Model Comparison

DeepSeek-R1 vs DeepSeek-R1 Distill Qwen 32B

Compare the listed model data for DeepSeek-R1 by DeepSeek and DeepSeek-R1 Distill Qwen 32B by DeepSeek. 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

DeepSeek-R1: 128K tokens; DeepSeek-R1 Distill Qwen 32B: 128K tokens. The listed context-window values are equal. DeepSeek-R1 is listed for coding, reasoning, long-context tasks; DeepSeek-R1 Distill Qwen 32B is listed for coding, reasoning, long-context tasks. Shared catalog benchmark entries: MATH-500: DeepSeek-R1 97.3%; DeepSeek-R1 Distill Qwen 32B 94.3%; AIME 2024: DeepSeek-R1 79.8%; DeepSeek-R1 Distill Qwen 32B 72.6%; GPQA Diamond: DeepSeek-R1 71.5%; DeepSeek-R1 Distill Qwen 32B 62.1%; SWE-bench Verified: DeepSeek-R1 49.2%; DeepSeek-R1 Distill Qwen 32B 31.1%; LiveCodeBench: DeepSeek-R1 65.9%; DeepSeek-R1 Distill Qwen 32B 57.2%. 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, MATH-500: DeepSeek-R1 − DeepSeek-R1 Distill Qwen 32B = +3 percentage points (97.3% vs 94.3%); AIME 2024: DeepSeek-R1 − DeepSeek-R1 Distill Qwen 32B = +7.2 percentage points (79.8% vs 72.6%); GPQA Diamond: DeepSeek-R1 − DeepSeek-R1 Distill Qwen 32B = +9.4 percentage points (71.5% vs 62.1%); SWE-bench Verified: DeepSeek-R1 − DeepSeek-R1 Distill Qwen 32B = +18.1 percentage points (49.2% vs 31.1%); LiveCodeBench: DeepSeek-R1 − DeepSeek-R1 Distill Qwen 32B = +8.7 percentage points (65.9% vs 57.2%).
  • The first listed parameter quantities are 671B total (37B active via MoE) and 32B; the larger quantity is 20.97× the smaller. Total and active MoE parameters are not directly equivalent.
  • The catalog release dates are January 2025 and January 2025, about 0 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 DeepSeek-R1 if…

Choose DeepSeek-R1 when your task aligns with catalog-listed uses or strengths such as Competition mathematics and proof verification, Advanced software engineering: debugging complex systems, production-grade algorithms, Scientific research in physics, chemistry, biology, Enterprise AI agents needing transparent, auditable reasoning.

Choose DeepSeek-R1 Distill Qwen 32B if…

Choose DeepSeek-R1 Distill Qwen 32B when your task aligns with catalog-listed uses or strengths such as Mathematical reasoning on accessible hardware, Reasoning enterprise applications that cannot afford 671B infrastructure, Best reasoning at 32B scale — distilled from 671B R1 teacher, 94.3% MATH-500 — exceptional mathematical reasoning at 32B.

DeepSeek-R1

Browse DeepSeek models

Groundbreaking open reasoning model — rivals OpenAI o1 on math, science, and coding. Transparent chain-of-thought reasoning.

Parameters: 671B total (37B active via MoE)

Context: 128K tokens

Catalog pricing: Open source MIT license — free to self-host. DeepSeek API: $0.14/MTok input (cache hit), $0.55/MTok input, $2.19/MTok output

DeepSeek Coding · Reasoning · Long Context Open Source

View DeepSeek-R1 model profile

DeepSeek-R1 Distill Qwen 32B

Browse DeepSeek models

Powerful R1 distillation in a 32B package — matches GPT-4-class performance on math, science, and complex reasoning.

Parameters: 32B

Context: 128K tokens

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

DeepSeek Coding · Reasoning · Long Context Open Source

View DeepSeek-R1 Distill Qwen 32B model profile

Feature Comparison — DeepSeek-R1 vs DeepSeek-R1 Distill Qwen 32B

Catalog specifications for DeepSeek-R1 and DeepSeek-R1 Distill Qwen 32B
Specification DeepSeek-R1 DeepSeek-R1 Distill Qwen 32B
Provider DeepSeek DeepSeek
Parameters 671B total (37B active via MoE) 32B
Context window 128K tokens 128K tokens
Maximum output Not listed Not listed
Catalog pricing Open source MIT license — free to self-host. DeepSeek API: $0.14/MTok input (cache hit), $0.55/MTok input, $2.19/MTok output MIT — free. Third-party API ~$0.14-0.30/MTok
Listed input/output cost per 1M tokens input $0.55, output $2.19 per 1M tokens Separate numeric input/output rates not listed
License MIT MIT
Coding support Listed Listed
Reasoning support Listed Listed
Vision input Not listed Not listed
Multimodal Not listed Not listed
Open weights Yes Yes
Benchmark: MATH-500 97.3% 94.3%
Benchmark: AIME 2024 79.8% 72.6%
Benchmark: GPQA Diamond 71.5% 62.1%
Benchmark: SWE-bench Verified 49.2% 31.1%
Benchmark: LiveCodeBench 65.9% 57.2%

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 DeepSeek-R1 and DeepSeek-R1 Distill Qwen 32B?
DeepSeek-R1: 128K tokens; DeepSeek-R1 Distill Qwen 32B: 128K tokens. The listed context-window values are equal. DeepSeek-R1 is listed for coding, reasoning, long-context tasks; DeepSeek-R1 Distill Qwen 32B is listed for coding, reasoning, long-context tasks. Shared catalog benchmark entries: MATH-500: DeepSeek-R1 97.3%; DeepSeek-R1 Distill Qwen 32B 94.3%; AIME 2024: DeepSeek-R1 79.8%; DeepSeek-R1 Distill Qwen 32B 72.6%; GPQA Diamond: DeepSeek-R1 71.5%; DeepSeek-R1 Distill Qwen 32B 62.1%; SWE-bench Verified: DeepSeek-R1 49.2%; DeepSeek-R1 Distill Qwen 32B 31.1%; LiveCodeBench: DeepSeek-R1 65.9%; DeepSeek-R1 Distill Qwen 32B 57.2%. 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 DeepSeek-R1 and DeepSeek-R1 Distill Qwen 32B?
MATH-500: DeepSeek-R1 97.3%; DeepSeek-R1 Distill Qwen 32B 94.3%; AIME 2024: DeepSeek-R1 79.8%; DeepSeek-R1 Distill Qwen 32B 72.6%; GPQA Diamond: DeepSeek-R1 71.5%; DeepSeek-R1 Distill Qwen 32B 62.1%; SWE-bench Verified: DeepSeek-R1 49.2%; DeepSeek-R1 Distill Qwen 32B 31.1%; LiveCodeBench: DeepSeek-R1 65.9%; DeepSeek-R1 Distill Qwen 32B 57.2%. 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?
DeepSeek-R1: 128K tokens; DeepSeek-R1 Distill Qwen 32B: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for DeepSeek-R1 and DeepSeek-R1 Distill Qwen 32B?
DeepSeek-R1: input $0.55, output $2.19 per 1M tokens (Open source MIT license — free to self-host. DeepSeek API: $0.14/MTok input (cache hit), $0.55/MTok input, $2.19/MTok output). DeepSeek-R1 Distill Qwen 32B: Separate numeric input/output rates not listed (MIT — free. Third-party API ~$0.14-0.30/MTok). 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 DeepSeek-R1 and DeepSeek-R1 Distill Qwen 32B?
The catalog lists DeepSeek-R1 as January 2025 and DeepSeek-R1 Distill Qwen 32B as January 2025, about 0 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 DeepSeek-R1 and DeepSeek-R1 Distill Qwen 32B?
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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