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DeepSeek
DeepSeek V4 Pro
VS
ornith-ai
Ornith-1.5-35B-A3B
Analysis By: The WhichLLMModel Editorial Team|Updated: June 2026
Live Hardware Compatibility Simulator
GPU Dedicated VRAM16 GB
System Host RAM32 GB
DeepSeek V4 ProOOM / Exceeds Limit
VRAM Footprint (Q4):1037.31 GB
Active GPU Load:0 GB
Ornith-1.5-35B-A3BPartial CPU Offload
VRAM Footprint (Q4):21.59 GB
Active GPU Load:14.05 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | DeepSeek V4 Pro | Ornith-1.5-35B-A3B |
|---|---|---|
| Parameter Scale | 1600B Parameters | 35B Parameters |
| Attention Architecture | Standard GQA / MHA | GQA + DeltaNet Linear |
| Max Context Window | 1.05M | 262.14k |
| Available GGUF Quants | 0 Quant Formats | 2 Quant Formats |
| Full Model Page | Hardware Details | Hardware Details |
Intelligence & Benchmark Head-to-Head
Coding Benchmark (SWE-bench / HumanEval)
DeepSeek V4 Pro52.1%
Ornith-1.5-35B-A3B22%
Reasoning Benchmark (GPQA / MMLU)
DeepSeek V4 Pro88%
Ornith-1.5-35B-A3B89.2%
Frequently Asked Questions about DeepSeek V4 Pro vs Ornith-1.5-35B-A3B
Ornith-1.5-35B-A3B is cheaper than DeepSeek V4 Pro. Ornith-1.5-35B-A3B has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than DeepSeek V4 Pro at $NaN/1M tokens.
For coding tasks, DeepSeek V4 Pro scores 52.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while Ornith-1.5-35B-A3B scores 22% on deep-swe (frontier deep software engineering and end-to-end repository problem solving).
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