head-to-head

inclusionAI: Ling 3.0 Flash vs OpenAI: GPT-5.6 Sol Pro

Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-10-02.

inclusionAI: Ling 3.0 Flash OpenAI: GPT-5.6 Sol Pro
Vendorinclusionaiopenai
Quality Score10098
Benchmark Score30.4-
Input Price$0.02/M$4.00/M
Output Price$0.06/M$20.00/M
Context Window262,1441,050,000
Max Output32,768128,000
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision-✓
Audio--
Benchmark Scores
ai_index33.2-

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, Ling 3.0 Flash costs $0.037 and GPT-5.6 Sol Pro costs $9.00, so Ling 3.0 Flash is about 244.9 times cheaper for the same work.

GPT-5.6 Sol Pro reads up to 1,050,000 tokens at once, roughly 1,575 pages of text, against about 393 pages for Ling 3.0 Flash.

Only GPT-5.6 Sol Pro offers image input.

Across the 20 tasks where they differ, Ling 3.0 Flash scores higher on 16 (SQL Generation, Code Review, Code Completion and others), GPT-5.6 Sol Pro on 4 (Code Refactoring, Frontend Component Design, OCR / Document Parsing and others).

Pick Ling 3.0 Flash if you need lower cost or the stronger all-round task scores; pick GPT-5.6 Sol Pro if you need long documents or image 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?

TaskinclusionAI: Ling 3.0 FlashOpenAI: GPT-5.6 Sol Pro
SQL Generation 135 129
Code Review 132 130
Code Completion 129 113
Code Refactoring 133 134
Bug Fixing 136 134
Unit Test Generation 125 122
Code Documentation 129 125
Regex Writing 123 113
CI/CD Pipelines 121 118
Frontend Component Design 96 120
Data Analysis 129 122
CSV / Spreadsheet Cleanup 129 129
ETL Scripting 127 126
JSON Extraction 132 117
Bulk Data Labeling 130 112
OCR / Document Parsing 84 129
Table Extraction from PDFs 84 129
Long-Document Summarization 135 133
Short-Form Summarization 125 109
Blog Post Writing 123 117

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

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