Model Comparison

DeepSeek-V2.5 vs Llama 3.1 8B Instruct

DeepSeek-V2.5 significantly outperforms across most benchmarks. Llama 3.1 8B Instruct is 5.8x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V2.5 outperforms in 2 benchmarks (HumanEval, MMLU), while Llama 3.1 8B Instruct is better at 0 benchmarks.

DeepSeek-V2.5 significantly outperforms across most benchmarks.

Wed Apr 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.1 8B Instruct costs less

For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 4.7x more expensive than Llama 3.1 8B Instruct ($0.03/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 9.3x more expensive than Llama 3.1 8B Instruct ($0.03/1M tokens).

In conclusion, DeepSeek-V2.5 is more expensive than Llama 3.1 8B Instruct.*

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

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Meta
Llama 3.1 8B Instruct
Input tokens$0.03
Output tokens$0.03
Best providerLambda
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

228.0B diff

DeepSeek-V2.5 has 228.0B more parameters than Llama 3.1 8B Instruct, making it 2850.0% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Meta
Llama 3.1 8B Instruct
8.0Bparameters
236.0B
DeepSeek-V2.5
8.0B
Llama 3.1 8B Instruct

Context Window

Maximum input and output token capacity

Llama 3.1 8B Instruct accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.1 8B Instruct can generate longer responses up to 131,072 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 8B Instruct
Input131,072 tokens
Output131,072 tokens
Wed Apr 15 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Llama 3.1 8B 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 8B 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 8B Instruct was released on 2024-07-23.

Llama 3.1 8B Instruct is 3 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

1.9 years ago

Llama 3.1 8B Instruct

Jul 23, 2024

1.7 years ago

2mo newer

Knowledge Cutoff

When training data ends

Llama 3.1 8B Instruct has a documented knowledge cutoff of 2023-12-31, while DeepSeek-V2.5's cutoff date is not specified.

We can confirm Llama 3.1 8B Instruct's training data extends to 2023-12-31, but cannot make a direct comparison without DeepSeek-V2.5's cutoff date.

DeepSeek-V2.5

—

Llama 3.1 8B Instruct

Dec 2023

Provider Availability

DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Llama 3.1 8B Instruct is available from Lambda, DeepInfra, Groq, Sambanova, Cerebras, Hyperbolic, Together, Fireworks, Bedrock.

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 8B Instruct

lambda logo
Lambda
Input Price:Input: $0.03/1MOutput Price:Output: $0.03/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.05/1MOutput Price:Output: $0.05/1M
groq logo
Groq
Input Price:Input: $0.05/1MOutput Price:Output: $0.08/1M
sambanova logo
Sambanova
Input Price:Input: $0.10/1MOutput Price:Output: $0.20/1M
cerebras logo
Cerebras
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
together logo
Together
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
fireworks logo
Fireworks
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.22/1MOutput Price:Output: $0.22/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Higher HumanEval score (89.0% vs 72.6%)
Higher MMLU score (80.4% vs 69.4%)
Meta
Larger context window (131,072 tokens)
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Meta
Llama 3.1 8B Instruct

FAQ

Common questions about DeepSeek-V2.5 vs Llama 3.1 8B Instruct

DeepSeek-V2.5 significantly outperforms across most benchmarks. DeepSeek-V2.5 is made by DeepSeek and Llama 3.1 8B Instruct is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. Llama 3.1 8B Instruct scores GSM-8K (CoT): 84.5%, ARC-C: 83.4%, API-Bank: 82.6%, IFEval: 80.4%, BFCL: 76.1%.
Llama 3.1 8B Instruct is 4.7x cheaper for input tokens. DeepSeek-V2.5 costs $0.14/M input and $0.28/M output via deepseek. Llama 3.1 8B Instruct costs $0.03/M input and $0.03/M output via lambda.
DeepSeek-V2.5 supports 8K tokens and Llama 3.1 8B Instruct supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (8K vs 131K), input pricing ($0.14 vs $0.03/M), licensing (deepseek vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V2.5 is developed by DeepSeek and Llama 3.1 8B Instruct is developed by Meta.