💻 Coding (SWE) WinnerGPT-6.1 Sol 86.4% SWE-bench · 96.8% HumanEval
🧠 STEM Reasoning WinnerGPT-6.1 Sol 92.4% GPQA · 98.8% MATH-500
Which model is better: GLM-5.3-Flash or GPT-6.1 Sol?
GLM-5.3-Flash is 94.0% cheaper for input tokens ($0.12 vs. $2.00 per 1M tokens) and $0.40 vs. $10.00 for output tokens (16.7x cost advantage). In terms of operational performance, GLM-5.3-Flash delivers lower response latency at 90ms TTFT (430ms faster than GPT-6.1 Sol) with 175 vs. 135 tok/s throughput. GPT-6.1 Sol leads coding evaluations at 86.4% SWE-bench Verified vs. 52.8% for GLM-5.3-Flash and 92.4% vs. 63.5% 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
GLM-5.3-Flash
GPT-6.1 Sol
Net Delta / Advantage
Input Cost / 1M Tokens
$0.12
$2.00
GLM-5.3-Flash is 16.7x cheaper
Output Cost / 1M Tokens
$0.40
$10.00
GLM-5.3-Flash (96.0% lower)
Prompt Cache Read / 1M
$0.030
$0.100
GLM-5.3-Flash is 3.3x cheaper
Prompt Cache Write / 1M
$0.12
$2.00
GLM-5.3-Flash is 16.7x cheaper
Context Window (Max Output)
262k (16k out)
1,075k (128k out)
GPT-6.1 Sol (4.1x larger context)
Avg TTFT Response Latency
90ms
520ms
GLM-5.3-Flash (430ms faster)
Generation Speed (Throughput)
175 tok/s
135 tok/s
GLM-5.3-Flash (+40 tok/s faster)
SWE-bench Verified (Coding)
52.8%
86.4%
GPT-6.1 Sol (+33.6% lead)
GPQA Diamond (PhD STEM)
63.5%
92.4%
GPT-6.1 Sol (+28.9% lead)
MMLU-Pro (Multi-Discipline)
78.4%
91%
GPT-6.1 Sol (+12.6% lead)
MATH-500 (Quantitative Logic)
84%
98.8%
GPT-6.1 Sol (+14.8% lead)
Inference Value Score (IVR)
76.7 / 100
62.9 / 100
GLM-5.3-Flash (+13.8 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose GLM-5.3-Flash (Zhipu AI) for sub-100ms ultra-fast inference model designed for real-time customer support, document classification, and agentic workflows — offering a 262k context window, 90ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
Choose GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
GLM-5.3-Flash is cheaper at $0.12/1M input tokens ($0.40/1M output) compared to $2.00/1M input ($10.00/1M output) for GPT-6.1 Sol.
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3-Flash or GPT-6.1 Sol?
GLM-5.3-Flash delivers faster first-token latency at 90ms TTFT (175 tok/s throughput) compared to 520ms TTFT (135 tok/s) for GPT-6.1 Sol.
How do GLM-5.3-Flash and GPT-6.1 Sol compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3-Flash scores 52.8% versus 86.4% for GPT-6.1 Sol. On GPQA Diamond graduate-level STEM reasoning, GLM-5.3-Flash scores 63.5% versus 92.4% for GPT-6.1 Sol (and 84% vs 98.8% on MATH-500).
When should you choose GLM-5.3-Flash?
Choose GLM-5.3-Flash (Zhipu AI) for sub-100ms ultra-fast inference model designed for real-time customer support, document classification, and agentic workflows — offering a 262k context window, 90ms TTFT, and $0.12/1M input pricing ($0.030/1M cached reads).
When should you choose GPT-6.1 Sol?
Choose GPT-6.1 Sol (OpenAI) for delivers near-GPT-6 Astra frontier accuracy at one-fifth the token cost with a 95% cache read discount for high-efficiency agentic coding, computer use, and enterprise workflow automation — offering a 1,075k context window, 520ms TTFT, and $2.00/1M input pricing ($0.100/1M cached reads).
