Last refreshed 2026-05-19. Next refresh: weekly.
Why use DeepSeek Coder 7B V1.5 on Fireworks AI?
Fireworks AI offers DeepSeek Coder 7B V1.5 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 DeepSeek Coder 7B V1.5 across 2 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/deepseek-coder-7b-base-v1p5Request 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/deepseek-coder-7b-base-v1p5",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/deepseek-coder-7b-base-v1p5", not the LLMReference slug "deepseek-coder-7b-v1.5".
- 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 DeepSeek Coder 7B V1.5 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Alibaba Cloud PAI-EAS | — | — |
| Fireworks AI | $0.20 | $0.20 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
No model capability flags are currently sourced.
About DeepSeek Coder 7B V1.5
DeepSeek Coder 7B Base V1.5 is a large language model tailored for code generation and related tasks, part of the advanced DeepSeek Coder series. It is distinguished by its proficiency in code completion, generation, and understanding across multiple programming languages. Trained on a dataset of 2 trillion tokens with 87% coding content, it employs the Llama architecture to achieve high performance on coding benchmarks. The model features a 16K token context window, enabling complex project-level code handling, and supports both English and Chinese, enhancing its multilingual capabilities.