head-to-head

Z.ai: GLM 5.3 Flash (batch) vs Google: Gemini 3.6 Flash (batch)

Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-10-19.

Z.ai: GLM 5.3 Flash (batch) Google: Gemini 3.6 Flash (batch)
Vendorz-aigoogle
Quality Score100100
Benchmark Score69.6-
Input Price$0.05/M$0.38/M
Output Price$0.18/M$1.88/M
Context Window1,048,5761,048,576
Max Output131,07265,536
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio-✓
Benchmark Scores
ai_index69.0-

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, GLM 5.3 Flash (batch) costs $0.095 and Gemini 3.6 Flash (batch) costs $0.84, so GLM 5.3 Flash (batch) is about 8.9 times cheaper for the same work.

Both read up to 1,048,576 tokens at once, roughly 1,573 pages of text.

Only Gemini 3.6 Flash (batch) offers audio input.

Across the 20 tasks where they differ, GLM 5.3 Flash (batch) scores higher on 20 (SQL Generation, Code Review, Code Completion and others).

GLM 5.3 Flash (batch) was released in August 2026 and Gemini 3.6 Flash (batch) in July 2026.

Pick GLM 5.3 Flash (batch) if you need lower cost or the stronger all-round task scores; pick Gemini 3.6 Flash (batch) if you need audio input.

Costs use current list prices per million tokens; page counts assume about 0.75 words per token and 500 words per page.

Who wins by task?

TaskZ.ai: GLM 5.3 Flash (batch)Google: Gemini 3.6 Flash (batch)
SQL Generation 142 133
Code Review 145 132
Code Completion 133 131
Code Refactoring 147 136
Bug Fixing 149 136
Unit Test Generation 132 124
Code Documentation 138 131
Regex Writing 126 119
CI/CD Pipelines 128 120
Frontend Component Design 130 122
Data Analysis 134 124
CSV / Spreadsheet Cleanup 136 133
ETL Scripting 137 128
JSON Extraction 132 131
Bulk Data Labeling 129 129
OCR / Document Parsing 134 131
Table Extraction from PDFs 134 131
Long-Document Summarization 149 137
Short-Form Summarization 127 123
Blog Post Writing 130 121

Scores reflect capability match + benchmark data + pricing for each task. Methodology →

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