import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "undi95/remm-slerp-l2-13b", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "undi95/remm-slerp-l2-13b", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"undi95/remm-slerp-l2-13b","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
ReMM-SLERP-L2-13B This page | Reasoning + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
ReMM-SLERP-L2-13B has a 6,144 tokens context window and can return up to 4,096 tokens.
ReMM-SLERP-L2-13B takes image, text as input and returns text.
Use undi95/remm-slerp-l2-13b as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
ReMM-SLERP-L2-13B is priced at input $0.6877 / 1M tokens, output $1.03155 / 1M tokens.
Yes, ReMM-SLERP-L2-13B can stream responses as they are generated.
Yes, ReMM-SLERP-L2-13B accepts image input alongside text.
ReMM-SLERP-L2-13B was built by Undi95.
It supports function calling, reasoning, structured outputs, and web search, making it usable for chat, tool use, and vision-based tasks.