Side-by-side comparison
R1 Distill Llama 70B vs Gemma 2 27B
DeepSeek
R1 Distill Llama 70B
{} JSON
Input / 1M
$0.7000
Output / 1M
$0.8000
| R1 Distill Llama 70B | Gemma 2 27B | |
|---|---|---|
| Provider | DeepSeek | |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 8,192 |
| 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 | 0.6500 |
| 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 Gemma 2 27B?
Gemma 2 27B is cheaper than R1 Distill Llama 70B on a 50/50 input/output blend by about $0.1 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 Gemma 2 27B?
R1 Distill Llama 70B has the larger context window at 131k tokens versus 8k tokens for Gemma 2 27B. That means R1 Distill Llama 70B can ingest about 16.0x as much text per request.
What is the difference between R1 Distill Llama 70B and Gemma 2 27B?
R1 Distill Llama 70B comes from DeepSeek; Gemma 2 27B comes from Google. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.