DeepSeek-V2.5 vs Llama 3.1 405B Instruct Comparison

Comparing DeepSeek-V2.5 and Llama 3.1 405B Instruct across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V2.5 outperforms in 1 benchmarks (MATH), while Llama 3.1 405B Instruct is better at 2 benchmarks (GSM8k, MMLU).

Llama 3.1 405B Instruct has a slight edge in benchmark performance.

Wed Mar 18 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 6.4x cheaper than Llama 3.1 405B Instruct ($0.89/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 3.2x cheaper than Llama 3.1 405B Instruct ($0.89/1M tokens).

In conclusion, Llama 3.1 405B Instruct is more expensive than DeepSeek-V2.5.*

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

Lowest available price from all providers
Wed Mar 18 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Meta
Llama 3.1 405B Instruct
Input tokens$0.89
Output tokens$0.89
Best providerLambda
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Model Size

Parameter count comparison

169.0B diff

Llama 3.1 405B Instruct has 169.0B more parameters than DeepSeek-V2.5, making it 71.6% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Meta
Llama 3.1 405B Instruct
405.0Bparameters
236.0B
DeepSeek-V2.5
405.0B
Llama 3.1 405B Instruct

Context Window

Maximum input and output token capacity

Llama 3.1 405B Instruct accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.1 405B 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 3.1 405B Instruct
Input128,000 tokens
Output128,000 tokens
Wed Mar 18 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Llama 3.1 405B Instruct uses Llama 3.1 Community License.

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

DeepSeek-V2.5

deepseek

Open weights

Llama 3.1 405B Instruct

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Llama 3.1 405B Instruct was released on 2024-07-23.

Llama 3.1 405B Instruct is 3 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

1.9 years ago

Llama 3.1 405B Instruct

Jul 23, 2024

1.7 years ago

2mo 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 3.1 405B Instruct is available from Lambda, DeepInfra, Fireworks, Bedrock, Together, Hyperbolic, Google, Replicate. 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 3.1 405B Instruct

lambda logo
Lambda
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
deepinfra logo
Deepinfra
Input Price:Input: $1.79/1MOutput Price:Output: $1.79/1M
fireworks logo
Fireworks
Input Price:Input: $3.00/1MOutput Price:Output: $3.00/1M
bedrock logo
AWS Bedrock
Input Price:Input: $3.00/1MOutput Price:Output: $3.00/1M
together logo
Together
Input Price:Input: $3.50/1MOutput Price:Output: $3.50/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $4.00/1MOutput Price:Output: $4.00/1M
google logo
Google
Input Price:Input: $5.00/1MOutput Price:Output: $16.00/1M
replicate logo
Replicate
Input Price:Input: $9.50/1MOutput Price:Output: $9.50/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Less expensive input tokens
Less expensive output tokens
Higher MATH score (74.7% vs 73.8%)
Larger context window (128,000 tokens)
Higher GSM8k score (96.8% vs 95.1%)
Higher MMLU score (87.3% vs 80.4%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Meta
Llama 3.1 405B Instruct