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Qwen: Qwen3 30B A3B Thinking 2507 vs Qwen: Qwen3.5-Flash

Head-to-head API cost, context, and performance comparison. Synced at 2:42:44 PM.

Executive Summary

When evaluating Qwen: Qwen3 30B A3B Thinking 2507 against Qwen: Qwen3.5-Flash, the pricing structure is a key differentiator. Qwen: Qwen3.5-Flash is approximately 32% more cost-effective per 1 million tokens overall.

However, when looking at raw reasoning capabilities, Qwen: Qwen3 30B A3B Thinking 2507 leads with a statistical ELO score of 1430. For tasks involving complex logic, coding, or instruction-following, developers might prefer Qwen: Qwen3 30B A3B Thinking 2507, provided their budget allows for the API burn rate.

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Raw Technical comparison

Metric
Qwen: Qwen3 30B A3B Thinking 2507
Qwen: Qwen3.5-Flash
Performance (ELO)
1430
1429
Input Cost / 1M
$0.08
$0.07
Output Cost / 1M
$0.40
$0.26
Context Window
131,072 tokens
1,000,000 tokens

Verdict

If you are looking for pure performance and capability, Qwen: Qwen3 30B A3B Thinking 2507 is statistically superior. However, if API burn rate is the primary concern, Qwen: Qwen3.5-Flash wins out aggressively in pricing.

People Also Ask

Is Qwen: Qwen3 30B A3B Thinking 2507 cheaper than Qwen: Qwen3.5-Flash?

No. Qwen: Qwen3.5-Flash is the more cost-effective model, operating at a lower price point per 1 million tokens.

Which model has the larger context window?

The Qwen: Qwen3.5-Flash model has the advantage in memory, offering a massive 1,000,000 token limit for document ingestion.

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