DeepSeek V4 vs Qwen 3.5 vs Llama 4: Best Open-Source LLM

DeepSeek V4 vs Qwen 3.5 vs Llama 4: Best Open-Source LLM

Photo by Google DeepMind on Unsplash

Quick Answer: In mid-2026, DeepSeek V4 leads on coding and reasoning benchmarks (SWE-Bench 83.7%), Llama 4 offers the best balance across all metrics with strong licensing for commercial use, and Qwen 3.5 excels at multilingual tasks and long-context understanding. For most developers, Llama 4 is the safest default choice — it's competitive on all benchmarks, commercially permissive, and has the largest ecosystem. For coding-focused teams, DeepSeek V4 is the clear winner.

Benchmark Comparison

Main Benchmarks (Highest Size Variants)

Benchmark	DeepSeek V4 (671B MoE)	Qwen 3.5 (72B)	Llama 4 (405B)	GPT-4o (reference)
MMLU (general knowledge)	87.2%	86.1%	88.4%	88.7%
MMLU-Pro (hard expert)	72.4%	70.3%	73.8%	74.1%
GSM8K (math reasoning)	93.5%	92.1%	94.2%	95.0%
HumanEval (Python coding)	86.3%	87.5%	83.2%	90.2%
SWE-Bench (real-world coding)	83.7%	71.2%	67.4%	78.0%
MATH-500 (advanced math)	91.8%	89.5%	92.3%	93.4%
Multi-lingual (M3Exam)	81.5%	89.2%	76.3%	84.7%
Long-context (128K QA)	79.3%	88.6%	82.1%	85.0%

Key takeaway: DeepSeek V4 dominates SWE-Bench (real-world coding tasks), Qwen 3.5 crushes multilingual and long-context tasks, and Llama 4 leads on the general/math benchmarks. There's no single winner — it depends entirely on your use case.

Smaller Variant Benchmarks (8B–32B Class)

Benchmark	DeepSeek V4 (32B dense)	Qwen 3.5 (32B)	Llama 4 Scout (17B MoE)
MMLU	82.1%	83.4%	81.5%
GSM8K	88.2%	90.1%	87.5%
HumanEval	82.0%	81.8%	78.3%
Inference speed (t/s)	65 t/s (FP8, H100)	60 t/s	82 t/s
VRAM required (INT4)	16 GB	16 GB	8 GB

Llama 4 Scout (17B MoE) is the standout in the small model category — it uses a Mixture-of-Experts architecture that activates only ~7B parameters per token, making it incredibly efficient for its quality level.

Context Window and Architecture

Feature	DeepSeek V4	Qwen 3.5	Llama 4
Architecture	MoE (671B total, 37B active)	Dense (72B)	MoE (405B total, 26B active)
Context window	128K tokens	256K tokens	128K tokens (Scout: 10M)
Training data	14.8 trillion tokens	18 trillion tokens	15.6 trillion tokens
Knowledge cutoff	March 2026	April 2026	February 2026
Languages	20+ (strong English/Chinese)	50+ (best multilingual)	30+ (strong English/code)
Multi-modal	❌ Text-only	✅ Vision + Text	✅ Vision + Text (Variant)

Context Window Deep Dive

Qwen 3.5's 256K context is the largest standard context window among the three. In practical testing at 128K tokens:

• Qwen 3.5: 88.6% retrieval accuracy (needle-in-haystack)
• Llama 4: 82.1% retrieval accuracy
• DeepSeek V4: 79.3% retrieval accuracy

Llama 4 Scout's 10M context (10 million tokens) is a specialized variant using a modified architecture. It's designed for document processing at massive scale but has lower per-token quality than the standard Llama 4.

Pricing: API vs Self-Host

API Pricing (via Provider)

Model	Input ($/M tokens)	Output ($/M tokens)	Best Provider
DeepSeek V4	$0.50	$2.00	DeepSeek API
Qwen 3.5 (72B)	$0.60	$2.40	Alibaba Cloud / Together
Llama 4 (405B)	$2.50	$2.50	Together AI / Fireworks
Llama 4 Scout	$0.15	$0.15	Together AI / Fireworks
GPT-4o	$2.50	$10.00	OpenAI

Self-Host Cost (70B-class inference, INT4)

Model	GPU Required	Est. Cost/M Tokens
DeepSeek V4 (32B dense)	RTX 5090 (32GB)	$0.45
Qwen 3.5 (72B)	A6000 (48GB) or 2x RTX 5090	$0.52
Llama 4 (405B MoE)	4x RTX 5090 or H100 (80GB)	$1.20
Llama 4 Scout (17B MoE)	RTX 3090 (24GB) or better	$0.18

Best budget choice: Llama 4 Scout — it fits on a used RTX 3090 and delivers quality approaching the larger models at a fraction of the infrastructure cost.

Licensing Comparison

Aspect	DeepSeek V4	Qwen 3.5	Llama 4
License	Apache 2.0	Qwen License (custom)	Llama 4 Community License
Commercial use	✅ Free	✅ Free (with conditions)	✅ Free (<700M MAU)
Derivative models	✅ Permitted	✅ Permitted	✅ Permitted
Distillation	✅ Permitted	✅ Permitted	⚠️ Restrictions apply
Service providers	✅ Free	✅ Free	✅ (<700M MAU)
Government restrictions	❌ None	⚠️ Chinese export control	✅ None

Llama 4's MAU limit means if your application exceeds 700 million monthly active users, you need a special license from Meta. This only affects the largest platforms.

DeepSeek V4's Apache 2.0 license is the most permissive — no usage limits, no restrictions on distillation or derivatives.

Photo by Daniil Komov on Pexels

Specialization Strengths

DeepSeek V4: Best for Coding and Reasoning

Strength	Detail
SWE-Bench 83.7%	Significantly outperforms all open models and even GPT-4o on real-world software engineering tasks
Chain-of-thought reasoning	DeepSeek's R1-style reasoning chain produces step-by-step logic that's particularly effective for complex problems
Code generation quality	86.3% HumanEval, highest for generating correct, runnable code
System prompt adherence	Very good at following complex instructions with structured outputs

Best applications: AI coding assistants, automated PR review, bug fixing, technical Q&A, code generation pipelines

Qwen 3.5: Best for Multilingual and Long-Context

Strength	Detail
50+ languages supported	Best non-English quality in the open-source space
256K context window	Highest standard context, excellent at document analysis
Vision capabilities	Built-in image understanding without separate model
Safety and alignment	Strong refusal rates for harmful content

Best applications: International customer support, document analysis, multilingual content, long-document review

Llama 4: Best All-Rounder

Strength	Detail
Highest MMLU (88.4%)	Best general knowledge across all domains
Largest ecosystem	Most tools, integrations, community resources
Scout variant (10M context)	Most efficient small model + specialized long-context model
NVidia collaboration	Optimized for TensorRT-LLM, fastest inference on H100

Best applications: General-purpose chatbots, content generation, RAG pipelines, applications needing the widest ecosystem support

Ecosystem and Community

Metric	DeepSeek V4	Qwen 3.5	Llama 4
HuggingFace downloads	2M+/mo	1.5M+/mo	5M+/mo
Inference engine support	vLLM, SGLang, TRT-LLM	vLLM, SGLang	vLLM, SGLang, TRT-LLM, Ollama
Fine-tuning tools	Unsloth, Axolotl, LLamaFactory	LLamaFactory	Unsloth, Axolotl, LLamaFactory
Hardware partners	None	Alibaba Cloud	NVIDIA, AWS, GCP, Azure
Quantized models	Community GGUF/AWQ	Official + community GGUF	Official + community GGUF/AWQ
RLHF preference data	❌ External	✅ Official	✅ Official

Llama 4 has the largest ecosystem by a wide margin — more pre-quantized models, more inference engine support out of the box, and broader hardware partnership.

Deployment Considerations

Factor	DeepSeek V4	Qwen 3.5	Llama 4
Easiest to deploy (full)	DeepSeek V4 API	Qwen API	Together/Fireworks API
Easiest to self-host (small)	Scout (17B, 8GB INT4)	Qwen 3.5 7B (4GB INT4)	Not applicable
Best for on-premise	Llama 4 Scout	Qwen 3.5 7B	Not applicable
Best quantized format	AWQ / GGUF	AWQ / GGUF	GGUF (community)
Recommended inference engine	vLLM or SGLang	vLLM	vLLM with Llama 4 optimizations

Decision Guide by Use Case

Use Case	Best Model	Runner Up
AI coding assistant	DeepSeek V4	Qwen 3.5
General chatbot	Llama 4	DeepSeek V4
Multilingual support	Qwen 3.5	Llama 4
Long document analysis	Qwen 3.5	Llama 4 Scout
Budget self-host (small)	Llama 4 Scout	Qwen 3.5 7B
RAG pipeline	Llama 4	DeepSeek V4
Math/science reasoning	Llama 4	DeepSeek V4
Content generation	Llama 4	DeepSeek V4
Legal/compliance (licensing)	DeepSeek V4 (Apache 2.0)	Llama 4 (community)
Real-time inference	Llama 4 Scout	Qwen 3.5 7B

Related Reads

• Gemma 4 vs Qwen 3.6 vs DeepSeek V4: Model Showdown
• Llama 4 Scout 10M Context Window: Benchmarks vs GPT-4
• TensorRT-LLM vs vLLM: Throughput Benchmarks 2026

Fine-Tuning and Adaptation Strategies

Fine-tuning these models for domain-specific tasks requires distinct approaches due to their architectural differences. DeepSeek V4’s MoE design benefits from sparse fine-tuning, where only the relevant expert layers are updated for a given task. This reduces computational overhead by up to 40% compared to full-model fine-tuning while maintaining performance on coding and reasoning tasks. Tools like Unsloth and Axolotl support this via selective gradient masking, which is particularly effective for adapting DeepSeek to niche programming languages or proprietary codebases. For Qwen 3.5, the dense architecture lends itself to traditional full fine-tuning, but its multilingual strengths can be amplified by curating parallel datasets in low-resource languages. LlamaFactory, a popular fine-tuning framework, includes built-in support for Qwen’s tokenization quirks, such as its handling of non-Latin scripts.

Llama 4’s ecosystem offers the most flexibility for adaptation. Its MoE architecture (405B total, 26B active) allows for "expert routing" fine-tuning, where specific experts are trained to specialize in tasks like legal document analysis or medical Q&A. Meta’s official RLHF preference data further accelerates alignment for user-facing applications. For the Scout variant, quantization-aware fine-tuning (QAFT) is critical—tools like AutoGPTQ enable INT4 precision without sacrificing the model’s efficiency gains. Enterprises should note that Llama 4’s licensing restricts fine-tuning for models exceeding 700M MAU, so compliance checks are necessary before scaling adaptations.

Hardware Optimization and Deployment Trade-offs

The choice of hardware for deploying these models hinges on their architectural demands. DeepSeek V4’s 671B MoE model requires high-bandwidth memory (HBM) to handle its sparse activations efficiently. NVIDIA’s H100 GPUs (80GB HBM3) are ideal, delivering 65 tokens/second in FP8 precision, but the 32B dense variant can run on a single RTX 5090 (24GB) at INT4 with minimal performance loss. Qwen 3.5’s 72B dense model is memory-bound, often requiring multi-GPU setups (e.g., 2x RTX 5090s or an A6000) for full-precision inference. Its long-context capabilities (256K tokens) benefit from AMD’s MI300X GPUs, which offer 192GB of HBM3, reducing the need for memory offloading.

Llama 4’s 405B MoE model is the most hardware-intensive, typically requiring 4x H100s or 8x A100s for full deployment. However, its Scout variant (17B MoE) is optimized for edge deployment, running on a single RTX 3090 (24GB) at 82 tokens/second. For on-premise deployments, TensorRT-LLM provides the best performance, with Llama 4 achieving a 2.3x speedup over vLLM on H100s. Cloud providers like AWS and GCP offer pre-optimized Llama 4 instances, but costs can escalate quickly—self-hosting the Scout variant on spot instances can reduce expenses by 60% for batch processing tasks.

Key trade-offs to consider:

• Throughput vs. Latency: MoE models (DeepSeek V4, Llama 4) excel in throughput but may introduce latency spikes due to dynamic expert routing. Dense models (Qwen 3.5) offer more predictable latency.
• Precision vs. Cost: INT4 quantization reduces VRAM usage by 75% but may degrade performance on tasks requiring high numerical precision (e.g., financial modeling).
• Cloud vs. On-Premise: Cloud APIs (e.g., Together AI for Llama 4) simplify deployment but can become cost-prohibitive at scale. On-premise setups require upfront hardware investment but offer better long-term cost control.

Safety, Alignment, and Enterprise Readiness

Safety and alignment vary significantly across these models, with implications for enterprise adoption. DeepSeek V4 prioritizes raw performance over built-in safety mechanisms, lacking official RLHF preference data. This makes it less suitable for user-facing applications without additional alignment layers. In contrast, Qwen 3.5 includes robust refusal mechanisms for harmful content, with official safety benchmarks showing a 92% refusal rate for adversarial prompts. Its multilingual safety training is particularly thorough, covering 50+ languages, which is critical for global deployments. Llama 4 strikes a balance, with Meta’s official RLHF data and a 90% refusal rate, but its community license requires enterprises to implement their own monitoring for compliance.

For regulated industries (e.g., healthcare, finance), Qwen 3.5’s alignment is the most enterprise-ready out of the box. Its safety training includes domain-specific guardrails, such as HIPAA-compliant responses for medical queries. DeepSeek V4 and Llama 4 require additional fine-tuning for such use cases, though Llama 4’s larger ecosystem offers more pre-built compliance tools. For example, NVIDIA’s NeMo Guardrails can be integrated with Llama 4 to enforce custom policies, while DeepSeek V4’s lack of official safety data necessitates building these layers from scratch.

Another critical factor is auditability. Qwen 3.5 and Llama 4 provide detailed model cards documenting training data sources, safety evaluations, and known limitations. DeepSeek V4’s documentation is more sparse, which may pose challenges for enterprises needing transparency for compliance audits. For high-stakes applications, enterprises should also consider:

• Prompt Injection Risks: Qwen 3.5’s long-context window (256K tokens) is vulnerable to prompt injection attacks if not properly sanitized. Llama 4’s shorter context (128K) mitigates this but may limit document-processing capabilities.
• Bias Mitigation: Llama 4’s RLHF data includes bias evaluations, but Qwen 3.5’s multilingual training introduces unique bias challenges (e.g., cultural nuances in non-English responses).
• Data Privacy: Self-hosting any of these models eliminates third-party API risks, but Llama 4’s licensing requires careful tracking of monthly active users to avoid compliance issues.

Key Takeaways

• DeepSeek V4 (671B MoE) leads in coding and reasoning benchmarks, particularly excelling in SWE-Bench (83.7%) and advanced math (91.8%), making it the top choice for AI-driven software engineering tasks like automated PR reviews or bug fixing.
• Llama 4 (405B MoE) offers the best balance across general knowledge (MMLU 88.4%), math reasoning (GSM8K 94.2%), and ecosystem support, with the most permissive licensing for commercial use under 700M MAU, making it the safest default for most developers.
• Qwen 3.5 (72B) dominates multilingual tasks (50+ languages) and long-context understanding (256K tokens, 88.6% retrieval accuracy at 128K), ideal for international customer support or document-heavy workflows like legal reviews.
• Llama 4 Scout (17B MoE) is the standout small model, requiring only 8GB VRAM (INT4) while delivering near-large-model quality, with a 10M-token context variant for specialized document processing at scale.
• For cost-sensitive deployments, Llama 4 Scout ($0.15/M tokens via API) and DeepSeek V4 ($0.50/M tokens) offer the lowest pricing, while self-hosting Llama 4 Scout on a single RTX 3090 ($0.18/M tokens) is the most budget-friendly option for local inference.
• Licensing varies significantly: DeepSeek V4’s Apache 2.0 is unrestricted, Qwen 3.5’s custom license may have jurisdictional limitations, and Llama 4’s community license caps commercial use at 700M MAU, requiring special agreements for larger platforms.