Last refreshed 2026-06-15. Next refresh: weekly.
Why use MythoMax L2 13B on Together AI?
Together AI offers MythoMax L2 13B with pay-as-you-go pricing at $0.30/1M 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 MythoMax L2 13B across 6 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="mythomax-l2-13b",mythomax-l2-13bRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="mythomax-l2-13b",
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 MythoMax L2 13B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.30 | $0.30 |
| Lepton AI API | $0.13 | $0.13 |
| Fireworks AI | $0.20 | $0.20 |
| OpenRouter | $0.06 | $0.06 |
| Novita AI | $0.09 | $0.09 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.30 |
| Output tokens | $0.30 |
Capabilities
About MythoMax L2 13B
MythoMax L2 13B is an advanced large language model developed by Gryphe, designed specifically for creative text generation, with a focus on storytelling and role-playing applications. It builds upon the Llama 2 architecture and utilizes a unique tensor merging technique that combines strengths from the MythoLogic-L2 and Huginn models. This enhances its ability to produce coherent, contextually relevant text for extended narratives and complex character interactions. The model features a substantial parameter count of 13 billion, supporting high-quality, fluent text generation.