Model comparison
Gemini 3.5 Flash-Lite vs Kimi K2.6
Google against Moonshot AI, compared on context, price, and verified benchmark results.
Catalog record checked August 12, 2026; individual provider fields may change.
Gemini 3.5 Flash-Lite
Budget
Moonshot AI
Kimi K2.6
Budget · Open weights
| Specification | Gemini 3.5 Flash-Lite | Kimi K2.6 |
|---|---|---|
| Provider | Moonshot AI | |
| Tier | Budget | Budget |
| Context window | Winner: 1.05M | 262K |
| Max output | 66K | Not verified |
| Input / 1M tokens | Winner: $0.30 | $0.95 |
| Output / 1M tokens | Winner: $2.50 | $4 |
| Weights | Closed | Open |
| Parameters | Not disclosed | 1T total / 32B active (MoE) |
| Reasoning levels | Not verified | Not verified |
| Modalities | text, image, video, audio, pdf | text, image, video |
| Released | July 21, 2026 | April 21, 2026 |
| Artificial Analysis Intelligence Index (2026-08-08) | 37 | Winner: 45 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: Kimi K2.6: Cache-hit input is $0.16 / MTok; cache-miss input is $0.95 / MTok; output $4.00 / MTok (Moonshot list).
Gemini 3.5 Flash-Lite
Google's cheapest current Flash-class API tier for high-volume, latency-sensitive agent loops.
Best for
- High-volume agents
- Document pipelines
- Cost-sensitive multimodal work
Watch out
Cheaper than 3.6 and 3.7 Flash, not stronger on hard reasoning — pick it for throughput, not frontier coding.
Kimi K2.6
Open-weight mixture-of-experts model priced well below the closed frontier tiers.
Best for
- Cost-sensitive volume
- Self-hosting
- Avoiding vendor lock-in
Watch out
262K context is among the smaller windows here — a real constraint on long-document work.
Common questions
Gemini 3.5 Flash-Lite vs Kimi K2.6
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.5 Flash-Lite or Kimi K2.6 cheaper for input?
Gemini 3.5 Flash-Lite is cheaper at $0.30 per million input tokens, against $0.95 for Kimi K2.6 — roughly 3.2× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Kimi K2.6 has tiered pricing: Cache-hit input is $0.16 / MTok; cache-miss input is $0.95 / MTok; output $4.00 / MTok (Moonshot list).
Is Gemini 3.5 Flash-Lite or Kimi K2.6 cheaper for output?
Gemini 3.5 Flash-Lite is cheaper at $2.50 per million output tokens, against $4 for Kimi K2.6 — roughly 1.6× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Kimi K2.6 has tiered pricing: Cache-hit input is $0.16 / MTok; cache-miss input is $0.95 / MTok; output $4.00 / MTok (Moonshot list).
Which has the larger context window, Gemini 3.5 Flash-Lite or Kimi K2.6?
Gemini 3.5 Flash-Lite accepts 1.05M tokens against 262K for Kimi K2.6. This only matters if you routinely send very long documents or large codebases.
Should I use Gemini 3.5 Flash-Lite or Kimi K2.6?
Both sit in the budget tier, so the choice usually comes down to price and context rather than capability. Gemini 3.5 Flash-Lite suits high-volume agents; Kimi K2.6 suits cost-sensitive volume.
Can I self-host Gemini 3.5 Flash-Lite or Kimi K2.6?
Kimi K2.6 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 3.5 Flash-Lite is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.