What is the difference between Qwen3 Coder Next and Ornith 1.5 35B A3B?

Qwen3 Coder Next (Alibaba Cloud) features Open Weights with a 262k context window, while Ornith 1.5 35B A3B (DeepReinforce) features 35B with a 262k context window.

Which is cheaper for production inference, Qwen3 Coder Next or Ornith 1.5 35B A3B?

Qwen3 Coder Next input pricing is Open / Free per 1M tokens vs Ornith 1.5 35B A3B at Open / Free per 1M tokens.

Can I run Qwen3 Coder Next or Ornith 1.5 35B A3B locally on private GPU hardware?

Qwen3 Coder Next is an open-weights model and can be hosted locally using Ollama or vLLM. Ornith 1.5 35B A3B is an open-weights model and can be hosted locally.

Back to LLM Benchmark & Hardware Sizing Engine

Alibaba CloudvsDeepReinforce

Qwen3 Coder Next vs Ornith 1.5 35B A3B

Side-by-side technical showdown between Qwen3 Coder Next and Ornith 1.5 35B A3B on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.

Executive Showdown & Winner Breakdown

Who Wins Where: Qwen3 Coder Next vs Ornith 1.5 35B A3B

Verified across benchmarks, pricing & local VRAM footprint

Context Window Capacity

Context Depth

Leader:Qwen3 Coder Next(262k vs 262k)

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 1Cloud Hosted

Qwen3 Coder Next

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:

262k tokens

Target Hardware: Zero Local VRAM (Managed Cloud API)

Model 2Open 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)

GPU & Hardware Tier Compatibility Matrix

Hardware Configuration	Available VRAM	Qwen3 Coder Next	Ornith 1.5 35B A3B
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB)	16 GB	Cloud API	✕ OOM
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)	24 GB	Cloud API	Tight (Low Ctx)
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB)	48 GB	Cloud API	Optimal
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB)	80 GB	Cloud API	Optimal
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB)	160 GB	Cloud API	Optimal
Multi-Node Cluster 4x–8x H100 Datacenter Cluster	320 GB	Cloud API	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	Qwen3 Coder Next	Ornith 1.5 35B A3B
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	Qwen3 Coder Next	Ornith 1.5 35B A3B
Release Date	2/3/2026	8/18/2026
Routing / MoE	Dense	Dense
Attention Mechanism	Standard / GQA	Standard / GQA
Source Type	Open Weights (Open)	Open Weights (MIT)

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