What is the difference between Ornith 1.5 35B A3B and Mistral Small 3.1 24B?

Ornith 1.5 35B A3B (DeepReinforce) features 35B with a 262k context window, while Mistral Small 3.1 24B (Mistral AI) features 24B with a 128k context window.

Which is cheaper for production inference, Ornith 1.5 35B A3B or Mistral Small 3.1 24B?

Ornith 1.5 35B A3B input pricing is Open / Free per 1M tokens vs Mistral Small 3.1 24B at Open / Free per 1M tokens.

Can I run Ornith 1.5 35B A3B or Mistral Small 3.1 24B locally on private GPU hardware?

Ornith 1.5 35B A3B is an open-weights model and can be hosted locally using Ollama or vLLM. Mistral Small 3.1 24B is an open-weights model and can be hosted locally.

Back to LLM Benchmark & Hardware Sizing Engine

DeepReinforcevsMistral AI

Ornith 1.5 35B A3B vs Mistral Small 3.1 24B

Side-by-side technical showdown between Ornith 1.5 35B A3B and Mistral Small 3.1 24B 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 Mistral Small 3.1 24B

Verified across benchmarks, pricing & local VRAM footprint

VRAM Efficiency (INT4)

7 GB lighter

Leader:Mistral Small 3.1 24B(22.4 GB vs 15.3 GB)

Mistral Small 3.1 24B 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

Mistral Small 3.1 24B

15.3GB

Weights: ~13.2 GBKV Cache: ~0.1 GB+15% CUDA Buffer

Total Params:

24B

Active Compute:

Dense

Attention Scheme:

GQA (Grouped-Query)

Max Context:

128k tokens

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

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	Ornith 1.5 35B A3B	Mistral Small 3.1 24B
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.

Monthly Queries: 100,000

10k500k1M

Avg Prompt Tokens: 1000

Avg Output Tokens: 300

Pricing Metric	Ornith 1.5 35B A3B	Mistral Small 3.1 24B
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	Mistral Small 3.1 24B
Release Date	8/18/2026	3/17/2025
Routing / MoE	Dense	Dense
Attention Mechanism	Standard / GQA	Standard / GQA
Source Type	Open Weights (MIT)	Open Weights (Open)

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