head-to-head

Anthropic: Claude Opus 5.5 (batch) vs Z.ai: GLM 5.3 FlashX

Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-10-13.

Anthropic: Claude Opus 5.5 (batch) Z.ai: GLM 5.3 FlashX
Vendoranthropicz-ai
Quality Score100100
Benchmark Score100.0-
Input Price$2.00/M$0.37/M
Output Price$10.00/M$1.25/M
Context Window1,000,0001,048,576
Max Output128,000131,072
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio--
Benchmark Scores
ai_index95.1-

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, GLM 5.3 FlashX costs $0.68 and Claude Opus 5.5 (batch) costs $4.50, so GLM 5.3 FlashX is about 6.6 times cheaper for the same work.

Both take in a similar amount of text: 1,000,000 and 1,048,576 tokens, about 1,500 and 1,573 pages.

Only GLM 5.3 FlashX offers video input.

Across the 20 tasks where they differ, Claude Opus 5.5 (batch) scores higher on 16 (SQL Generation, Code Review, Code Refactoring and others), GLM 5.3 FlashX on 4 (Code Completion, JSON Extraction, Bulk Data Labeling and others).

Pick Claude Opus 5.5 (batch) if you need the stronger all-round task scores; pick GLM 5.3 FlashX if you need lower cost or video 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?

TaskAnthropic: Claude Opus 5.5 (batch)Z.ai: GLM 5.3 FlashX
SQL Generation 144 133
Code Review 150 132
Code Completion 119 131
Code Refactoring 151 136
Bug Fixing 154 136
Unit Test Generation 135 124
Code Documentation 138 131
Regex Writing 127 119
CI/CD Pipelines 131 120
Frontend Component Design 133 122
Data Analysis 138 124
CSV / Spreadsheet Cleanup 136 133
ETL Scripting 141 128
JSON Extraction 121 131
Bulk Data Labeling 118 129
OCR / Document Parsing 135 131
Table Extraction from PDFs 135 131
Long-Document Summarization 152 137
Short-Form Summarization 118 123
Blog Post Writing 132 121

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

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