Model Comparison

DeepSeek-V3.1 vs Qwen2.5 7B Instruct

DeepSeek-V3.1 significantly outperforms across most benchmarks. Qwen2.5 7B Instruct is 1.5x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V3.1 outperforms in 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, MMLU-Redux), while Qwen2.5 7B Instruct is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Thu Apr 30 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen2.5 7B Instruct costs less

For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 1.1x cheaper than Qwen2.5 7B Instruct ($0.30/1M tokens).

For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 3.3x more expensive than Qwen2.5 7B Instruct ($0.30/1M tokens).

In conclusion, DeepSeek-V3.1 is more expensive than Qwen2.5 7B Instruct.*

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

Lowest available price from all providers

Thu Apr 30 2026 • llm-stats.com

DeepSeek-V3.1

Input tokens$0.27

Output tokens$1.00

Best providerDeepinfra

Qwen2.5 7B Instruct

Input tokens$0.30

Output tokens$0.30

Best providerTogether

Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

663.4B diff

DeepSeek-V3.1 has 663.4B more parameters than Qwen2.5 7B Instruct, making it 8717.3% larger.

DeepSeek-V3.1

671.0Bparameters

Qwen2.5 7B Instruct

7.6Bparameters

671.0B

DeepSeek-V3.1

7.6B

Qwen2.5 7B Instruct

Context Window

Maximum input and output token capacity

DeepSeek-V3.1 accepts 163,840 input tokens compared to Qwen2.5 7B Instruct's 131,072 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Qwen2.5 7B Instruct is limited to 8,192 tokens.

DeepSeek-V3.1

Input163,840 tokens

Output163,840 tokens

Qwen2.5 7B Instruct

Input131,072 tokens

Output8,192 tokens

Thu Apr 30 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while Qwen2.5 7B Instruct uses Apache 2.0.

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

DeepSeek-V3.1

MIT

Open weights

Qwen2.5 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Qwen2.5 7B Instruct was released on 2024-09-19.

DeepSeek-V3.1 is 4 months newer than Qwen2.5 7B Instruct.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

3mo newer

Qwen2.5 7B Instruct

Sep 19, 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-V3.1 is available from DeepInfra, Novita. Qwen2.5 7B Instruct is available from Together.

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

Qwen2.5 7B Instruct

Together

Input Price:Input: $0.30/1MOutput Price:Output: $0.30/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek-V3.1

View details

DeepSeek

Larger context window (163,840 tokens)

Less expensive input tokens

Higher GPQA score (74.9% vs 36.4%)

Higher LiveCodeBench score (56.4% vs 28.7%)

Higher MMLU-Pro score (83.7% vs 56.3%)

Higher MMLU-Redux score (91.8% vs 75.4%)

Qwen2.5 7B Instruct

View details

Alibaba Cloud / Qwen Team

Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek-V3.1	Qwen2.5 7B Instruct

FAQ

Common questions about DeepSeek-V3.1 vs Qwen2.5 7B Instruct

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Qwen2.5 7B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Qwen2.5 7B Instruct scores GSM8k: 91.6%, MT-Bench: 87.5%, HumanEval: 84.8%, MBPP: 79.2%, MATH: 75.5%.

DeepSeek-V3.1 is 1.1x cheaper for input tokens. DeepSeek-V3.1 costs $0.27/M input and $1.00/M output via deepinfra. Qwen2.5 7B Instruct costs $0.30/M input and $0.30/M output via together.

DeepSeek-V3.1 supports 164K tokens and Qwen2.5 7B Instruct supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Key differences include context window (164K vs 131K), input pricing ($0.27 vs $0.30/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

DeepSeek-V3.1 is developed by DeepSeek and Qwen2.5 7B Instruct is developed by Alibaba Cloud / Qwen Team.

DeepSeek-V3.1 vs Qwen2.5 7B Instruct

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

License

Release Timeline

Knowledge Cutoff

Provider Availability

DeepSeek-V3.1

Qwen2.5 7B Instruct

Outputs Comparison

Key Takeaways

DeepSeek-V3.1

Qwen2.5 7B Instruct

Detailed Comparison

FAQ

Which is better, DeepSeek-V3.1 or Qwen2.5 7B Instruct?

How does DeepSeek-V3.1 compare to Qwen2.5 7B Instruct in benchmarks?

Is DeepSeek-V3.1 cheaper than Qwen2.5 7B Instruct?

What are the context window sizes for DeepSeek-V3.1 and Qwen2.5 7B Instruct?

What are the main differences between DeepSeek-V3.1 and Qwen2.5 7B Instruct?

Who makes DeepSeek-V3.1 and Qwen2.5 7B Instruct?