Model Comparison

DeepSeek-V2.5 vs DeepSeek-V3.1

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V2.5 is 2.6x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek-V2.5 outperforms in 0 benchmarks, while DeepSeek-V3.1 is better at 1 benchmark (SWE-Bench Verified).

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Fri May 15 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-V3.1 ($0.27/1M tokens).

For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 3.6x cheaper than DeepSeek-V3.1 ($1.00/1M tokens).

In conclusion, DeepSeek-V3.1 is more expensive than DeepSeek-V2.5.*

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

Lowest available price from all providers

Fri May 15 2026 • llm-stats.com

DeepSeek-V2.5

Input tokens$0.14

Output tokens$0.28

Best providerDeepSeek

DeepSeek-V3.1

Input tokens$0.27

Output tokens$1.00

Best providerDeepinfra

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Model Size

Parameter count comparison

435.0B diff

DeepSeek-V3.1 has 435.0B more parameters than DeepSeek-V2.5, making it 184.3% larger.

DeepSeek-V2.5

236.0Bparameters

DeepSeek-V3.1

671.0Bparameters

236.0B

DeepSeek-V2.5

671.0B

DeepSeek-V3.1

Context Window

Maximum input and output token capacity

DeepSeek-V3.1 accepts 163,840 input tokens compared to DeepSeek-V2.5's 8,192 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.

DeepSeek-V2.5

Input8,192 tokens

Output8,192 tokens

DeepSeek-V3.1

Input163,840 tokens

Output163,840 tokens

Fri May 15 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V2.5 is licensed under deepseek, while DeepSeek-V3.1 uses MIT.

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

DeepSeek-V2.5

deepseek

Open weights

DeepSeek-V3.1

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V2.5 was released on 2024-05-08, while DeepSeek-V3.1 was released on 2025-01-10.

DeepSeek-V3.1 is 8 months newer than DeepSeek-V2.5.

DeepSeek-V2.5

May 8, 2024

2.0 years ago

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

8mo 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-V3.1 is available from DeepInfra, Novita.

DeepSeek-V2.5

DeepSeek

Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M

Deepinfra

Input Price:Input: $0.70/1MOutput Price:Output: $1.40/1M

Hyperbolic

Input Price:Input: $2.00/1MOutput Price:Output: $2.00/1M

DeepSeek-V3.1

Deepinfra

Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

Novita

Input Price:Input: $0.27/1MOutput Price:Output: $1.00/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek-V2.5

View details

DeepSeek

Less expensive input tokens

Less expensive output tokens

DeepSeek-V3.1

View details

DeepSeek

Larger context window (163,840 tokens)

Higher SWE-Bench Verified score (66.0% vs 16.8%)

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek-V2.5	DeepSeek-V3.1

FAQ

Common questions about DeepSeek-V2.5 vs DeepSeek-V3.1.

Which is better, DeepSeek-V2.5 or DeepSeek-V3.1?

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V2.5 is made by DeepSeek and DeepSeek-V3.1 is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V2.5 compare to DeepSeek-V3.1 in benchmarks?

DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%. DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%.

Is DeepSeek-V2.5 cheaper than DeepSeek-V3.1?

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-V3.1 costs $0.27/M input and $1.00/M output via deepinfra.

What are the context window sizes for DeepSeek-V2.5 and DeepSeek-V3.1?

DeepSeek-V2.5 supports 8K tokens and DeepSeek-V3.1 supports 164K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V2.5 and DeepSeek-V3.1?

Key differences include context window (8K vs 164K), input pricing ($0.14 vs $0.27/M), licensing (deepseek vs MIT). See the full comparison above for benchmark-by-benchmark results.