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DeepReinforcevsMistral AI
Ornith 1.5 35B A3B vs Codestral-22B-v0.1
Side-by-side technical showdown between Ornith 1.5 35B A3B and Codestral-22B-v0.1 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 1.5 35B A3B vs Codestral-22B-v0.1
Verified across benchmarks, pricing & local VRAM footprintVRAM Efficiency (INT4)
8 GB lighterLeader:Codestral-22B-v0.1(22.4 GB vs 14.1 GB)
Codestral-22B-v0.1 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
Ornith 1.5 35B A3B
22.4GB
Weights: ~19.3 GBKV Cache: ~0.2 GB+15% CUDA Buffer
Total Params:
35B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
262k tokens
Target Hardware: 2x RTX 4090 (TP=2) or 1x L40S (48GB) (TP=2)
Model 2Open Weights
Codestral-22B-v0.1
14.1GB
Weights: ~12.1 GBKV Cache: ~0.1 GB+15% CUDA Buffer
Total Params:
22B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
33k tokens
Target Hardware: 1x RTX 4070 / 4080 (16GB) or Mac (16GB Unified)
GPU & Hardware Tier Compatibility Matrix
| Hardware Configuration | Available VRAM | Ornith 1.5 35B A3B | Codestral-22B-v0.1 |
|---|---|---|---|
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB) | 16 GB | ✕ OOM | Tight (Low Ctx) |
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB) | 24 GB | Tight (Low Ctx) | 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 |
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 1.5 35B A3B | Codestral-22B-v0.1 |
|---|---|---|
| Input Cost (/1M tokens) | Free / Open | Free / Open |
| Cached Input (/1M tokens) | — | — |
| Output Cost (/1M tokens) | Free / Open | Free / Open |
| 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 | Ornith 1.5 35B A3B | Codestral-22B-v0.1 |
|---|---|---|
| Release Date | 8/18/2026 | 5/29/2024 |
| Routing / MoE | Dense | Dense |
| Attention Mechanism | Standard / GQA | Standard / GQA |
| Source Type | Open Weights (MIT) | Open Weights (Mistral AI Non-Production License) |
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