Qwen: Qwen3 235B A22B Thinking 2507 vs Qwen: Qwen3.5-27B
Head-to-head API cost, context, and performance comparison. Synced at 11:20:23 AM.
Executive Summary
When evaluating Qwen: Qwen3 235B A22B Thinking 2507 against Qwen: Qwen3.5-27B, the pricing structure is a key differentiator. Qwen: Qwen3 235B A22B Thinking 2507 is approximately 60% more cost-effective per 1 million tokens overall.
However, when looking at raw reasoning capabilities, Qwen: Qwen3.5-27B leads with a statistical ELO score of 1120. For tasks involving complex logic, coding, or instruction-following, developers might prefer Qwen: Qwen3.5-27B, provided their budget allows for the API burn rate.
You are losing 60%
per million tokens by hardcoding Qwen: Qwen3.5-27B.
Stop guessing exactly which model to route to. Deploy the 0ms Intelligence Engine to automatically arbitrage this 60% gap in your production environment instantly.
Raw Technical comparison
Verdict
If you are looking for pure performance and capability, Tie is statistically superior. However, if API burn rate is the primary concern, Qwen: Qwen3 235B A22B Thinking 2507 wins out aggressively in pricing.
People Also Ask
Is Qwen: Qwen3 235B A22B Thinking 2507 cheaper than Qwen: Qwen3.5-27B?
Yes. Qwen: Qwen3 235B A22B Thinking 2507 is cheaper for both input and output generation compared to Qwen: Qwen3.5-27B. Exploring alternatives often yields cost reductions.
Which model has the larger context window?
Both models offer an identical context window of 262,144 tokens.