Last refreshed 2026-05-19. Next refresh: weekly.
Why use Toppy M 7B on Fireworks AI?
Fireworks AI offers Toppy M 7B 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 Toppy M 7B 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/toppy-m-7bRequest 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/toppy-m-7b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/toppy-m-7b", not the LLMReference slug "toppy-m-7b".
- 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 Toppy M 7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.20 | $0.20 |
| Fireworks AI | $0.20 | $0.20 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About Toppy M 7B
Toppy M 7B is a 7-billion parameter large language model developed by Undi, designed for advanced natural language processing and sophisticated model interactions. It can handle real-time decision-making in AI-driven systems and dynamic content generation, making it highly compatible with leading AI development tools and platforms. The model supports enhanced tokenization and effective handling of special tokens. Various quantization formats, like GGUF, are available, offering trade-offs between model size, memory requirements, and performance. Users need to consider the appropriate quantization method for efficient integration into computational environments. However, the model’s limitations may include memory and processing power challenges.