DeepSeek R1 vs Llama 4 vs Qwen 3.5: Best Free Open Source AI 2026
What This Guide Covers: We tested DeepSeek R1, Meta Llama 4, and Alibaba Qwen 3.5 head-to-head across reasoning, coding, multilingual performance, and inference speed. All three models are open-source, free to download, and run locally with Ollama. This comparison helps you pick the right model for your specific needs — whether you're a developer, researcher, student, or AI enthusiast looking for the best free alternative to ChatGPT.
Model Overviews: Who Made What
DeepSeek R1
DeepSeek AI · China- Architecture: Dense Transformer
- Sizes: 1.5B, 7B, 8B, 14B, 32B, 70B, 671B
- Context: 128K tokens
- License: MIT (open commercial)
- Strength: Reasoning & math
Llama 4
Meta AI · USA- Architecture: MoE (Mixture of Experts)
- Sizes: Scout (7.9B/109B), Maverick
- Context: 10M tokens (Scout)
- License: Llama 4 Community
- Strength: Multimodal & long context
Qwen 3.5
Alibaba DAMO · China- Architecture: MoE + Dense variants
- Sizes: 0.6B to 235B (A22B MoE)
- Context: 128K tokens
- License: Apache 2.0
- Strength: Multilingual & code
Benchmark Comparison 2026
All three models were tested at comparable parameter counts (7B–8B range) for fair hardware-normalized comparison. Results use publicly available benchmark data from MMLU, HumanEval, GSM8K, and MATH datasets:
| Benchmark | DeepSeek R1 7B | Llama 4 Scout | Qwen 3.5 7B | Winner |
|---|---|---|---|---|
| MMLU (General) | 82.4% | 79.8% | 85.0% | 🌐 Qwen 3.5 |
| HumanEval (Code) | 78.2% | 71.5% | 82.6% | 🌐 Qwen 3.5 |
| GSM8K (Math) | 91.2% | 85.1% | 88.4% | 🔍 DeepSeek |
| MATH (Hard Math) | 72.1% | 60.3% | 68.7% | 🔍 DeepSeek |
| Multilingual (29 langs) | 68.9% | 72.4% | 87.5% | 🌐 Qwen 3.5 |
| Vision/Image (VQA) | N/A | 74.2% | 62.1% | 🦙 Llama 4 |
| Long Context (128K) | Good | Excellent (10M) | Good | 🦙 Llama 4 |
Inference Speed Comparison
Speed was tested with Ollama on two hardware configurations: RTX 4060 Ti 16GB (Windows) and MacBook Pro M3 Pro 18GB (macOS). All models used Q4_K_M quantization:
| Model | RTX 4060 Ti (t/s) | M3 Pro 18GB (t/s) | Download Size |
|---|---|---|---|
| DeepSeek R1:7b | 52 t/s | 48 t/s | 4.7GB |
| Llama 4 Scout | 38 t/s | 35 t/s | 6.1GB |
| Qwen3.5:7b | 45 t/s | 42 t/s | 4.5GB |
| DeepSeek R1:32b | 18 t/s | 22 t/s | 20GB |
| Qwen3.5:32b | 16 t/s | 20 t/s | 20GB |
Speed Verdict
At the 7B scale, DeepSeek R1 is fastest (52 t/s on RTX 4060), followed by Qwen 3.5 (45 t/s), and then Llama 4 Scout (38 t/s). However, Llama 4's MoE architecture means it uses only 7.9B active parameters from 109B total, giving it frontier-level quality at mid-range speed. For pure speed-per-quality ratio, Qwen 3.5 and DeepSeek R1 deliver the most value on GPU.
DeepSeek R1 Deep Dive
DeepSeek R1, released by DeepSeek AI in January 2026, shocked the AI industry by achieving performance matching GPT-o1 and Claude 3.5 Sonnet at a fraction of the training cost. Its distinctive feature is chain-of-thought reasoning — the model "thinks out loud" before answering, showing its reasoning steps in a <think> block before giving the final answer.
Best for: Math problems, logical reasoning, step-by-step analysis, research summarization, and any task where accuracy matters more than speed. The 7B distilled version is particularly impressive — it was trained on DeepSeek R1's reasoning traces, giving a small model the reasoning capabilities of a much larger one.
✅ DeepSeek R1 Strengths
- • Best-in-class mathematical reasoning (beats most models twice its size)
- • Transparent thinking process helps users verify reasoning quality
- • MIT license — fully open for commercial use and modification
- • Available in 6 sizes from 1.5B to 671B for all hardware levels
- • Excellent for Chinese and English bilingual tasks
❌ DeepSeek R1 Weaknesses
- • No vision/image understanding capability
- • Chain-of-thought adds latency — responses start slower
- • Multilingual support weaker than Qwen 3.5 for non-Chinese languages
- • The full 671B model requires 400GB+ disk and extreme hardware
# Install DeepSeek R1 with Ollama:
ollama run deepseek-r1:7b # 7B distilled (4.7GB) — best for most users
ollama run deepseek-r1:14b # 14B for better reasoning (9GB)
ollama run deepseek-r1:32b # 32B for near-frontier results (20GB)
Llama 4 Deep Dive
Meta's Llama 4, released in early 2026, introduces a fundamentally different architecture from its predecessors. Rather than a traditional dense transformer, Llama 4 uses a Mixture-of-Experts (MoE) design. The Scout variant has 109 billion total parameters but only activates 7.9 billion for any given token — giving frontier-quality responses at mid-tier hardware requirements.
Best for: Multimodal tasks (analyzing images and documents), very long document processing (10M token context is extraordinary), general-purpose chatting, and creative writing. Llama 4 is the only model in this comparison with native image understanding built-in.
✅ Llama 4 Strengths
- • 10 million token context — process entire books, codebases, or datasets
- • Native multimodal: understands images, charts, and documents
- • MoE efficiency: frontier quality at mid-range hardware cost
- • Strong multilingual support across 12 languages
- • Meta's large research team ensures continued improvement
❌ Llama 4 Weaknesses
- • Llama 4 Community License has commercial restrictions (>700M MAU requires license)
- • Slower inference than same-size dense models due to MoE routing overhead
- • The full Scout model file is 32GB despite 7.9B active params
- • Math reasoning not as strong as DeepSeek R1's chain-of-thought
# Install Llama 4 with Ollama:
ollama run llama4 # Llama 4 Scout (default, recommended)
ollama run llama4:maverick # Llama 4 Maverick (larger, higher quality)
Qwen 3.5 Deep Dive
Alibaba's Qwen 3.5 series, released in 2025–2026, is arguably the most versatile open-source model family. Unlike the other two models, Qwen 3.5 comes in both MoE and dense variants, covering everything from the tiny 0.6B model that runs on smartphones to the massive 235B A22B MoE model that challenges the very best proprietary models on benchmarks.
Best for: International applications requiring strong multilingual support, coding assistance across many programming languages, agentic tasks (Qwen 3.5 has excellent tool calling), and users who need flexible scaling from a phone app (0.6B) all the way to a GPU cluster (235B).
✅ Qwen 3.5 Strengths
- • Best open-source multilingual model — 29+ languages at high quality
- • Apache 2.0 license — most permissive of the three, full commercial freedom
- • Widest size range (0.6B to 235B) for any hardware situation
- • Excellent code generation, especially for Python, JavaScript, and C++
- • Strong tool calling and agentic behavior for automation tasks
❌ Qwen 3.5 Weaknesses
- • No native vision support in most sizes (vision model is separate)
- • Math reasoning not as reliably structured as DeepSeek R1
- • The 235B model requires extreme hardware (8× A100 or similar)
# Install Qwen 3.5 with Ollama:
ollama run qwen3.5:0.6b # Tiny, phone-friendly (400MB)
ollama run qwen3.5:7b # Recommended for most users (4.5GB)
ollama run qwen3.5:14b # High quality (8.5GB)
ollama run qwen3.5:32b # Near-frontier quality (20GB)
Which Model Should You Choose?
🔍 Choose DeepSeek R1 if...
You need maximum reasoning quality for math, science, or logical problems. The chain-of-thought output makes it ideal for research, academic work, and anywhere you want to verify the model's reasoning process. The 7B distilled version is a remarkable value — it punches far above its weight class for analytical tasks.
🦙 Choose Llama 4 if...
You work with images, charts, or very long documents, or you need to process context that exceeds 128K tokens. The 10M token context window is unmatched in open-source models — it can analyze entire research papers, large codebases, or complete legal documents in one pass. Also the best choice for general creative writing.
🌐 Choose Qwen 3.5 if...
You need multilingual support beyond English and Chinese, work primarily on coding tasks, or want to build agents that use external tools. Also the best choice if you need the most flexible model (can run on a phone at 0.6B or scale to 235B on clusters) and want the most permissive commercial license (Apache 2.0).
📊 Quick Selection Guide
Hardware Requirements Summary
| Use Case | Recommended | Min VRAM | Ollama Command |
|---|---|---|---|
| Budget (4GB GPU) | Qwen 3.5:0.6b or DeepSeek R1:1.5b | 2GB | ollama run qwen3.5:0.6b |
| Mid-range (8GB GPU) | DeepSeek R1:7b or Qwen 3.5:7b | 6GB | ollama run deepseek-r1:7b |
| High-end (16GB GPU) | Qwen 3.5:14b or Llama 4 Scout | 12GB | ollama run llama4 |
| Apple Silicon (16-24GB) | Qwen 3.5:14b or DeepSeek R1:14b | N/A (unified) | ollama run qwen3.5:14b |
Advanced Tips: Getting the Most from Each Model
Once you've installed your chosen model via Ollama, these advanced techniques dramatically improve output quality and efficiency for all three models:
System Prompts for Better Reasoning
All three models respond exceptionally well to clear system prompts. For DeepSeek R1, a prompt like "Think step by step and show your reasoning" reinforces the chain-of-thought behavior. For Qwen 3.5, specifying the desired output format ("Answer in JSON", "Write only Python code") dramatically improves consistency. For Llama 4, "You have access to the provided document. Answer only from its contents" works well for long-document RAG tasks.
Temperature Settings
Default temperature (0.7–0.8) is good for creative writing. For factual Q&A and code, lower temperature (0.1–0.3) produces more consistent, accurate answers. In Ollama, set temperature via the API: {"options": {"temperature": 0.2}}. DeepSeek R1 benefits most from low temperature for math — its chain-of-thought becomes more deterministic and reliable.
Mixing Models for Different Sub-Tasks
Advanced users run different models for different parts of a workflow. A powerful pattern: use Qwen 3.5 7B for initial code drafting (fast, excellent syntax), then pipe the result to DeepSeek R1 7B for a reasoning-based code review (slower, but catches logic errors). This "chain of models" approach leverages each model's strengths without requiring massive hardware — both 7B models fit simultaneously on 16GB RAM or 12GB VRAM.
| Task Category | Best Model | Recommended Size | Temperature |
|---|---|---|---|
| Math & Science | DeepSeek R1 | 14B or 32B | 0.1–0.2 |
| Python / JavaScript | Qwen 3.5 | 14B | 0.2–0.3 |
| Image Analysis | Llama 4 | Scout | 0.5–0.7 |
| Japanese / Korean content | Qwen 3.5 | 7B | 0.5 |
| Creative writing | Llama 4 | Maverick | 0.7–0.9 |
| Complex debugging | DeepSeek R1 | 32B | 0.1 |
Frequently Asked Questions
Q: Which model has the best answer quality per dollar of hardware?
DeepSeek R1 7B and Qwen 3.5 7B deliver the best value per hardware dollar. Both run on a 6GB VRAM GPU (RTX 3060 or similar) that costs $200–300 used. You get 45–52 tokens/second with world-class reasoning or multilingual capabilities — performance that would have required a $2,000+ GPU setup just two years ago. For zero hardware cost, both also run on CPU with 8GB RAM, just slower.
Q: Can I switch between models mid-conversation?
Not within the same conversation thread — each conversation is associated with one model's context. However, you can use Open WebUI to maintain separate conversation histories for each model and switch between them at any time. Advanced users pipe outputs between models: copy the response from DeepSeek R1 and paste it as a prompt to Qwen 3.5 for a "second opinion" review. This manual chaining is surprisingly effective for complex analysis tasks.
Q: Are these models safe to use with confidential data?
Yes — when running locally with Ollama, your prompts and the model's responses stay entirely on your machine. No data is transmitted to any external server, cloud service, or the model's original developer. This is one of the strongest advantages of local LLMs over ChatGPT or Claude API — sensitive business documents, personal information, or confidential code can be processed with zero data leakage risk. For maximum security, disconnect from the internet before running sensitive sessions.
Privacy and License Considerations
All three models run completely locally once downloaded — your prompts and conversations never leave your machine. However, their licenses have important differences for commercial use:
DeepSeek R1
MIT License — Maximum freedom. Use in any commercial product, modify, distribute, no restrictions. The most permissive option of the three. Some organizations prefer this for compliance simplicity.
Llama 4
Llama 4 Community License — Free for most uses, but companies with over 700 million monthly active users must obtain a separate commercial license from Meta. Includes usage restrictions for certain applications.
Qwen 3.5
Apache 2.0 License — Very permissive. Commercial use, modification, and redistribution all allowed. Must retain copyright notice. No specific user count restrictions, making it ideal for startups and enterprise products.
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