Model Comparison

DeepSeek-V2.5 vs Llama 3.1 70B Instruct

Both models are evenly matched across the benchmarks. DeepSeek-V2.5 is 1.1x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V2.5 outperforms in 1 benchmarks (HumanEval), while Llama 3.1 70B Instruct is better at 1 benchmark (MMLU).

Both models are evenly matched across the benchmarks.

Sun Apr 19 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 3.1 70B Instruct ($0.20/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.4x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

In conclusion, Llama 3.1 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
Sun Apr 19 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Meta
Llama 3.1 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

166.0B diff

DeepSeek-V2.5 has 166.0B more parameters than Llama 3.1 70B Instruct, making it 237.1% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Meta
Llama 3.1 70B Instruct
70.0Bparameters
236.0B
DeepSeek-V2.5
70.0B
Llama 3.1 70B Instruct

Context Window

Maximum input and output token capacity

Llama 3.1 70B Instruct accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.1 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 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Sun Apr 19 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Llama 3.1 70B 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 70B 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 70B Instruct was released on 2024-07-23.

Llama 3.1 70B Instruct is 3 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

1.9 years ago

Llama 3.1 70B 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 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Cerebras, Together, Fireworks, Bedrock, Sambanova.

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 70B Instruct

lambda logo
Lambda
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.35/1MOutput 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: $0.78/1M
cerebras logo
Cerebras
Input Price:Input: $0.60/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
sambanova logo
Sambanova
Input Price:Input: $5.00/1MOutput Price:Output: $10.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Less expensive input tokens
Higher HumanEval score (89.0% vs 80.5%)
Meta
Larger context window (128,000 tokens)
Less expensive output tokens
Higher MMLU score (83.6% vs 80.4%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Meta
Llama 3.1 70B Instruct

FAQ

Common questions about DeepSeek-V2.5 vs Llama 3.1 70B Instruct

Both models are evenly matched across the benchmarks. DeepSeek-V2.5 is made by DeepSeek and Llama 3.1 70B 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 70B Instruct scores GSM-8K (CoT): 95.1%, ARC-C: 94.8%, API-Bank: 90.0%, IFEval: 87.5%, Multilingual MGSM (CoT): 86.9%.
DeepSeek-V2.5 is 1.4x cheaper for input tokens. DeepSeek-V2.5 costs $0.14/M input and $0.28/M output via deepseek. Llama 3.1 70B Instruct costs $0.20/M input and $0.20/M output via lambda.
DeepSeek-V2.5 supports 8K tokens and Llama 3.1 70B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (8K vs 128K), input pricing ($0.14 vs $0.20/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 70B Instruct is developed by Meta.