Which model is better: Llama 3.3 70B Instruct or Qwen 3.8 27B?
Llama 3.3 70B Instruct is 40.0% cheaper for input tokens ($0.12 vs. $0.20 per 1M tokens) and $0.30 vs. $0.60 for output tokens (1.7x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (25ms faster than Llama 3.3 70B Instruct) with 140 vs. 165 tok/s throughput. Qwen 3.8 27B leads coding evaluations at 58.4% SWE-bench Verified vs. 42.5% for Llama 3.3 70B Instruct and 68.0% 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
Llama 3.3 70B Instruct
Qwen 3.8 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$0.12
$0.20
Llama 3.3 70B Instruct is 1.7x cheaper
Output Cost / 1M Tokens
$0.30
$0.60
Llama 3.3 70B Instruct (50.0% lower)
Prompt Cache Read / 1M
$0.030
$0.050
Llama 3.3 70B Instruct is 1.7x cheaper
Prompt Cache Write / 1M
$0.12
$0.20
Llama 3.3 70B Instruct is 1.7x cheaper
Context Window (Max Output)
131k (33k out)
131k (16k out)
Equal Context Window
Avg TTFT Response Latency
145ms
120ms
Qwen 3.8 27B (25ms faster)
Generation Speed (Throughput)
165 tok/s
140 tok/s
Llama 3.3 70B Instruct (+25 tok/s faster)
SWE-bench Verified (Coding)
42.5%
58.4%
Qwen 3.8 27B (+15.9% lead)
GPQA Diamond (PhD STEM)
55.6%
68%
Qwen 3.8 27B (+12.4% lead)
MMLU-Pro (Multi-Discipline)
76.8%
81.2%
Qwen 3.8 27B (+4.4% lead)
MATH-500 (Quantitative Logic)
77%
86.4%
Qwen 3.8 27B (+9.4% lead)
Inference Value Score (IVR)
74.6 / 100
74.9 / 100
Qwen 3.8 27B (+0.3 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
Which is cheaper: Llama 3.3 70B Instruct or Qwen 3.8 27B?
Llama 3.3 70B Instruct is cheaper at $0.12/1M input tokens ($0.30/1M output) compared to $0.20/1M input ($0.60/1M output) for Qwen 3.8 27B.
Which model has lower TTFT latency and higher tok/s throughput: Llama 3.3 70B Instruct or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 145ms TTFT (165 tok/s) for Llama 3.3 70B Instruct.
How do Llama 3.3 70B Instruct and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Llama 3.3 70B Instruct scores 42.5% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, Llama 3.3 70B Instruct scores 55.6% versus 68% for Qwen 3.8 27B (and 77% vs 86.4% on MATH-500).
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).
When should you choose Qwen 3.8 27B?
Choose Qwen 3.8 27B (Alibaba Cloud) for high-performance 27B open weights optimized for local deployment, code generation, and low-latency production pipelines — offering a 131k context window, 120ms TTFT, and $0.20/1M input pricing ($0.050/1M cached reads).
