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Claude Fable 5.1 vs Apertus 70B
Side-by-side technical showdown between Claude Fable 5.1 and Apertus 70B on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.
Executive Showdown & Winner Breakdown
Who Wins Where: Claude Fable 5.1 vs Apertus 70B
Verified across benchmarks, pricing & local VRAM footprintLocal Portability & Sovereignty
Self-HostableLeader:Apertus 70B(Open Weights vs Closed API)
Apertus 70B can be deployed on private GPUs, Ollama, and on-prem clusters without third-party vendor lock-in.
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
Claude Fable 5.1
Zero Local VRAM
Inference runs fully on provider cloud infrastructure. Zero GPU required locally.
Total Params:
Undisclosed
Active Compute:
Dense
Attention Scheme:
GQA / Multi-Head
Max Context:
1000k tokens
Target Hardware: Zero Local VRAM (Managed Cloud API)
Model 2Open Weights
Apertus 70B
44.8GB
Weights: ~38.5 GBKV Cache: ~0.4 GB+15% CUDA Buffer
Total Params:
70B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
66k tokens
Target Hardware: 1x NVIDIA A100 / H100 (80GB)
GPU & Hardware Tier Compatibility Matrix
| Hardware Configuration | Available VRAM | Claude Fable 5.1 | Apertus 70B |
|---|---|---|---|
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 | ✕ OOM |
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB) | 48 GB | Cloud API | Tight (Low Ctx) |
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 |
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
Claude Fable 5.1
96.2
Apertus 70B
—
GPQA Diamond (Hard Reasoning)Score % / Points
Claude Fable 5.1
94.8
Apertus 70B
—
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 | Claude Fable 5.1 | Apertus 70B |
|---|---|---|
| Input Cost (/1M tokens) | $10.00 | Free / Open |
| Cached Input (/1M tokens) | $0.25 | — |
| Output Cost (/1M tokens) | $50.00 | Free / Open |
| Simulated Monthly Bill (100,000 calls) | $2500.00 ~$25.00 / 1k queries | $0 API Cost Open Weights |
| Self-Hosted Breakeven vs $864/mo GPU | Self-hosting cheaper at this volume | Self-Hostable Day 1 |
Architecture Matrix
| Feature | Claude Fable 5.1 | Apertus 70B |
|---|---|---|
| Release Date | 9/1/2026 | 9/2/2025 |
| Routing / MoE | Dense | Dense |
| Attention Mechanism | Standard / GQA | Standard / GQA |
| Source Type | Proprietary Commercial API | Open Weights (Apache-2.0) |
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