Model Comparison

DeepSeek R1 Distill Llama 70B vs Llama 3.2 90B Instruct

DeepSeek R1 Distill Llama 70B significantly outperforms across most benchmarks. DeepSeek R1 Distill Llama 70B is 2.1x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek R1 Distill Llama 70B outperforms in 1 benchmarks (GPQA), while Llama 3.2 90B Instruct is better at 0 benchmarks.

DeepSeek R1 Distill Llama 70B significantly outperforms across most benchmarks.

Sun Apr 05 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek R1 Distill Llama 70B costs less

For input processing, DeepSeek R1 Distill Llama 70B ($0.10/1M tokens) is 3.5x cheaper than Llama 3.2 90B Instruct ($0.35/1M tokens).

For output processing, DeepSeek R1 Distill Llama 70B ($0.40/1M tokens) costs the same as Llama 3.2 90B Instruct ($0.40/1M tokens).

In conclusion, Llama 3.2 90B Instruct is more expensive than DeepSeek R1 Distill Llama 70B.*

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

Lowest available price from all providers
Sun Apr 05 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Llama 70B
Input tokens$0.10
Output tokens$0.40
Best providerDeepinfra
Meta
Llama 3.2 90B Instruct
Input tokens$0.35
Output tokens$0.40
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

19.4B diff

Llama 3.2 90B Instruct has 19.4B more parameters than DeepSeek R1 Distill Llama 70B, making it 27.5% larger.

DeepSeek
DeepSeek R1 Distill Llama 70B
70.6Bparameters
Meta
Llama 3.2 90B Instruct
90.0Bparameters
70.6B
DeepSeek R1 Distill Llama 70B
90.0B
Llama 3.2 90B 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 Llama 70B
Input128,000 tokens
Output128,000 tokens
Meta
Llama 3.2 90B Instruct
Input128,000 tokens
Output128,000 tokens
Sun Apr 05 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Llama 3.2 90B Instruct supports multimodal inputs, whereas DeepSeek R1 Distill Llama 70B does not.

Llama 3.2 90B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek R1 Distill Llama 70B

Text
Images
Audio
Video

Llama 3.2 90B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Llama 70B is licensed under MIT, while Llama 3.2 90B Instruct uses Llama 3.2.

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

DeepSeek R1 Distill Llama 70B

MIT

Open weights

Llama 3.2 90B Instruct

Llama 3.2

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Llama 70B was released on 2025-01-20, while Llama 3.2 90B Instruct was released on 2024-09-25.

DeepSeek R1 Distill Llama 70B is 4 months newer than Llama 3.2 90B Instruct.

DeepSeek R1 Distill Llama 70B

Jan 20, 2025

1.2 years ago

3mo newer
Llama 3.2 90B Instruct

Sep 25, 2024

1.5 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 Llama 70B is available from DeepInfra. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.

DeepSeek R1 Distill Llama 70B

deepinfra logo
Deepinfra
Input Price:Input: $0.10/1MOutput Price:Output: $0.40/1M

Llama 3.2 90B Instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.35/1MOutput Price:Output: $0.40/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.72/1MOutput Price:Output: $0.72/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
together logo
Together
Input Price:Input: $1.20/1MOutput Price:Output: $1.20/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $2.00/1MOutput Price:Output: $2.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Less expensive input tokens
Higher GPQA score (65.2% vs 46.7%)
Meta
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Llama 70B
Meta
Llama 3.2 90B Instruct

FAQ

Common questions about DeepSeek R1 Distill Llama 70B vs Llama 3.2 90B Instruct

DeepSeek R1 Distill Llama 70B significantly outperforms across most benchmarks. DeepSeek R1 Distill Llama 70B is made by DeepSeek and Llama 3.2 90B Instruct is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek R1 Distill Llama 70B scores MATH-500: 94.5%, AIME 2024: 86.7%, GPQA: 65.2%, LiveCodeBench: 57.5%. Llama 3.2 90B Instruct scores AI2D: 92.3%, DocVQA: 90.1%, MGSM: 86.9%, MMLU: 86.0%, ChartQA: 85.5%.
DeepSeek R1 Distill Llama 70B is 3.5x cheaper for input tokens. DeepSeek R1 Distill Llama 70B costs $0.10/M input and $0.40/M output via deepinfra. Llama 3.2 90B Instruct costs $0.35/M input and $0.40/M output via deepinfra.
DeepSeek R1 Distill Llama 70B supports 128K tokens and Llama 3.2 90B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include input pricing ($0.10 vs $0.35/M), multimodal support (no vs yes), licensing (MIT vs Llama 3.2). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Distill Llama 70B is developed by DeepSeek and Llama 3.2 90B Instruct is developed by Meta.