head-to-head

OpenAI: GPT-5.6 Terra Pro vs StepFun: Step 3.7 Flash

Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-11-07.

OpenAI: GPT-5.6 Terra Pro StepFun: Step 3.7 Flash
Vendoropenaistepfun
Quality Score100100
Benchmark Score-50.8
Input Price$2.00/M$0.20/M
Output Price$12.00/M$1.15/M
Context Window1,050,000262,144
Max Output128,000230,400
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio--
Benchmark Scores
ai_index-49.9
ai_index_agentic-35.5
ai_index_coding-65.3

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, Step 3.7 Flash costs $0.49 and GPT-5.6 Terra Pro costs $5.00, so Step 3.7 Flash is about 10.3 times cheaper for the same work.

GPT-5.6 Terra Pro reads up to 1,050,000 tokens at once, roughly 1,575 pages of text, against about 393 pages for Step 3.7 Flash.

Only Step 3.7 Flash offers video input.

Across the 20 tasks where they differ, Step 3.7 Flash on 20 (SQL Generation, Code Review, Code Completion and others).

GPT-5.6 Terra Pro was released in July 2026 and Step 3.7 Flash in May 2026.

Pick GPT-5.6 Terra Pro if you need long documents; pick Step 3.7 Flash if you need lower cost, video input 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?

TaskOpenAI: GPT-5.6 Terra ProStepFun: Step 3.7 Flash
SQL Generation 132 153
Code Review 132 146
Code Completion 117 130
Code Refactoring 136 144
Bug Fixing 136 155
Unit Test Generation 124 139
Code Documentation 129 133
Regex Writing 117 129
CI/CD Pipelines 120 131
Frontend Component Design 122 136
Data Analysis 124 150
CSV / Spreadsheet Cleanup 132 141
ETL Scripting 128 137
JSON Extraction 121 142
Bulk Data Labeling 117 133
OCR / Document Parsing 131 138
Table Extraction from PDFs 131 138
Long-Document Summarization 136 142
Short-Form Summarization 113 128
Blog Post Writing 120 129

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

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