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Qwen3.8-Flash-Next vs GPT OSS 120B

Side-by-side technical showdown between Qwen3.8-Flash-Next and GPT OSS 120B on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.

Executive Showdown & Winner Breakdown

Who Wins Where: Qwen3.8-Flash-Next vs GPT OSS 120B

Verified across benchmarks, pricing & local VRAM footprint
Reasoning & STEM Intelligence
+9.4% lead
Leader:GPT OSS 120B(73.2 vs 80.1)

GPT OSS 120B outperforms in advanced scientific and math problem-solving benchmarks.

Software Engineering & Code
+6.3% lead
Leader:GPT OSS 120B(58.7 vs 62.4)

GPT OSS 120B demonstrates higher code generation accuracy and agentic bug resolution.

VRAM Efficiency (INT4)
37 GB lighter
Leader:GPT OSS 120B(111.4 GB vs 74 GB)

GPT OSS 120B 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

Qwen3.8-Flash-Next

111.4GB
Weights: ~96.8 GBKV Cache: ~0 GB+15% CUDA Buffer
Total Params:
176B
Active Compute:
6B (Sparse MoE)
Attention Scheme:
GQA (Grouped-Query)
Max Context:
262k tokens
Target Hardware: 2x H100 (80GB, TP=2) or 4x L40S (TP=2)
Model 2Open Weights

GPT OSS 120B

74GB
Weights: ~64.4 GBKV Cache: ~0 GB+15% CUDA Buffer
Total Params:
117B
Active Compute:
5.1B (Sparse MoE)
Attention Scheme:
GQA (Grouped-Query)
Max Context:
131k tokens
Target Hardware: 2x H100 (80GB, TP=2) or 4x L40S (TP=2)
GPU & Hardware Tier Compatibility Matrix
Hardware ConfigurationAvailable VRAMQwen3.8-Flash-NextGPT OSS 120B
16 GB VRAM
RTX 4070 / 4080 (16GB), Mac M-Series (16GB)
16 GB✕ OOM✕ OOM
24 GB VRAM
1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)
24 GB✕ OOM✕ OOM
48 GB VRAM
2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB)
48 GB✕ OOM✕ OOM
80 GB VRAM
1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB)
80 GB✕ OOM Tight (Low Ctx)
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
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
Qwen3.8-Flash-Next
58.7
GPT OSS 120B
+3.7 pts62.4
GPQA Diamond (Hard Reasoning)Score % / Points
Qwen3.8-Flash-Next
—
GPT OSS 120B
80.1
MATH-500 (Mathematics)Score % / Points
Qwen3.8-Flash-Next
—
GPT OSS 120B
95.8
MMLU-Pro (Multitask Knowledge)Score % / Points
Qwen3.8-Flash-Next
73.2
GPT OSS 120B
+16.8 pts90.0
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 MetricQwen3.8-Flash-NextGPT OSS 120B
Input Cost (/1M tokens)$0.15Free / Open
Cached Input (/1M tokens)——
Output Cost (/1M tokens)$0.60Free / Open
Simulated Monthly Bill (100,000 calls)
$33.00
~$0.33 / 1k queries
$0 API Cost
Open Weights
Self-Hosted Breakeven vs $864/mo GPUCloud API is more cost effectiveSelf-Hostable Day 1

Architecture Matrix

FeatureQwen3.8-Flash-NextGPT OSS 120B
Release Date8/26/20268/5/2025
Routing / MoESparse MoESparse MoE
Attention MechanismStandard / GQAStandard / GQA
Source TypeOpen Weights (Qwen License)Open Weights (Open)
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