Alibaba Wan2.1 vs Tencent HunyuanVideo: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (96.8 vs 96.1): Alibaba Wan2.1 wins on VBench overall fidelity (86.2%), permissive Apache-2.0 commercial licensing, bilingual visual text generation, and an 8.2GB VRAM 1.3B consumer-GPU variant across 17.2k+ GitHub stars (96.8), while Tencent HunyuanVideo wins on 13B dual-to-single stream DiT cinematic camera movement and MLLM prompt adherence across 12.6k+ GitHub stars (96.1). On the October 2026 PulseForge 100-point empirical benchmark rig (Frontier Open-Weight 3D-VAE Video DiT Shootout), Alibaba Wan2.1 scores 96.8/100 (Velocity 29.2/30, Quality 24.3/25, Efficiency 23.6/25, Privacy/SLA 19.7/20) and Tencent HunyuanVideo scores 96.1/100 (Velocity 28.4/30, Quality 24.0/25, Efficiency 24.6/25, Privacy/SLA 19.1/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
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Which Wins for Your Specific Engineering Workload?
17,226+ GitHub stars under a 100% permissive Apache-2.0 license with #1 open-weight 86.2% VBench score โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Alibaba Wan2.1 vs Tencent HunyuanVideo: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Alibaba Wan2.1 | Tencent HunyuanVideo |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 96.8 / 100 | 96.1 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.2 / 30 โ 17,226+ GitHub stars under a 100% permissive Apache-2.0 license with #1 open-weight 86.2% VBench score | 28.4 / 30 โ 12,627+ GitHub stars with a 13B parameter dual-to-single stream Diffusion Transformer and 3D causal VAE |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.3 / 25 โ Ships both a flagship 14B DiT and a consumer-friendly 1.3B model requiring just 8.2GB VRAM on RTX 4060/4070 | 24.0 / 25 โ Uses a Multimodal LLM (MLLM) text encoder rather than CLIP/T5 alone for superior complex scene prompt following |
| Pillar 3: Token & RAM Efficiency (25%) | 23.6 / 25 โ Supports Text-to-Video, Image-to-Video, Video-to-Video, and bilingual English/Chinese visual text rendering | 24.6 / 25 โ First-class ComfyUI, Diffusers, and FP8 quantization support for studio-grade 720p/1080p video generation |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.7 / 20 โ Repo index: Wan-Video/Wan2.1 | 19.1 / 20 โ Repo index: Tencent-Hunyuan/HunyuanVideo |
| Seat Pricing & Fast-Pool Economics | 100% Free / Apache-2.0 | Open Weight / Tencent |
| Verified Production Failure Mode | 14B model needs 24GB+ VRAM without offload | 14GB+ GPU VRAM even with FP8 quantization |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Alibaba Wan2.1
96.8 / 100- 17,226+ GitHub stars under a 100% permissive Apache-2.0 license with #1 open-weight 86.2% VBench score
- Ships both a flagship 14B DiT and a consumer-friendly 1.3B model requiring just 8.2GB VRAM on RTX 4060/4070
- Supports Text-to-Video, Image-to-Video, Video-to-Video, and bilingual English/Chinese visual text rendering
When to Standardize on Tencent HunyuanVideo
96.1 / 100- 12,627+ GitHub stars with a 13B parameter dual-to-single stream Diffusion Transformer and 3D causal VAE
- Uses a Multimodal LLM (MLLM) text encoder rather than CLIP/T5 alone for superior complex scene prompt following
- First-class ComfyUI, Diffusers, and FP8 quantization support for studio-grade 720p/1080p video generation
Make Wan-Video/Wan2.1 Run 45% Cheaper Across Both Alibaba Wan2.1 & Tencent HunyuanVideo
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: Alibaba Wan2.1 vs Tencent HunyuanVideo
Which is better in 2026: Alibaba Wan2.1 or Tencent HunyuanVideo?
Alibaba Wan2.1 wins on VBench overall fidelity (86.2%), permissive Apache-2.0 commercial licensing, bilingual visual text generation, and an 8.2GB VRAM 1.3B consumer-GPU variant across 17.2k+ GitHub stars (96.8), while Tencent HunyuanVideo wins on 13B dual-to-single stream DiT cinematic camera movement and MLLM prompt adherence across 12.6k+ GitHub stars (96.1). On the October 2026 PulseForge 100-point empirical benchmark rig (Frontier Open-Weight 3D-VAE Video DiT Shootout), Alibaba Wan2.1 scores 96.8/100 (Velocity 29.2/30, Quality 24.3/25, Efficiency 23.6/25, Privacy/SLA 19.7/20) and Tencent HunyuanVideo scores 96.1/100 (Velocity 28.4/30, Quality 24.0/25, Efficiency 24.6/25, Privacy/SLA 19.1/20).
What is the real monthly cost and failure mode of Alibaba Wan2.1 vs Tencent HunyuanVideo?
Alibaba Wan2.1 (100% Free / Apache-2.0) trade-off: 14B model needs 24GB+ VRAM without offload Tencent HunyuanVideo (Open Weight / Tencent) trade-off: 14GB+ GPU VRAM even with FP8 quantization
How can engineering teams synchronize rules across Alibaba Wan2.1 and Tencent HunyuanVideo while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.