Model Comparison

DeepSeek-V3.2-Speciale vs Llama 3.2 3B Instruct

Comparing DeepSeek-V3.2-Speciale and Llama 3.2 3B Instruct across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2-Speciale and Llama 3.2 3B Instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.2 3B Instruct costs less

For input processing, DeepSeek-V3.2-Speciale ($0.28/1M tokens) is 28.0x more expensive than Llama 3.2 3B Instruct ($0.01/1M tokens).

For output processing, DeepSeek-V3.2-Speciale ($0.42/1M tokens) is 21.0x more expensive than Llama 3.2 3B Instruct ($0.02/1M tokens).

In conclusion, DeepSeek-V3.2-Speciale 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
Thu Apr 16 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2-Speciale
Input tokens$0.28
Output tokens$0.42
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

681.8B diff

DeepSeek-V3.2-Speciale has 681.8B more parameters than Llama 3.2 3B Instruct, making it 21239.6% larger.

DeepSeek
DeepSeek-V3.2-Speciale
685.0Bparameters
Meta
Llama 3.2 3B Instruct
3.2Bparameters
685.0B
DeepSeek-V3.2-Speciale
3.2B
Llama 3.2 3B Instruct

Context Window

Maximum input and output token capacity

DeepSeek-V3.2-Speciale accepts 131,072 input tokens compared to Llama 3.2 3B Instruct's 128,000 tokens. DeepSeek-V3.2-Speciale can generate longer responses up to 131,072 tokens, while Llama 3.2 3B Instruct is limited to 128,000 tokens.

DeepSeek
DeepSeek-V3.2-Speciale
Input131,072 tokens
Output131,072 tokens
Meta
Llama 3.2 3B Instruct
Input128,000 tokens
Output128,000 tokens
Thu Apr 16 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2-Speciale is licensed under MIT, 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-V3.2-Speciale

MIT

Open weights

Llama 3.2 3B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Speciale was released on 2025-12-01, while Llama 3.2 3B Instruct was released on 2024-09-25.

DeepSeek-V3.2-Speciale is 14 months newer than Llama 3.2 3B Instruct.

DeepSeek-V3.2-Speciale

Dec 1, 2025

4 months ago

1.2yr newer
Llama 3.2 3B Instruct

Sep 25, 2024

1.6 years ago

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-V3.2-Speciale is available from DeepSeek. Llama 3.2 3B Instruct is available from DeepInfra.

DeepSeek-V3.2-Speciale

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

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

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Meta
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2-Speciale
Meta
Llama 3.2 3B Instruct

FAQ

Common questions about DeepSeek-V3.2-Speciale vs Llama 3.2 3B Instruct

DeepSeek-V3.2-Speciale (DeepSeek) and Llama 3.2 3B Instruct (Meta) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek-V3.2-Speciale scores HMMT 2025: 99.2%, AIME 2025: 96.0%, CodeForces: 90.0%, t2-bench: 80.3%, SWE-Bench Verified: 73.1%. 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 28.0x cheaper for input tokens. DeepSeek-V3.2-Speciale costs $0.28/M input and $0.42/M output via deepseek. Llama 3.2 3B Instruct costs $0.01/M input and $0.02/M output via deepinfra.
DeepSeek-V3.2-Speciale supports 131K 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 (131K vs 128K), input pricing ($0.28 vs $0.01/M), licensing (MIT vs Llama 3.2 Community License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.2-Speciale is developed by DeepSeek and Llama 3.2 3B Instruct is developed by Meta.