Side-by-side comparison
R1 Distill Qwen 32B vs ReMM SLERP 13B
DeepSeek
R1 Distill Qwen 32B
{} JSON
Input / 1M
$0.2900
Output / 1M
$0.2900
| R1 Distill Qwen 32B | ReMM SLERP 13B | |
|---|---|---|
| Provider | DeepSeek | Undi95 |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 32,768 | 6,144 |
| 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 → | 0.2900 | 0.4500 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.2900 | 0.6500 |
Frequently asked questions
Which is cheaper, R1 Distill Qwen 32B or ReMM SLERP 13B?
R1 Distill Qwen 32B is cheaper than ReMM SLERP 13B on a 50/50 input/output blend by about $0.26 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, R1 Distill Qwen 32B or ReMM SLERP 13B?
R1 Distill Qwen 32B has the larger context window at 33k tokens versus 6k tokens for ReMM SLERP 13B. That means R1 Distill Qwen 32B can ingest about 5.3x as much text per request.
What is the difference between R1 Distill Qwen 32B and ReMM SLERP 13B?
R1 Distill Qwen 32B comes from DeepSeek; ReMM SLERP 13B comes from Undi95. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.