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Start with Mistral Small 3.2 24B and Qwen3 32B, or choose any two current ranked language models. Compare capability evidence, price, context, provider availability, and weight access without pretending one field decides every use case.

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Current public data

Mistral Small 3.2 24B vs Qwen3 32B

Live dataset updated 9/9/2026, 9:50:24 PM UTC

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Factual comparison of Mistral Small 3.2 24B and Qwen3 32B
Field	Mistral Small 3.2 24B Mistral AI	Qwen3 32B Qwen
ModelCap position	#112	#115
Index score	36.5	34.7
Evidence	Measured2 public benchmark observations across 2 boards	Measured2 public benchmark observations across 2 boards
Input / 1M	$0.075	$0.08
Output / 1M	$0.20	$0.28
Pricing status	fresh	fresh
Context	131K	131K
Providers	2	2
Weight access	Open weights	Open weights

Decision facts

Mistral Small 3.2 24B is #112; Qwen3 32B is #115 on the same current language board.
Index scores are 36.5 for Mistral Small 3.2 24B and 34.7 for Qwen3 32B. Their published uncertainty intervals overlap, so the rank difference alone does not establish a reliable capability advantage for your workload.
Both positions use Measured evidence.
Listed output price per 1M tokens is $0.20 for Mistral Small 3.2 24B and $0.28 for Qwen3 32B. For 1,000 requests with 2,000 input and 500 output tokens each (2M input + 0.5M output), the listed-rate estimate is $0.25 for Mistral Small 3.2 24B versus $0.30 for Qwen3 32B. Mistral Small 3.2 24B costs 16.7% less in this scenario. This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the selected endpoint before budgeting.
Published context is 131,072 tokens for Mistral Small 3.2 24B and 131,072 for Qwen3 32B.

These are separate published fields, not a synthetic winner. ModelCap does not collapse price, access, context, and capability evidence into a hidden recommendation score.

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