What is the difference between Ornith 1.5 35B A3B and Step 3.5 Flash 2603?

Ornith 1.5 35B A3B (DeepReinforce) features 35B with a 262k context window, while Step 3.5 Flash 2603 (Stepfun) features Open Weights with a 256k context window.

Which is cheaper for production inference, Ornith 1.5 35B A3B or Step 3.5 Flash 2603?

Ornith 1.5 35B A3B input pricing is Open / Free per 1M tokens vs Step 3.5 Flash 2603 at Open / Free per 1M tokens.

Can I run Ornith 1.5 35B A3B or Step 3.5 Flash 2603 locally on private GPU hardware?

Ornith 1.5 35B A3B is an open-weights model and can be hosted locally using Ollama or vLLM. Step 3.5 Flash 2603 is an open-weights model and can be hosted locally.

Back to LLM Benchmark & Hardware Sizing Engine

DeepReinforcevsStepfun

Ornith 1.5 35B A3B vs Step 3.5 Flash 2603

Side-by-side technical showdown between Ornith 1.5 35B A3B and Step 3.5 Flash 2603 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 Step 3.5 Flash 2603

Verified across benchmarks, pricing & local VRAM footprint

Context Window Capacity

Context Depth

Leader:Ornith 1.5 35B A3B(262k vs 256k)

Accommodates larger single-turn document ingestions and extensive conversation histories.

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 2Cloud Hosted

Step 3.5 Flash 2603

Zero Local VRAM

Inference runs fully on provider cloud infrastructure. Zero GPU required locally.

Total Params:

Open Weights

Active Compute:

Dense

Attention Scheme:

GQA / Multi-Head

Max Context:

256k tokens

Target Hardware: Zero Local VRAM (Managed Cloud API)

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	Ornith 1.5 35B A3B	Step 3.5 Flash 2603
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB)	16 GB	✕ OOM	Cloud API
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)	24 GB	Tight (Low Ctx)	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

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	Step 3.5 Flash 2603
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	Step 3.5 Flash 2603
Release Date	8/18/2026	4/2/2026
Routing / MoE	Dense	Dense
Attention Mechanism	Standard / GQA	Standard / GQA
Source Type	Open Weights (MIT)	Open Weights (Open)

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