What is the difference between Qwen3.8 27B and HunyuanVideo Pro?

Qwen3.8 27B (Alibaba Cloud) features 27B with a 262k context window, while HunyuanVideo Pro (Tencent AI Lab) features 13B with a 16k context window.

Which is cheaper for production inference, Qwen3.8 27B or HunyuanVideo Pro?

Qwen3.8 27B input pricing is Open / Free per 1M tokens vs HunyuanVideo Pro at $0 per 1M tokens.

Can I run Qwen3.8 27B or HunyuanVideo Pro locally on private GPU hardware?

Qwen3.8 27B is an open-weights model and can be hosted locally using Ollama or vLLM. HunyuanVideo Pro is an open-weights model and can be hosted locally.

Back to LLM Benchmark & Hardware Sizing Engine

Alibaba CloudvsTencent AI Lab

Qwen3.8 27B vs HunyuanVideo Pro

Side-by-side technical showdown between Qwen3.8 27B and HunyuanVideo Pro on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.

Executive Showdown & Winner Breakdown

Who Wins Where: Qwen3.8 27B vs HunyuanVideo Pro

Verified across benchmarks, pricing & local VRAM footprint

VRAM Efficiency (INT4)

9 GB lighter

Leader:HunyuanVideo Pro(17.3 GB vs 8.3 GB)

HunyuanVideo Pro requires substantially less memory to run at scale, fitting on more accessible GPU tiers.

Hardware Sizing & Model Compression

LLM Hardware Sizing & Quantization Sizing

Simulate weight compression levels and dynamic KV-cache expansion to verify whether these models fit on your local hardware or cloud GPU cluster.

Quantization Level

Active Precision

4 bits / parameter

VRAM Reduction

-72.5% vs FP16

Benchmark Retention

96–98% (Sweet Spot)

Context Simulator

Total VRAM Footprint @ 8k Context (INT4 (GGUF / AWQ))

Model 1Open Weights

Qwen3.8 27B

17.3GB

Weights: ~14.9 GBKV Cache: ~0.2 GB+15% CUDA Buffer

Total Params:

27B

Active Compute:

Dense

Attention Scheme:

GQA (Grouped-Query)

Max Context:

262k tokens

Target Hardware: 1x RTX 3090 / 4090 (24GB) or Mac (32GB Unified)

Model 2Open Weights

HunyuanVideo Pro

8.3GB

Weights: ~7.2 GBKV Cache: ~0.1 GB+15% CUDA Buffer

Total Params:

13B

Active Compute:

Dense

Attention Scheme:

GQA (Grouped-Query)

Max Context:

16k tokens

Target Hardware: 1x RTX 4070 / 4080 (16GB) or Mac (16GB Unified)

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	Qwen3.8 27B	HunyuanVideo Pro
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB)	16 GB	✕ OOM	Optimal
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)	24 GB	Optimal	Optimal
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB)	48 GB	Optimal	Optimal
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB)	80 GB	Optimal	Optimal
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB)	160 GB	Optimal	Optimal
Multi-Node Cluster 4x–8x H100 Datacenter Cluster	320 GB	Optimal	Optimal

LLM Benchmark Database

LLM Benchmark Showdown & Head-to-Head Deltas

Normalized evaluation scores across code generation, advanced reasoning, mathematics, and multidisciplinary exams.

SWE-bench (Coding)Score % / Points

Qwen3.8 27B

61.7

HunyuanVideo Pro

—

Inference Economics & Cost Simulator

Token Pricing & Scale Economics

Calculate estimated monthly cloud API spend and evaluate the breakeven point vs self-hosted GPU hardware.

Monthly Queries: 100,000

10k500k1M

Avg Prompt Tokens: 1000

Avg Output Tokens: 300

Pricing Metric	Qwen3.8 27B	HunyuanVideo Pro
Input Cost (/1M tokens)	Free / Open	$0.00
Cached Input (/1M tokens)	—	—
Output Cost (/1M tokens)	Free / Open	$0.00
Simulated Monthly Bill (100,000 calls)	$0 API Cost Open Weights	$0 API Cost Open Weights
Self-Hosted Breakeven vs $864/mo GPU	Self-Hostable Day 1	Self-Hostable Day 1

Architecture Matrix

Feature	Qwen3.8 27B	HunyuanVideo Pro
Release Date	8/14/2026	8/22/2026
Routing / MoE	Dense	Dense
Attention Mechanism	Standard / GQA	Standard / GQA
Source Type	Open Weights (Open)	Open Weights (Apache 2.0)

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