Which model is better: GPT-6 Astra or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next is 98.4% cheaper for input tokens ($0.16 vs. $10.00 per 1M tokens) and $0.47 vs. $50.00 for output tokens (62.5x cost advantage). In terms of operational performance, Qwen 3.8 Flash Next delivers lower response latency at 280ms TTFT (460ms faster than GPT-6 Astra) with 185 vs. 74 tok/s throughput. Qwen 3.8 Flash Next leads coding evaluations at 81.0% SWE-bench Verified vs. 75.7% for GPT-6 Astra and 91.7% vs. 96.0% 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 Flash Next
Net Delta / Advantage
Input Cost / 1M Tokens
$10.00
$0.16
Qwen 3.8 Flash Next is 62.5x cheaper
Output Cost / 1M Tokens
$50.00
$0.47
Qwen 3.8 Flash Next (99.1% lower)
Prompt Cache Read / 1M
$1.000
$0.016
Qwen 3.8 Flash Next is 62.5x cheaper
Prompt Cache Write / 1M
$12.50
$0.20
Qwen 3.8 Flash Next is 62.5x cheaper
Context Window (Max Output)
1,075k (128k out)
268k (66k out)
GPT-6 Astra (4.0x larger context)
Avg TTFT Response Latency
740ms
280ms
Qwen 3.8 Flash Next (460ms faster)
Generation Speed (Throughput)
74 tok/s
185 tok/s
Qwen 3.8 Flash Next (+111 tok/s faster)
SWE-bench Verified (Coding)
75.7%
81%
Qwen 3.8 Flash Next (+5.3% lead)
GPQA Diamond (PhD STEM)
96%
91.7%
GPT-6 Astra (+4.3% lead)
MMLU-Pro (Multi-Discipline)
91.2%
88.4%
GPT-6 Astra (+2.8% lead)
MATH-500 (Quantitative Logic)
98%
95.7%
GPT-6 Astra (+2.3% lead)
Inference Value Score (IVR)
47.6 / 100
85.1 / 100
Qwen 3.8 Flash Next (+37.5 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Which is cheaper: GPT-6 Astra or Qwen 3.8 Flash Next?
Qwen 3.8 Flash Next is cheaper at $0.16/1M input tokens ($0.47/1M 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 Flash Next?
Qwen 3.8 Flash Next delivers faster first-token latency at 280ms TTFT (185 tok/s throughput) compared to 740ms TTFT (74 tok/s) for GPT-6 Astra.
How do GPT-6 Astra and Qwen 3.8 Flash Next compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GPT-6 Astra scores 75.7% versus 81% for Qwen 3.8 Flash Next. On GPQA Diamond graduate-level STEM reasoning, GPT-6 Astra scores 96% versus 91.7% for Qwen 3.8 Flash Next (and 98% vs 95.7% 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 Flash Next?
Choose Qwen 3.8 Flash Next (Alibaba Cloud) for next-generation open-weight multimodal MoE activating only 6B parameters per token for ultra-low-cost enterprise office automation, tool calling, and multilingual repository engineering — offering a 268k context window, 280ms TTFT, and $0.16/1M input pricing ($0.016/1M cached reads).
