Kimi Linear 48B A3B Instruct vs Qwen3 30B A3B Thinking 2507
Compare Kimi Linear 48B A3B Instruct by Moonshot AI against Qwen3 30B A3B Thinking 2507 by Qwen, in 7 community votes, qwen3 30b a3b thinking 2507 wins 100% of head-to-head duels, context windows of 1.0M vs 262K, tested across 35 shared challenges. Updated April 2026.
Which is better, Kimi Linear 48B A3B Instruct or Qwen3 30B A3B Thinking 2507?
Qwen3 30B A3B Thinking 2507 is the better choice overall, winning 100% of 7 blind community votes on Rival. Kimi Linear 48B A3B Instruct costs $0.3/M input tokens vs $0.071/M for Qwen3 30B A3B Thinking 2507. Context windows: 1049K vs 262K tokens. Compare their real outputs side by side below.
Key Differences Between Kimi Linear 48B A3B Instruct and Qwen3 30B A3B Thinking 2507
Kimi Linear 48B A3B Instruct is made by moonshotai while Qwen3 30B A3B Thinking 2507 is from qwen. Kimi Linear 48B A3B Instruct has a 1049K token context window compared to Qwen3 30B A3B Thinking 2507's 262K. On pricing, Kimi Linear 48B A3B Instruct costs $0.3/M input tokens vs $0.071/M for Qwen3 30B A3B Thinking 2507. In community voting, In 7 community votes, Qwen3 30B A3B Thinking 2507 wins 100% of head-to-head duels.
In 7 community votes, Qwen3 30B A3B Thinking 2507 wins 100% of head-to-head duels. Qwen3 30B A3B Thinking 2507 leads in Reasoning. Based on blind community voting from the Rival open dataset of 7+ human preference judgments for this pair.
Pick Qwen3 30B A3B Thinking 2507. In 7 blind votes, Qwen3 30B A3B Thinking 2507 wins 100% of the time. That's not luck.
Qwen3 30B A3B Thinking 2507 particularly excels in Reasoning.
Style Comparison
Qwen3 30B A3B Thinking 2507 uses 67.9x more emoji
vsAsk them anything yourself
Some models write identically. You are paying for the brand.
178 models fingerprinted across 32 writing dimensions. Free research.
185x
price gap between models that write identically
178
models
12
clone pairs
32
dimensions
279 AI models invented the same fake scientist.
We read every word. 250 models. 2.14 million words. This is what we found.
