Qwen3-235B vs DeepSeek-R1: Open Source LLM Deep Research Showdown 2026

The open-source AI revolution has reached a tipping point. Two Chinese models—Alibaba’s Qwen3-235B and DeepSeek’s R1—are now competing head-to-head with GPT-4o and Claude 3.5 Sonnet. For the first time, open-weight models are not just catching up; they’re leading in specific benchmarks.

This comprehensive comparison breaks down everything you need to know about these two titans of open-source AI.

TL;DR

• Qwen3-235B excels at multilingual tasks and general knowledge with 235B parameters
• DeepSeek-R1 dominates in reasoning and coding with its unique chain-of-thought approach
• Both models are available for local deployment with various quantization options
• DeepSeek-R1 offers better value for coding tasks; Qwen3 is superior for diverse language support
• For most developers, DeepSeek-R1’s reasoning capabilities make it the better choice

The Rise of Chinese Open-Source LLMs

2025 marked a turning point in AI development. While OpenAI and Anthropic continued their closed-source approach, Chinese companies bet big on open weights. The result? Models that anyone can download, modify, and deploy without API costs or usage restrictions.

Aspect	Qwen3-235B	DeepSeek-R1
Developer	Alibaba Cloud	DeepSeek AI
Parameters	235 billion	671 billion
Architecture	Dense Transformer	Mixture of Experts
License	Apache 2.0	MIT
Release Date	January 2026	January 2025
Training Cost	~$15M estimated	~$5.6M reported

Architecture Deep Dive

Qwen3-235B: Dense Powerhouse

Qwen3 uses a traditional dense transformer architecture, meaning all 235 billion parameters are active during inference. This approach offers:

• Consistent performance across all tasks
• Simpler deployment with predictable resource usage
• Better fine-tuning characteristics

The model features a 128K context window and supports over 100 languages natively.

DeepSeek-R1: Efficient Giant

DeepSeek-R1 employs a Mixture of Experts (MoE) architecture with 671B total parameters but only 37B active per token. This means:

• Lower inference costs despite larger total size
• Specialized expert routing for different task types
• Unique reasoning chains visible in output

The “thinking” process in DeepSeek-R1 is particularly notable—you can see the model’s step-by-step reasoning before it provides an answer.

Benchmark Comparison

Let’s look at how these models perform across standard benchmarks:

Reasoning Benchmarks

Benchmark	Qwen3-235B	DeepSeek-R1	GPT-4o	Claude 3.5
MMLU	88.2%	90.8%	88.7%	88.3%
GPQA Diamond	62.1%	71.5%	53.6%	65.0%
MATH-500	85.3%	97.3%	76.6%	78.3%
ARC-Challenge	96.1%	94.8%	96.4%	96.7%

DeepSeek-R1 dominates mathematical reasoning, achieving near-perfect scores on MATH-500. This is largely due to its explicit chain-of-thought approach.

Coding Benchmarks

Benchmark	Qwen3-235B	DeepSeek-R1	GPT-4o	Claude 3.5
HumanEval	82.4%	96.3%	90.2%	92.0%
MBPP	78.6%	84.2%	86.8%	90.5%
LiveCodeBench	45.2%	65.9%	52.3%	55.8%
Codeforces Rating	1698	2029	1891	1886

For coding tasks, DeepSeek-R1 is the clear winner. Its Codeforces rating of 2029 places it in the “Candidate Master” category—better than most human competitive programmers.

For a detailed coding comparison with ChatGPT, check our DeepSeek vs ChatGPT coding benchmark.

Multilingual Performance

Language	Qwen3-235B	DeepSeek-R1
English	95.2%	94.8%
Chinese	96.8%	95.1%
Japanese	89.3%	82.1%
Korean	87.6%	79.4%
Arabic	84.2%	71.3%
Spanish	91.5%	88.2%

Qwen3 significantly outperforms DeepSeek-R1 in non-English languages, making it the better choice for multilingual applications.

Hardware Requirements

Running Qwen3-235B Locally

The full Qwen3-235B requires substantial hardware:

Quantization	VRAM Required	Quality
FP16	470GB	Full
Q8	235GB	Excellent
Q4	120GB	Good
Q2	60GB	Acceptable

For most users, the distilled versions are more practical:

• Qwen3-72B: 48GB VRAM (Q4)
• Qwen3-32B: 24GB VRAM (Q4)
• Qwen3-14B: 12GB VRAM (Q4)

Running DeepSeek-R1 Locally

Thanks to MoE architecture, DeepSeek-R1 is more efficient:

Version	Active Params	VRAM Required
DeepSeek-R1 (Full)	37B	400GB+
DeepSeek-R1-Distill-70B	70B	48GB
DeepSeek-R1-Distill-32B	32B	24GB
DeepSeek-R1-Distill-14B	14B	12GB
DeepSeek-R1-Distill-7B	7B	6GB

For a complete guide on running DeepSeek locally, see our DeepSeek-R1 local deployment tutorial.

Real-World Performance Tests

We tested both models on practical tasks to see how benchmarks translate to real use.

Test 1: Complex Code Generation

Task: Generate a complete REST API with authentication, rate limiting, and database integration.

Qwen3-235B Result:

• Generated functional code in 45 seconds
• Required 2 iterations to fix minor bugs
• Good documentation but missed edge cases

DeepSeek-R1 Result:

• Generated functional code in 38 seconds
• Code worked on first attempt
• Included comprehensive error handling
• Showed reasoning process for architectural decisions

Winner: DeepSeek-R1

Test 2: Research Paper Analysis

Task: Summarize and critique a 30-page machine learning paper.

Qwen3-235B Result:

• Comprehensive summary with key findings
• Identified methodology strengths and weaknesses
• Better at extracting nuanced arguments

DeepSeek-R1 Result:

• Accurate summary but more surface-level
• Strong on mathematical analysis
• Missed some contextual implications

Winner: Qwen3-235B

Test 3: Multilingual Translation

Task: Translate a technical document from English to Japanese, Korean, and Arabic.

Qwen3-235B Result:

• Natural, fluent translations in all languages
• Preserved technical terminology accurately
• Maintained document structure

DeepSeek-R1 Result:

• Good English-to-Chinese translation
• Japanese and Korean translations had grammatical issues
• Arabic translation was notably weaker

Winner: Qwen3-235B (by a significant margin)

Cost Analysis

API Pricing (as of February 2026)

Provider	Input (per 1M tokens)	Output (per 1M tokens)
Qwen3-235B (Alibaba)	$0.50	$1.50
DeepSeek-R1 (API)	$0.14	$2.19
GPT-4o	$2.50	$10.00
Claude 3.5 Sonnet	$3.00	$15.00

Both Chinese models offer dramatically lower API costs than Western alternatives. DeepSeek-R1 has the cheapest input pricing, while Qwen3 offers more balanced input/output costs.

Self-Hosting Costs

For organizations running these models on their own infrastructure:

Setup	Monthly Cost	Best For
Cloud GPU (A100 80GB)	$2,000-3,000	Medium usage
Cloud GPU (H100 cluster)	$15,000+	High throughput
On-premise (RTX 4090 x4)	$200 (electricity)	Privacy-focused

Use Case Recommendations

Choose Qwen3-235B If You Need:

1. Multilingual support - Superior performance in 100+ languages
2. General knowledge tasks - Broader training data coverage
3. Fine-tuning flexibility - Dense architecture is easier to adapt
4. Consistent behavior - No MoE routing variability

Choose DeepSeek-R1 If You Need:

1. Coding assistance - Best-in-class code generation
2. Mathematical reasoning - Near-perfect on math benchmarks
3. Transparent reasoning - Visible chain-of-thought process
4. Cost efficiency - Lower inference costs due to MoE

For Local Deployment

If you’re running models locally, DeepSeek-R1’s distilled versions offer the best performance-per-GB ratio. The 14B distilled version provides 80% of the full model’s capability at a fraction of the resource cost.

Integration Options

With Ollama

Both models are available through Ollama:

# DeepSeek-R1
ollama pull deepseek-r1:14b

# Qwen3
ollama pull qwen3:14b

With LM Studio

Download GGUF quantizations directly from the LM Studio interface. Both models are well-supported.

With vLLM

For production deployments:

from vllm import LLM

# DeepSeek-R1
llm = LLM(model="deepseek-ai/DeepSeek-R1-Distill-Qwen-14B")

# Qwen3
llm = LLM(model="Qwen/Qwen3-14B")

The Bigger Picture

The success of Qwen3 and DeepSeek-R1 signals a fundamental shift in AI development. Open-source models are no longer playing catch-up—they’re setting the pace in specific domains.

For developers and businesses, this means:

1. Reduced dependency on closed-source providers
2. Lower costs for AI integration
3. Greater control over model behavior and data privacy
4. Faster innovation through community contributions

The competition between these models also benefits everyone. As Alibaba and DeepSeek push each other forward, the entire open-source ecosystem improves.

Conclusion

Both Qwen3-235B and DeepSeek-R1 represent the pinnacle of open-source AI in 2026. Your choice depends on your specific needs:

• For coding and reasoning: DeepSeek-R1 is the clear winner
• For multilingual and general tasks: Qwen3-235B takes the lead
• For cost-conscious deployments: DeepSeek-R1’s MoE efficiency wins
• For fine-tuning projects: Qwen3’s dense architecture is more predictable

The best news? Both models are free to use, modify, and deploy. The era of open-source AI dominance has arrived.

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Want to run these models locally? Check our complete guide to running local LLMs in 2026. For understanding how AI models can work together, see our MCP Protocol complete guide.