Model Comparison

DeepSeek-V2.5 vs Llama 3.2 3B Instruct

DeepSeek-V2.5 significantly outperforms across most benchmarks. Llama 3.2 3B Instruct is 14.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V2.5 outperforms in 3 benchmarks (GSM8k, MATH, MMLU), while Llama 3.2 3B Instruct is better at 0 benchmarks.

DeepSeek-V2.5 significantly outperforms across most benchmarks.

Mon Apr 20 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.2 3B Instruct costs less

For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 14.0x more expensive than Llama 3.2 3B Instruct ($0.01/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 14.0x more expensive than Llama 3.2 3B Instruct ($0.02/1M tokens).

In conclusion, DeepSeek-V2.5 is more expensive than Llama 3.2 3B Instruct.*

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

Lowest available price from all providers
Mon Apr 20 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Meta
Llama 3.2 3B Instruct
Input tokens$0.01
Output tokens$0.02
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

232.8B diff

DeepSeek-V2.5 has 232.8B more parameters than Llama 3.2 3B Instruct, making it 7252.0% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
Meta
Llama 3.2 3B Instruct
3.2Bparameters
236.0B
DeepSeek-V2.5
3.2B
Llama 3.2 3B Instruct

Context Window

Maximum input and output token capacity

Llama 3.2 3B Instruct accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.2 3B 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 3.2 3B Instruct
Input128,000 tokens
Output128,000 tokens
Mon Apr 20 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while Llama 3.2 3B Instruct uses Llama 3.2 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 3.2 3B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while Llama 3.2 3B Instruct was released on 2024-09-25.

Llama 3.2 3B Instruct is 5 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.0 years ago

Llama 3.2 3B Instruct

Sep 25, 2024

1.6 years ago

4mo 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 3.2 3B Instruct is available from DeepInfra.

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 3.2 3B Instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.01/1MOutput Price:Output: $0.02/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Higher GSM8k score (95.1% vs 77.7%)
Higher MATH score (74.7% vs 48.0%)
Higher MMLU score (80.4% vs 63.4%)
Meta
Larger context window (128,000 tokens)
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
Meta
Llama 3.2 3B Instruct

FAQ

Common questions about DeepSeek-V2.5 vs Llama 3.2 3B Instruct

DeepSeek-V2.5 significantly outperforms across most benchmarks. DeepSeek-V2.5 is made by DeepSeek and Llama 3.2 3B 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 3.2 3B Instruct scores NIH/Multi-needle: 84.7%, ARC-C: 78.6%, GSM8k: 77.7%, IFEval: 77.4%, HellaSwag: 69.8%.
Llama 3.2 3B Instruct is 14.0x cheaper for input tokens. DeepSeek-V2.5 costs $0.14/M input and $0.28/M output via deepseek. Llama 3.2 3B Instruct costs $0.01/M input and $0.02/M output via deepinfra.
DeepSeek-V2.5 supports 8K tokens and Llama 3.2 3B 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 $0.01/M), licensing (deepseek vs Llama 3.2 Community License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V2.5 is developed by DeepSeek and Llama 3.2 3B Instruct is developed by Meta.