Side-by-side comparison
Olmo 3 32B Think vs R1 Distill Qwen 32B
DeepSeek
R1 Distill Qwen 32B
{} JSON
Input / 1M
$0.2900
Output / 1M
$0.2900
| Olmo 3 32B Think | R1 Distill Qwen 32B | |
|---|---|---|
| Provider | AllenAI | DeepSeek |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 65,536 | 32,768 |
| 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 → | 0.1500 | 0.2900 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.5000 | 0.2900 |
Frequently asked questions
Which is cheaper, Olmo 3 32B Think or R1 Distill Qwen 32B?
R1 Distill Qwen 32B is cheaper than Olmo 3 32B Think on a 50/50 input/output blend by about $0.035 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, Olmo 3 32B Think or R1 Distill Qwen 32B?
Olmo 3 32B Think has the larger context window at 66k tokens versus 33k tokens for R1 Distill Qwen 32B. That means Olmo 3 32B Think can ingest about 2.0x as much text per request.
What is the difference between Olmo 3 32B Think and R1 Distill Qwen 32B?
Olmo 3 32B Think comes from AllenAI; R1 Distill Qwen 32B comes from DeepSeek. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.