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DeepReinforcevsDeepSeek
Ornith 1.0 9B vs DeepSeek V4 Flash Vision Exp
Side-by-side technical showdown between Ornith 1.0 9B and DeepSeek V4 Flash Vision Exp on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.
Executive Showdown & Winner Breakdown
Who Wins Where: Ornith 1.0 9B vs DeepSeek V4 Flash Vision Exp
Verified across benchmarks, pricing & local VRAM footprintLocal Portability & Sovereignty
Self-HostableLeader:Ornith 1.0 9B(Open Weights vs Closed API)
Ornith 1.0 9B can be deployed on private GPUs, Ollama, and on-prem clusters without third-party vendor lock-in.
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
Ornith 1.0 9B
5.8GB
Weights: ~5 GBKV Cache: ~0.1 GB+15% CUDA Buffer
Total Params:
9B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
262k tokens
Target Hardware: 1x RTX 4070 / 4080 (16GB) or Mac (16GB Unified)
Model 2Cloud Hosted
DeepSeek V4 Flash Vision Exp
Zero Local VRAM
Inference runs fully on provider cloud infrastructure. Zero GPU required locally.
Total Params:
Proprietary
Active Compute:
Dense
Attention Scheme:
MLA (Multi-Head Latent)
Max Context:
1000k tokens
Target Hardware: Zero Local VRAM (Managed Cloud API)
GPU & Hardware Tier Compatibility Matrix
| Hardware Configuration | Available VRAM | Ornith 1.0 9B | DeepSeek V4 Flash Vision Exp |
|---|---|---|---|
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB) | 16 GB | Optimal | Cloud API |
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB) | 24 GB | Optimal | Cloud API |
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB) | 48 GB | Optimal | Cloud API |
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB) | 80 GB | Optimal | Cloud API |
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB) | 160 GB | Optimal | Cloud API |
Multi-Node Cluster 4x–8x H100 Datacenter Cluster | 320 GB | Optimal | Cloud API |
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
Ornith 1.0 9B
69.4
DeepSeek V4 Flash Vision Exp
—
HumanEval (Python Code)Score % / Points
Ornith 1.0 9B
63.1
DeepSeek V4 Flash Vision Exp
—
Inference Economics & Cost Simulator
Token Pricing & Scale Economics
Calculate estimated monthly cloud API spend and evaluate the breakeven point vs self-hosted GPU hardware.
10k500k1M
| Pricing Metric | Ornith 1.0 9B | DeepSeek V4 Flash Vision Exp |
|---|---|---|
| Input Cost (/1M tokens) | Free / Open | $0.44 |
| Cached Input (/1M tokens) | — | — |
| Output Cost (/1M tokens) | Free / Open | $1.32 |
| Simulated Monthly Bill (100,000 calls) | $0 API Cost Open Weights | $83.60 ~$0.84 / 1k queries |
| Self-Hosted Breakeven vs $864/mo GPU | Self-Hostable Day 1 | Cloud API is more cost effective |
Architecture Matrix
| Feature | Ornith 1.0 9B | DeepSeek V4 Flash Vision Exp |
|---|---|---|
| Release Date | 6/25/2026 | 8/21/2026 |
| Routing / MoE | Dense | Dense |
| Attention Mechanism | Standard / GQA | Standard / GQA |
| Source Type | Open Weights (MIT) | Proprietary Commercial API |
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