequest 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="starcoder2-15b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- 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 StarCoder2 15B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Fireworks AI | $0.20 | $0.20 |
| DeepInfra | $0.20 | $0.60 |
| NVIDIA NIM | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About StarCoder2 15B
StarCoder2-15B is a sophisticated large language model, expertly crafted for code generation and understanding. Developed by the BigCode project, it features 15 billion parameters and is trained on The Stack v2, a vast dataset of over 4 trillion tokens from more than 600 programming languages. Its advanced transformer decoder architecture, equipped with a grouped-query and sliding window attention mechanism and a Fill-in-the-Middle training objective, allows a context window of 16,384 tokens. In addition to generating and completing code, the model excels in tasks like code summarization and retrieving relevant snippets through natural language queries.