Model Comparison

DeepSeek-V3.1 vs Qwen2.5-Coder 32B Instruct

DeepSeek-V3.1 significantly outperforms across most benchmarks. Qwen2.5-Coder 32B Instruct is 5.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3.1 outperforms in 3 benchmarks (LiveCodeBench, MMLU-Pro, MMLU-Redux), while Qwen2.5-Coder 32B Instruct is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Tue Apr 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Qwen2.5-Coder 32B Instruct costs less

For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 3.0x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).

For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 11.1x more expensive than Qwen2.5-Coder 32B Instruct ($0.09/1M tokens).

In conclusion, DeepSeek-V3.1 is more expensive than Qwen2.5-Coder 32B Instruct.*

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

Lowest available price from all providers

Tue Apr 14 2026 • llm-stats.com

DeepSeek-V3.1

Input tokens$0.27

Output tokens$1.00

Best providerDeepinfra

Qwen2.5-Coder 32B Instruct

Input tokens$0.09

Output tokens$0.09

Best providerLambda

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Model Size

Parameter count comparison

639.0B diff

DeepSeek-V3.1 has 639.0B more parameters than Qwen2.5-Coder 32B Instruct, making it 1996.9% larger.

DeepSeek-V3.1

671.0Bparameters

Qwen2.5-Coder 32B Instruct

32.0Bparameters

671.0B

DeepSeek-V3.1

32.0B

Qwen2.5-Coder 32B Instruct

Context Window

Maximum input and output token capacity

DeepSeek-V3.1 accepts 163,840 input tokens compared to Qwen2.5-Coder 32B Instruct's 128,000 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Qwen2.5-Coder 32B Instruct is limited to 128,000 tokens.

DeepSeek-V3.1

Input163,840 tokens

Output163,840 tokens

Qwen2.5-Coder 32B Instruct

Input128,000 tokens

Output128,000 tokens

Tue Apr 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while Qwen2.5-Coder 32B 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-Coder 32B 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-Coder 32B Instruct was released on 2024-09-19.

DeepSeek-V3.1 is 4 months newer than Qwen2.5-Coder 32B Instruct.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

3mo newer

Qwen2.5-Coder 32B 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-Coder 32B Instruct is available from Lambda, DeepInfra, Hyperbolic, Fireworks.

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-Coder 32B Instruct

Lambda

Input Price:Input: $0.09/1MOutput Price:Output: $0.09/1M

Deepinfra

Input Price:Input: $0.18/1MOutput Price:Output: $0.18/1M

Hyperbolic

Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M

Fireworks

Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M

* 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)

Higher LiveCodeBench score (56.4% vs 31.4%)

Higher MMLU-Pro score (83.7% vs 50.4%)

Higher MMLU-Redux score (91.8% vs 77.5%)

Qwen2.5-Coder 32B Instruct

View details

Alibaba Cloud / Qwen Team

Less expensive input tokens

Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek-V3.1	Qwen2.5-Coder 32B Instruct

FAQ

Common questions about DeepSeek-V3.1 vs Qwen2.5-Coder 32B Instruct

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Qwen2.5-Coder 32B 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-Coder 32B Instruct scores HumanEval: 92.7%, GSM8k: 91.1%, MBPP: 90.2%, HellaSwag: 83.0%, Winogrande: 80.8%.

Qwen2.5-Coder 32B Instruct is 3.0x cheaper for input tokens. DeepSeek-V3.1 costs $0.27/M input and $1.00/M output via deepinfra. Qwen2.5-Coder 32B Instruct costs $0.09/M input and $0.09/M output via lambda.

DeepSeek-V3.1 supports 164K tokens and Qwen2.5-Coder 32B 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 (164K vs 128K), input pricing ($0.27 vs $0.09/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-Coder 32B Instruct is developed by Alibaba Cloud / Qwen Team.

DeepSeek-V3.1 vs Qwen2.5-Coder 32B Instruct

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

License

Release Timeline

Knowledge Cutoff

Provider Availability

DeepSeek-V3.1

Qwen2.5-Coder 32B Instruct

Outputs Comparison

Key Takeaways

DeepSeek-V3.1

Qwen2.5-Coder 32B Instruct

Detailed Comparison

FAQ

Which is better, DeepSeek-V3.1 or Qwen2.5-Coder 32B Instruct?

How does DeepSeek-V3.1 compare to Qwen2.5-Coder 32B Instruct in benchmarks?

Is DeepSeek-V3.1 cheaper than Qwen2.5-Coder 32B Instruct?

What are the context window sizes for DeepSeek-V3.1 and Qwen2.5-Coder 32B Instruct?

What are the main differences between DeepSeek-V3.1 and Qwen2.5-Coder 32B Instruct?

Who makes DeepSeek-V3.1 and Qwen2.5-Coder 32B Instruct?