DeepSeek-V2.5 vs Llama 3.3 70B Instruct Comparison

Comparing DeepSeek-V2.5 and Llama 3.3 70B Instruct across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V2.5 outperforms in 1 benchmarks (HumanEval), while Llama 3.3 70B Instruct is better at 2 benchmarks (MATH, MMLU).

Llama 3.3 70B Instruct shows notably better performance in the majority of benchmarks.

Tue Mar 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V2.5 costs less

For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 1.4x cheaper than Llama 3.3 70B Instruct ($0.20/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.4x more expensive than Llama 3.3 70B Instruct ($0.20/1M tokens).

In conclusion, Llama 3.3 70B Instruct is more expensive than DeepSeek-V2.5.*

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

Lowest available price from all providers
Tue Mar 17 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Meta
Llama 3.3 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
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Model Size

Parameter count comparison

166.0B diff

DeepSeek-V2.5 has 166.0B more parameters than Llama 3.3 70B Instruct, making it 237.1% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Meta
Llama 3.3 70B Instruct
70.0Bparameters
236.0B
DeepSeek-V2.5
70.0B
Llama 3.3 70B Instruct

Context Window

Maximum input and output token capacity

Llama 3.3 70B Instruct accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.3 70B Instruct can generate longer responses up to 128,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.

DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Meta
Llama 3.3 70B Instruct
Input128,000 tokens
Output128,000 tokens
Tue Mar 17 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, 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-V2.5

deepseek

Open weights

Llama 3.3 70B Instruct

Llama 3.3 Community License Agreement

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Llama 3.3 70B Instruct was released on 2024-12-06.

Llama 3.3 70B Instruct is 7 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

1.9 years ago

Llama 3.3 70B Instruct

Dec 6, 2024

1.3 years ago

7mo newer

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-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. 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-V2.5

deepseek logo
DeepSeek
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.70/1MOutput Price:Output: $1.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $2.00/1MOutput Price:Output: $2.00/1M

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

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Key Takeaways

Less expensive input tokens
Higher HumanEval score (89.0% vs 88.4%)
Larger context window (128,000 tokens)
Less expensive output tokens
Higher MATH score (77.0% vs 74.7%)
Higher MMLU score (86.0% vs 80.4%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Meta
Llama 3.3 70B Instruct