head-to-head

Google: Gemini 3.1 Flash Lite vs MoonshotAI: Kimi K2.6

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

Google: Gemini 3.1 Flash Lite MoonshotAI: Kimi K2.6
Vendorgooglemoonshotai
Quality Score100100
Benchmark Score-76.7
Input Price$0.25/M$0.65/M
Output Price$1.50/M$3.41/M
Context Window1,048,576262,144
Max Output65,536235,929
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio✓-
Benchmark Scores
ai_index-73.0
ai_index_agentic-49.9
ai_index_coding-100.0
eqbench-63.5

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, Gemini 3.1 Flash Lite costs $0.62 and Kimi K2.6 costs $1.50, so Gemini 3.1 Flash Lite is about 2.4 times cheaper for the same work.

Gemini 3.1 Flash Lite reads up to 1,048,576 tokens at once, roughly 1,573 pages of text, against about 393 pages for Kimi K2.6.

Only Gemini 3.1 Flash Lite offers audio input and video input.

Across the 20 tasks where they differ, Kimi K2.6 on 20 (SQL Generation, Code Review, Code Completion and others).

Gemini 3.1 Flash Lite was released in May 2026 and Kimi K2.6 in April 2026.

Pick Gemini 3.1 Flash Lite if you need lower cost, long documents, audio input or video input; pick Kimi K2.6 if you need 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?

TaskGoogle: Gemini 3.1 Flash LiteMoonshotAI: Kimi K2.6
SQL Generation 133 168
Code Review 132 164
Code Completion 131 131
Code Refactoring 136 160
Bug Fixing 136 174
Unit Test Generation 124 152
Code Documentation 131 140
Regex Writing 119 136
CI/CD Pipelines 120 142
Frontend Component Design 122 145
Data Analysis 124 166
CSV / Spreadsheet Cleanup 133 151
ETL Scripting 128 151
JSON Extraction 131 147
Bulk Data Labeling 129 134
OCR / Document Parsing 131 144
Table Extraction from PDFs 131 144
Long-Document Summarization 137 156
Short-Form Summarization 123 131
Blog Post Writing 121 139

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

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