Model Comparison

DeepSeek-V2.5 vs DeepSeek-V3

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V2.5 is 2.7x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V2.5 outperforms in 0 benchmarks, while DeepSeek-V3 is better at 3 benchmarks (HumanEval-Mul, MMLU, SWE-Bench Verified).

DeepSeek-V3 significantly outperforms across most benchmarks.

Sat Apr 04 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 1.9x cheaper than DeepSeek-V3 ($0.27/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 3.9x cheaper than DeepSeek-V3 ($1.10/1M tokens).

In conclusion, DeepSeek-V3 is more expensive than DeepSeek-V2.5.*

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

Lowest available price from all providers
Sat Apr 04 2026 • llm-stats.com
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

435.0B diff

DeepSeek-V3 has 435.0B more parameters than DeepSeek-V2.5, making it 184.3% larger.

DeepSeek
DeepSeek-V2.5
236.0Bparameters
DeepSeek
DeepSeek-V3
671.0Bparameters
236.0B
DeepSeek-V2.5
671.0B
DeepSeek-V3

Context Window

Maximum input and output token capacity

DeepSeek-V3 accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. DeepSeek-V3 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
DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Sat Apr 04 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while DeepSeek-V3 uses MIT + Model License (Commercial use allowed).

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

DeepSeek-V2.5

deepseek

Open weights

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while DeepSeek-V3 was released on 2024-12-25.

DeepSeek-V3 is 8 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

1.9 years ago

DeepSeek-V3

Dec 25, 2024

1.3 years ago

7mo 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. DeepSeek-V3 is available from DeepSeek.

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

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Less expensive input tokens
Less expensive output tokens
DeepSeek
Larger context window (131,072 tokens)
Higher HumanEval-Mul score (82.6% vs 73.8%)
Higher MMLU score (88.5% vs 80.4%)
Higher SWE-Bench Verified score (42.0% vs 16.8%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V2.5
DeepSeek
DeepSeek-V3

FAQ

Common questions about DeepSeek-V2.5 vs DeepSeek-V3

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V2.5 is made by DeepSeek and DeepSeek-V3 is made by DeepSeek. 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%. DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%.
DeepSeek-V2.5 is 1.9x cheaper for input tokens. DeepSeek-V2.5 costs $0.14/M input and $0.28/M output via deepseek. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek.
DeepSeek-V2.5 supports 8K tokens and DeepSeek-V3 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 $0.27/M), licensing (deepseek vs MIT + Model License (Commercial use allowed)). See the full comparison above for benchmark-by-benchmark results.