Model Comparison

Qwen3 32B vs Apertus 70B Instruct

Compare the listed model data for Qwen3 32B by Qwen and Apertus 70B Instruct by KAIST AI. Benchmark and pricing records are not a controlled test and may change.

Data last updated: 2026-10-16. Verify current model details with each provider.

What the listed data says

Qwen3 32B: 128K tokens; Apertus 70B Instruct: 128K tokens. The listed context-window values are equal. Qwen3 32B is listed for coding, reasoning, agentic workflows, long-context tasks; Apertus 70B Instruct is listed for coding, reasoning, long-context tasks. Shared catalog benchmark entries: MMLU: Qwen3 32B 92.0%; Apertus 70B Instruct 84.0%; MATH: Qwen3 32B 94.0%; Apertus 70B Instruct 82.0%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.

Computed differences from catalog data

  • The listed context windows are 128K tokens and 128K tokens; the larger is 1× the smaller.
  • On shared numeric benchmark entries, MMLU: Qwen3 32B − Apertus 70B Instruct = +8 percentage points (92.0% vs 84.0%); MATH: Qwen3 32B − Apertus 70B Instruct = +12 percentage points (94.0% vs 82.0%).
  • The first listed parameter quantities are 32B and 70B; the larger quantity is 2.19× the smaller. Total and active MoE parameters are not directly equivalent.
  • The catalog release dates are April 2025 and September 2025, about 5 months apart at the precision supplied.

Cost example: 1M input and 1M output tokens

A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.

Choose Qwen3 32B if…

Choose Qwen3 32B when your task aligns with catalog-listed uses or strengths such as Production coding assistants for enterprise software teams, Mathematical reasoning and data science, Complex document analysis and long-form reporting, Multilingual enterprise applications.

Choose Apertus 70B Instruct if…

Choose Apertus 70B Instruct when your task aligns with catalog-listed uses or strengths such as Korean-English bilingual enterprise applications, Academic and research AI in Korea, 70B instruction model from Korea's KAIST — top engineering university, Strong Korean and English bilingual performance.

Qwen3 32B

Browse Qwen models

32B dense Qwen3 — excels at complex coding, mathematics, and deep multi-step reasoning. Top performer in its size class.

Parameters: 32B

Context: 128K tokens

Catalog pricing: Open source — free. Third-party API ~$0.14-0.30/MTok

Qwen Coding · Reasoning · Agentic · Long Context Open Source

View Qwen3 32B model profile

KAIST AIK
Apertus 70B Instruct

Browse KAIST AI models

KAIST AI's 70B instruction model — strong reasoning and academic task performance.

Parameters: 70B

Context: 128K tokens

Catalog pricing: Check KAIST AI for license terms

KAIST AI Coding · Reasoning · Long Context Open Source

View Apertus 70B Instruct model profile

Feature Comparison — Qwen3 32B vs Apertus 70B Instruct

Catalog specifications for Qwen3 32B and Apertus 70B Instruct
Specification Qwen3 32B Apertus 70B Instruct
Provider Qwen KAIST AI
Parameters 32B 70B
Context window 128K tokens 128K tokens
Maximum output Not listed Not listed
Catalog pricing Open source — free. Third-party API ~$0.14-0.30/MTok Check KAIST AI for license terms
Listed input/output cost per 1M tokens input $0.3, output $0.7 per 1M tokens Separate numeric input/output rates not listed
License Apache 2.0 Custom
Coding support Listed Listed
Reasoning support Listed Listed
Vision input Not listed Not listed
Multimodal Not listed Not listed
Open weights Yes Yes
Benchmark: MMLU 92.0% 84.0%
Benchmark: MATH 94.0% 82.0%

Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.

Frequently Asked Questions

What is different between Qwen3 32B and Apertus 70B Instruct?
Qwen3 32B: 128K tokens; Apertus 70B Instruct: 128K tokens. The listed context-window values are equal. Qwen3 32B is listed for coding, reasoning, agentic workflows, long-context tasks; Apertus 70B Instruct is listed for coding, reasoning, long-context tasks. Shared catalog benchmark entries: MMLU: Qwen3 32B 92.0%; Apertus 70B Instruct 84.0%; MATH: Qwen3 32B 94.0%; Apertus 70B Instruct 82.0%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for Qwen3 32B and Apertus 70B Instruct?
MMLU: Qwen3 32B 92.0%; Apertus 70B Instruct 84.0%; MATH: Qwen3 32B 94.0%; Apertus 70B Instruct 82.0%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
Qwen3 32B: 128K tokens; Apertus 70B Instruct: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for Qwen3 32B and Apertus 70B Instruct?
Qwen3 32B: input $0.3, output $0.7 per 1M tokens (Open source — free. Third-party API ~$0.14-0.30/MTok). Apertus 70B Instruct: Separate numeric input/output rates not listed (Check KAIST AI for license terms). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for Qwen3 32B and Apertus 70B Instruct?
The catalog lists Qwen3 32B as April 2025 and Apertus 70B Instruct as September 2025, about 5 months apart at the precision supplied.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between Qwen3 32B and Apertus 70B Instruct?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.

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