Side-by-side comparison
ReMM SLERP 13B vs GPT-3.5 Turbo Instruct
OpenAI
GPT-3.5 Turbo Instruct
{} JSON
Input / 1M
$1.5000
Output / 1M
$2.0000
| ReMM SLERP 13B | GPT-3.5 Turbo Instruct | |
|---|---|---|
| Provider | Undi95 | OpenAI |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 6,144 | 4,095 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | json_mode | json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.4500 | 1.5000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.6500 | 2.0000 |
Frequently asked questions
Which is cheaper, ReMM SLERP 13B or GPT-3.5 Turbo Instruct?
ReMM SLERP 13B is cheaper than GPT-3.5 Turbo Instruct on a 50/50 input/output blend by about $1.2 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, ReMM SLERP 13B or GPT-3.5 Turbo Instruct?
ReMM SLERP 13B has the larger context window at 6k tokens versus 4k tokens for GPT-3.5 Turbo Instruct. That means ReMM SLERP 13B can ingest about 1.5x as much text per request.
What is the difference between ReMM SLERP 13B and GPT-3.5 Turbo Instruct?
ReMM SLERP 13B comes from Undi95; GPT-3.5 Turbo Instruct comes from OpenAI. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.