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Claude Opus 4 (latest) vs GPT-5.5
Side-by-side technical showdown between Claude Opus 4 (latest) and GPT-5.5 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 Opus 4 (latest) vs GPT-5.5
Verified across benchmarks, pricing & local VRAM footprintAPI Cost & Token Economics
11% cheaperLeader:Claude Opus 4 (latest)($5.00 vs $5.00 / 1M in)
Claude Opus 4 (latest) delivers significantly lower input/output token pricing for high-throughput production.
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 Opus 4 (latest)
Zero Local VRAM
Inference runs fully on provider cloud infrastructure. Zero GPU required locally.
Total Params:
Proprietary
Active Compute:
Dense
Attention Scheme:
GQA / Multi-Head
Max Context:
200k tokens
Target Hardware: Zero Local VRAM (Managed Cloud API)
Model 2Cloud Hosted
GPT-5.5
Zero Local VRAM
Inference runs fully on provider cloud infrastructure. Zero GPU required locally.
Total Params:
Proprietary
Active Compute:
Dense
Attention Scheme:
GQA / Multi-Head
Max Context:
1050k tokens
Target Hardware: Zero Local VRAM (Managed Cloud API)
GPU & Hardware Tier Compatibility Matrix
| Hardware Configuration | Available VRAM | Claude Opus 4 (latest) | GPT-5.5 |
|---|---|---|---|
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB) | 16 GB | Cloud API | Cloud API |
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB) | 24 GB | Cloud API | Cloud API |
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB) | 48 GB | Cloud API | Cloud API |
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB) | 80 GB | Cloud API | Cloud API |
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB) | 160 GB | Cloud API | Cloud API |
Multi-Node Cluster 4x–8x H100 Datacenter Cluster | 320 GB | Cloud API | Cloud API |
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 Opus 4 (latest)
—
GPT-5.5
58.6
GPQA Diamond (Hard Reasoning)Score % / Points
Claude Opus 4 (latest)
—
GPT-5.5
93.6
MATH-500 (Mathematics)Score % / Points
Claude Opus 4 (latest)
—
GPT-5.5
51.7
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 Opus 4 (latest) | GPT-5.5 |
|---|---|---|
| Input Cost (/1M tokens) | $5.00 | $5.00 |
| Cached Input (/1M tokens) | — | — |
| Output Cost (/1M tokens) | $25.00 | $30.00 |
| Simulated Monthly Bill (100,000 calls) | $1250.00 ~$12.50 / 1k queries | $1400.00 ~$14.00 / 1k queries |
| Self-Hosted Breakeven vs $864/mo GPU | Self-hosting cheaper at this volume | Self-hosting cheaper at this volume |
Architecture Matrix
| Feature | Claude Opus 4 (latest) | GPT-5.5 |
|---|---|---|
| Release Date | 5/22/2025 | 4/23/2026 |
| Routing / MoE | Dense | Dense |
| Attention Mechanism | Standard / GQA | Standard / GQA |
| Source Type | Proprietary Commercial API | Proprietary Commercial API |
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