Which model is better: GLM-5.3 or Mistral Large 2 (2411)?
GLM-5.3 is 82.5% cheaper for input tokens ($0.35 vs. $2.00 per 1M tokens) and $1.00 vs. $6.00 for output tokens (5.7x cost advantage). In terms of operational performance, GLM-5.3 delivers lower response latency at 160ms TTFT (220ms faster than Mistral Large 2 (2411)) with 120 vs. 75 tok/s throughput. GLM-5.3 leads coding evaluations at 56.0% SWE-bench Verified vs. 45.2% for Mistral Large 2 (2411) and 67.0% vs. 54.2% 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
Mistral Large 2 (2411)
Net Delta / Advantage
Input Cost / 1M Tokens
$0.35
$2.00
GLM-5.3 is 5.7x cheaper
Output Cost / 1M Tokens
$1.00
$6.00
GLM-5.3 (83.3% lower)
Prompt Cache Read / 1M
$0.090
$0.200
GLM-5.3 is 2.2x cheaper
Prompt Cache Write / 1M
$0.35
$2.00
GLM-5.3 is 5.7x cheaper
Context Window (Max Output)
131k (16k out)
131k (8k out)
Equal Context Window
Avg TTFT Response Latency
160ms
380ms
GLM-5.3 (220ms faster)
Generation Speed (Throughput)
120 tok/s
75 tok/s
GLM-5.3 (+45 tok/s faster)
SWE-bench Verified (Coding)
56%
45.2%
GLM-5.3 (+10.8% lead)
GPQA Diamond (PhD STEM)
67%
54.2%
GLM-5.3 (+12.8% lead)
MMLU-Pro (Multi-Discipline)
80.5%
74.8%
GLM-5.3 (+5.7% lead)
MATH-500 (Quantitative Logic)
88%
76.5%
GLM-5.3 (+11.5% lead)
Inference Value Score (IVR)
69.9 / 100
50.1 / 100
GLM-5.3 (+19.8 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 (Zhipu AI) for bilingual flagship foundation model optimized for cross-lingual reasoning, long-form structured analysis, and enterprise tool execution — offering a 131k context window, 160ms TTFT, and $0.35/1M input pricing ($0.090/1M cached reads).
Choose Mistral Large 2 (2411) (Mistral AI) for sovereign enterprise multilingual flagship tuned for reasoning, strict JSON schemas, and European regulatory compliance — offering a 131k context window, 380ms TTFT, and $2.00/1M input pricing ($0.200/1M cached reads).
Which is cheaper: GLM-5.3 or Mistral Large 2 (2411)?
GLM-5.3 is cheaper at $0.35/1M input tokens ($1.00/1M output) compared to $2.00/1M input ($6.00/1M output) for Mistral Large 2 (2411).
Which model has lower TTFT latency and higher tok/s throughput: GLM-5.3 or Mistral Large 2 (2411)?
GLM-5.3 delivers faster first-token latency at 160ms TTFT (120 tok/s throughput) compared to 380ms TTFT (75 tok/s) for Mistral Large 2 (2411).
How do GLM-5.3 and Mistral Large 2 (2411) compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, GLM-5.3 scores 56% versus 45.2% for Mistral Large 2 (2411). On GPQA Diamond graduate-level STEM reasoning, GLM-5.3 scores 67% versus 54.2% for Mistral Large 2 (2411) (and 88% vs 76.5% on MATH-500).
When should you choose GLM-5.3?
Choose GLM-5.3 (Zhipu AI) for bilingual flagship foundation model optimized for cross-lingual reasoning, long-form structured analysis, and enterprise tool execution — offering a 131k context window, 160ms TTFT, and $0.35/1M input pricing ($0.090/1M cached reads).
When should you choose Mistral Large 2 (2411)?
Choose Mistral Large 2 (2411) (Mistral AI) for sovereign enterprise multilingual flagship tuned for reasoning, strict JSON schemas, and European regulatory compliance — offering a 131k context window, 380ms TTFT, and $2.00/1M input pricing ($0.200/1M cached reads).
