Which model is better: Claude Opus 5.5 or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is 97.0% cheaper for input tokens ($0.12 vs. $4.00 per 1M tokens) and $0.30 vs. $20.00 for output tokens (33.3x cost advantage). In terms of operational performance, Llama 3.3 70B Instruct delivers lower response latency at 145ms TTFT (535ms faster than Claude Opus 5.5) with 165 vs. 95 tok/s throughput. Claude Opus 5.5 leads coding evaluations at 89.9% SWE-bench Verified vs. 42.5% for Llama 3.3 70B Instruct and 93.2% vs. 55.6% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
Claude Opus 5.5
Llama 3.3 70B Instruct
Net Delta / Advantage
Input Cost / 1M Tokens
$4.00
$0.12
Llama 3.3 70B Instruct is 33.3x cheaper
Output Cost / 1M Tokens
$20.00
$0.30
Llama 3.3 70B Instruct (98.5% lower)
Prompt Cache Read / 1M
$0.200
$0.030
Llama 3.3 70B Instruct is 6.7x cheaper
Prompt Cache Write / 1M
$5.00
$0.12
Llama 3.3 70B Instruct is 41.7x cheaper
Context Window (Max Output)
1,024k (128k out)
131k (33k out)
Claude Opus 5.5 (7.8x larger context)
Avg TTFT Response Latency
680ms
145ms
Llama 3.3 70B Instruct (535ms faster)
Generation Speed (Throughput)
95 tok/s
165 tok/s
Llama 3.3 70B Instruct (+70 tok/s faster)
SWE-bench Verified (Coding)
89.9%
42.5%
Claude Opus 5.5 (+47.4% lead)
GPQA Diamond (PhD STEM)
93.2%
55.6%
Claude Opus 5.5 (+37.6% lead)
MMLU-Pro (Multi-Discipline)
92.1%
76.8%
Claude Opus 5.5 (+15.3% lead)
MATH-500 (Quantitative Logic)
98%
77%
Claude Opus 5.5 (+21.0% lead)
Inference Value Score (IVR)
57.8 / 100
74.6 / 100
Llama 3.3 70B Instruct (+16.8 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Claude Opus 5.5 (Anthropic) for top-ranked frontier intelligence leader for autonomous multi-step agentic coding, repository-scale migrations, and complex scientific reasoning at a 40% lower workload cost than Opus 5 — offering a 1,024k context window, 680ms TTFT, and $4.00/1M input pricing ($0.200/1M cached reads).
Which is cheaper: Claude Opus 5.5 or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct is cheaper at $0.12/1M input tokens ($0.30/1M output) compared to $4.00/1M input ($20.00/1M output) for Claude Opus 5.5.
Which model has lower TTFT latency and higher tok/s throughput: Claude Opus 5.5 or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct delivers faster first-token latency at 145ms TTFT (165 tok/s throughput) compared to 680ms TTFT (95 tok/s) for Claude Opus 5.5.
How do Claude Opus 5.5 and Llama 3.3 70B Instruct compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Claude Opus 5.5 scores 89.9% versus 42.5% for Llama 3.3 70B Instruct. On GPQA Diamond graduate-level STEM reasoning, Claude Opus 5.5 scores 93.2% versus 55.6% for Llama 3.3 70B Instruct (and 98% vs 77% on MATH-500).
When should you choose Claude Opus 5.5?
Choose Claude Opus 5.5 (Anthropic) for top-ranked frontier intelligence leader for autonomous multi-step agentic coding, repository-scale migrations, and complex scientific reasoning at a 40% lower workload cost than Opus 5 — offering a 1,024k context window, 680ms TTFT, and $4.00/1M input pricing ($0.200/1M cached reads).
When should you choose Llama 3.3 70B Instruct?
Choose Llama 3.3 70B Instruct (Meta) for cost-optimized on-premise deployments, fast text transformation, structured entity extraction, and enterprise dialogue pipelines — offering a 131k context window, 145ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
