Which model is better: GPT-6 Astra or Qwen 3.8 27B?
Qwen 3.8 27B is 98.0% cheaper for input tokens ($0.20 vs. $10.00 per 1M tokens) and $0.60 vs. $50.00 for output tokens (50.0x cost advantage). In terms of operational performance, Qwen 3.8 27B delivers lower response latency at 120ms TTFT (620ms faster than GPT-6 Astra) with 140 vs. 74 tok/s throughput. GPT-6 Astra leads coding evaluations at 75.7% SWE-bench Verified vs. 58.4% for Qwen 3.8 27B and 96.0% vs. 68.0% 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
GPT-6 Astra
Qwen 3.8 27B
Net Delta / Advantage
Input Cost / 1M Tokens
$10.00
$0.20
Qwen 3.8 27B is 50.0x cheaper
Output Cost / 1M Tokens
$50.00
$0.60
Qwen 3.8 27B (98.8% lower)
Prompt Cache Read / 1M
$1.000
$0.050
Qwen 3.8 27B is 20.0x cheaper
Prompt Cache Write / 1M
$12.50
$0.20
Qwen 3.8 27B is 62.5x cheaper
Context Window (Max Output)
1,075k (128k out)
131k (16k out)
GPT-6 Astra (8.2x larger context)
Avg TTFT Response Latency
740ms
120ms
Qwen 3.8 27B (620ms faster)
Generation Speed (Throughput)
74 tok/s
140 tok/s
Qwen 3.8 27B (+66 tok/s faster)
SWE-bench Verified (Coding)
75.7%
58.4%
GPT-6 Astra (+17.3% lead)
GPQA Diamond (PhD STEM)
96%
68%
GPT-6 Astra (+28.0% lead)
MMLU-Pro (Multi-Discipline)
91.2%
81.2%
GPT-6 Astra (+10.0% lead)
MATH-500 (Quantitative Logic)
98%
86.4%
GPT-6 Astra (+11.6% lead)
Inference Value Score (IVR)
47.6 / 100
74.9 / 100
Qwen 3.8 27B (+27.3 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).
Qwen 3.8 27B is cheaper at $0.20/1M input tokens ($0.60/1M output) compared to $10.00/1M input ($50.00/1M output) for GPT-6 Astra.
Which model has lower TTFT latency and higher tok/s throughput: GPT-6 Astra or Qwen 3.8 27B?
Qwen 3.8 27B delivers faster first-token latency at 120ms TTFT (140 tok/s throughput) compared to 740ms TTFT (74 tok/s) for GPT-6 Astra.
How do GPT-6 Astra and Qwen 3.8 27B compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-6 Astra scores 75.7% versus 58.4% for Qwen 3.8 27B. On GPQA Diamond graduate-level STEM reasoning, GPT-6 Astra scores 96% versus 68% for Qwen 3.8 27B (and 98% vs 86.4% on MATH-500).
When should you choose GPT-6 Astra?
Choose GPT-6 Astra (OpenAI) for high-autonomy flagship reasoning model engineered for end-to-end computer and browser use, advanced scientific research, and complex multi-turn software engineering — offering a 1,075k context window, 740ms TTFT, and $10.00/1M input pricing ($1.000/1M 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).
