head-to-head

Qwen: Qwen3.8 27B vs OpenAI: GPT-5.6 Sol

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

Qwen: Qwen3.8 27B OpenAI: GPT-5.6 Sol
Vendorqwenopenai
Quality Score100100
Benchmark Score55.680.2
Input Price$0.42/M$2.00/M
Output Price$3.00/M$10.00/M
Context Window1,000,0001,050,000
Max Output131,072128,000
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio--
Benchmark Scores
ai_index55.677.5

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, Qwen3.8 27B costs $1.17 and GPT-5.6 Sol costs $4.50, so Qwen3.8 27B is about 3.8 times cheaper for the same work.

Both take in a similar amount of text: 1,000,000 and 1,050,000 tokens, about 1,500 and 1,575 pages.

Only Qwen3.8 27B offers video input.

On our blended benchmark score GPT-5.6 Sol leads 80.2 to 55.6.

Across the 19 tasks where they differ, Qwen3.8 27B scores higher on 4 (Code Completion, JSON Extraction, Bulk Data Labeling and others), GPT-5.6 Sol on 15 (SQL Generation, Code Review, Code Refactoring and others).

Qwen3.8 27B was released in August 2026 and GPT-5.6 Sol in July 2026.

Pick Qwen3.8 27B if you need lower cost or video input; pick GPT-5.6 Sol if you need benchmark performance or the stronger all-round task scores.

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?

TaskQwen: Qwen3.8 27BOpenAI: GPT-5.6 Sol
SQL Generation 140 142
Code Review 142 146
Code Completion 132 119
Code Refactoring 144 148
Bug Fixing 146 150
Unit Test Generation 130 133
Code Documentation 136 136
Regex Writing 124 125
CI/CD Pipelines 126 129
Frontend Component Design 128 131
Data Analysis 132 135
ETL Scripting 135 138
JSON Extraction 131 121
Bulk Data Labeling 129 118
OCR / Document Parsing 133 134
Table Extraction from PDFs 133 134
Long-Document Summarization 146 149
Short-Form Summarization 126 117
Blog Post Writing 128 130

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

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