DeepSeek R1 Distill Qwen 32B vs Llama 3.3 70B Instruct Comparison

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek R1 Distill Qwen 32B outperforms in 1 benchmarks (GPQA), while Llama 3.3 70B Instruct is better at 0 benchmarks.

DeepSeek R1 Distill Qwen 32B significantly outperforms across most benchmarks.

Sun Mar 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek R1 Distill Qwen 32B costs less

For input processing, DeepSeek R1 Distill Qwen 32B ($0.12/1M tokens) is 1.7x cheaper than Llama 3.3 70B Instruct ($0.20/1M tokens).

For output processing, DeepSeek R1 Distill Qwen 32B ($0.18/1M tokens) is 1.1x cheaper than Llama 3.3 70B Instruct ($0.20/1M tokens).

In conclusion, Llama 3.3 70B Instruct is more expensive than DeepSeek R1 Distill Qwen 32B.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sun Mar 15 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
Meta
Llama 3.3 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

37.2B diff

Llama 3.3 70B Instruct has 37.2B more parameters than DeepSeek R1 Distill Qwen 32B, making it 113.4% larger.

DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
Meta
Llama 3.3 70B Instruct
70.0Bparameters
32.8B
DeepSeek R1 Distill Qwen 32B
70.0B
Llama 3.3 70B Instruct

Context Window

Maximum input and output token capacity

Both models have the same input context window of 128,000 tokens. Both models can generate responses up to 128,000 tokens.

DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
Meta
Llama 3.3 70B Instruct
Input128,000 tokens
Output128,000 tokens
Sun Mar 15 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 32B is licensed under MIT, while Llama 3.3 70B Instruct uses Llama 3.3 Community License Agreement.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek R1 Distill Qwen 32B

MIT

Open weights

Llama 3.3 70B Instruct

Llama 3.3 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while Llama 3.3 70B Instruct was released on 2024-12-06.

DeepSeek R1 Distill Qwen 32B is 2 months newer than Llama 3.3 70B Instruct.

DeepSeek R1 Distill Qwen 32B

Jan 20, 2025

1.1 years ago

1mo newer
Llama 3.3 70B Instruct

Dec 6, 2024

1.3 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek R1 Distill Qwen 32B is available from DeepInfra. Llama 3.3 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Sambanova, Cerebras, Bedrock, Together, Fireworks. The availability of providers can affect quality of the model and reliability.

DeepSeek R1 Distill Qwen 32B

deepinfra logo
Deepinfra
Input Price:Input: $0.12/1MOutput Price:Output: $0.18/1M

Llama 3.3 70B Instruct

lambda logo
Lambda
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.23/1MOutput Price:Output: $0.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.40/1MOutput Price:Output: $0.40/1M
groq logo
Groq
Input Price:Input: $0.59/1MOutput Price:Output: $7.90/1M
sambanova logo
Sambanova
Input Price:Input: $0.60/1MOutput Price:Output: $1.20/1M
cerebras logo
Cerebras
Input Price:Input: $0.70/1MOutput Price:Output: $0.80/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.72/1MOutput Price:Output: $0.72/1M
together logo
Together
Input Price:Input: $0.88/1MOutput Price:Output: $0.88/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Less expensive input tokens
Less expensive output tokens
Higher GPQA score (62.1% vs 50.5%)

Detailed Comparison