Last refreshed 2026-06-15. Next refresh: weekly.
Why use Toppy M 7B on Together AI?
Together AI offers Toppy M 7B with pay-as-you-go pricing at $0.20/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Toppy M 7B across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="toppy-m-7b",toppy-m-7bRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="toppy-m-7b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_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.