Side-by-side comparison
MiniMax M2.7 vs Qwen3 235B A22B Thinking 2507
Qwen
Qwen3 235B A22B Thinking 2507
🔧 Tools
{} JSON
Input / 1M
$0.1495
Output / 1M
$1.4950
| MiniMax M2.7 | Qwen3 235B A22B Thinking 2507 | |
|---|---|---|
| Provider | MiniMax | Qwen |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 196,608 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.2600 | 0.1495 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 1.2000 | 1.4950 |
Frequently asked questions
Which is cheaper, MiniMax M2.7 or Qwen3 235B A22B Thinking 2507?
MiniMax M2.7 is cheaper than Qwen3 235B A22B Thinking 2507 on a 50/50 input/output blend by about $0.0923 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, MiniMax M2.7 or Qwen3 235B A22B Thinking 2507?
MiniMax M2.7 has the larger context window at 197k tokens versus 131k tokens for Qwen3 235B A22B Thinking 2507. That means MiniMax M2.7 can ingest about 1.5x as much text per request.
What is the difference between MiniMax M2.7 and Qwen3 235B A22B Thinking 2507?
MiniMax M2.7 comes from MiniMax; Qwen3 235B A22B Thinking 2507 comes from Qwen. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.