Side-by-side comparison
R1 Distill Llama 70B vs ReMM SLERP 13B
DeepSeek
R1 Distill Llama 70B
{} JSON
Input / 1M
$0.7000
Output / 1M
$0.8000
| R1 Distill Llama 70B | ReMM SLERP 13B | |
|---|---|---|
| Provider | DeepSeek | Undi95 |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 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 → | 0.7000 | 0.4500 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.8000 | 0.6500 |
Frequently asked questions
Which is cheaper, R1 Distill Llama 70B or ReMM SLERP 13B?
ReMM SLERP 13B is cheaper than R1 Distill Llama 70B on a 50/50 input/output blend by about $0.2 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, R1 Distill Llama 70B or ReMM SLERP 13B?
R1 Distill Llama 70B has the larger context window at 131k tokens versus 6k tokens for ReMM SLERP 13B. That means R1 Distill Llama 70B can ingest about 21.3x as much text per request.
What is the difference between R1 Distill Llama 70B and ReMM SLERP 13B?
R1 Distill Llama 70B 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.