DeepSeek V4 vs Qwen 3.7 vs Llama 4.5: Which Open-Weight LLM Should You Self-Host in 2026?

The three biggest open-weight releases of 2026 are all Mixture-of-Experts flagships, all claim frontier-level performance, and all cost a fraction of what GPT-5.5 or Claude Opus 4.7 charge through APIs. But they are not interchangeable. If you are deciding which one to deploy on your own hardware, the right answer depends on what you are actually building.

This guide breaks down the benchmarks, hardware requirements, licensing, and real-world trade-offs to help you pick the right model for your workload.

The Three Contenders

All three use Mixture-of-Experts architectures, meaning only a fraction of parameters activate per token. This is why a 1.6T model can run on less hardware than a dense 100B model. But the hardware requirements still differ significantly.

Benchmark Showdown

The headline numbers from each lab look impressive, but they tell different stories depending on the task.

Coding and Software Engineering

DeepSeek V4 Pro is the clear open-weight leader on SWE-Bench Verified at 83.7%, closing the gap with proprietary models like Claude Opus 4.7 (85.1%). If your workload involves code generation, bug fixing, or agentic software engineering, this is your model.

Qwen 3.7 Max scores around 80.4% on the same benchmark — competitive but not leading. Llama 4.5 Maverick trails at approximately 70%.

Winner: DeepSeek V4 Pro

Scientific Reasoning

Qwen 3.7 Max dominates GPQA Diamond at 88.4%, the best score among open weights and competitive with all but the very top proprietary models. For research workloads, scientific question-answering, and complex multi-step reasoning, Qwen is the strongest choice.

DeepSeek V4 Pro scores around 85% on GPQA. Llama 4.5 trails at approximately 80%.

Winner: Qwen 3.7 Max

Long Context

Llama 4.5 Scout offers a 10 million token context window — by far the longest available in any open-weight model. For processing entire codebases, long documents, or multi-file analysis, nothing else comes close.

DeepSeek V4 and Qwen 3.7 both offer 1 million token windows, which covers most practical use cases but falls short for extreme-length workflows.

Winner: Llama 4.5 Scout (10M), DeepSeek/Qwen (1M, practical)

Math and Structured Data

DeepSeek V4 Pro leads on quantitative benchmarks including HMMT and competition mathematics. Its Compressed Sparse Attention architecture is specifically optimized for the kind of structured, predictable token patterns that math and code produce.

Winner: DeepSeek V4 Pro

Hardware Requirements: What You Actually Need

This is where the decision gets real. The active parameter count determines inference cost, but total parameter count determines VRAM requirements.

DeepSeek V4 Pro (49B active, 1.6T total)

• Full precision (FP16): 8x H100 80GB or equivalent (~320GB VRAM)
• Quantized (INT4/INT8): 4x H100 or 8x A100 40GB
• Cloud equivalent: Together AI, Fireworks, DeepInfra at approximately $0.14/M input tokens
• Innovation: Compressed Sparse Attention reduces inference FLOPs to roughly 27% of comparable dense models, with only 10% of typical KV-cache memory. This is the most efficient MoE inference architecture shipping in 2026.

Qwen 3.7 Max (49B active, ~1.6T total)

• Full precision: Similar to DeepSeek V4 — 8x H100 class
• Quantized: 4x H100 or comparable
• Cloud equivalent: Alibaba API, Together AI at approximately $0.20/M input tokens
• Note: Smaller open-weight siblings (Qwen 3.6-35B-A3B) can run on a single H100 or even high-end consumer hardware

Llama 4.5 Maverick (17B active, ~400B total)

• Full precision: 2x H100 80GB
• Quantized (INT4): Single H100 or RTX 4090 cluster (4x 24GB)
• Cloud equivalent: Together AI, Groq at approximately $0.20/M input tokens
• Advantage: The lightest hardware footprint of the three flagships thanks to lower total parameter count

Quick Hardware Cheat Sheet

Licensing: The Hidden Decision

This is where DeepSeek pulls ahead for commercial deployments.

DeepSeek V4: MIT License. The most permissive license in the frontier tier. No usage caps, no attribution requirements beyond standard MIT, no restrictions on commercial use. You can fine-tune, redistribute, and sell derived products with zero friction.

Qwen 3.7: Qwen License (Apache 2.0-like). Permissive for most use cases but with some commercial use clauses. The Max tier is closed-weights (API only); smaller siblings like Qwen 3.6-35B-A3B are fully open under Apache 2.0.

Llama 4.5: Llama Community License. Permissive but includes the 700 million monthly active user cap. If your product exceeds that threshold, you need a separate commercial license from Meta. For most enterprise self-hosting, this is irrelevant — but for consumer-facing products, it is a real constraint.

For maximum commercial freedom: DeepSeek V4 wins clearly.

The Decision Framework

Choose DeepSeek V4 Pro if:

• Your primary workload is coding, math, or structured data processing
• You need MIT-level license clarity for commercial redistribution
• You want the most efficient inference architecture (Compressed Sparse Attention)
• Cost per token is the primary constraint (~$0.14/M input on open hosters)

Choose Qwen 3.7 Max if:

• Scientific reasoning and broad cognitive tasks are your priority
• You need strong multilingual performance, especially for Asian languages
• Your workload benefits from native multimodal (vision + text) understanding
• You can work with the API tier or smaller open-weight siblings

Choose Llama 4.5 Maverick if:

• You need extreme long-context (10M tokens with Scout variant)
• Hardware budget is constrained (runs on 2x H100 or even 4x consumer GPUs)
• You want a non-Chinese-origin model for compliance or geopolitical reasons
• Your MAU count stays under 700 million

The Practical Reality: Mix and Match

Most production teams in 2026 do not pick one model. They route between them:

• Triage and classification → DeepSeek V4 Flash (cheapest, fast)
• Complex reasoning and analysis → Qwen 3.7 Max (strongest general reasoner)
• Long-document processing → Llama 4.5 Scout (10M context)
• Code generation and review → DeepSeek V4 Pro (best open-weight coder)

With tools like vLLM, SGLang, or lmdeploy, you can host multiple models behind a single routing layer and dispatch based on task type. This gives you the best of all three without locking into one architecture.

Bottom Line

The open-weight gap with proprietary frontier models has effectively closed for most practical workloads. DeepSeek V4 Pro, Qwen 3.7 Max, and Llama 4.5 Maverick are all viable production choices — they just optimize for different things.

Pick the one that matches your actual workload. Or better yet, run two and route between them. The hardware costs less than a month of GPT-5.5 API bills.

Benchmark data sourced from provider model cards, SWE-Bench public leaderboard, and third-party evaluations from Presenc AI and Taskade. Hardware requirements are approximate — always validate with your specific quantization and batch size configuration.