LANGUAGE MODEL Google/DeepMind Last updated:

Gemini 2.5 Flash-Lite (Non-reasoning)

Gemini 2.5 Flash-Lite (Non-reasoning) is an API model from Google. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.

Try Gemini API Docs ↗
Intelligence
Below avg
Speed
Top 5%
316 tok/s output
Cost
Low
$0.10 in / $0.40 out
Context
1M
Up to 1,000,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/M out).
Intelligence
  • Top 1%≤ 1%
  • Top 5%≤ 5%
  • Top 10%≤ 10%
  • Good≤ 25%
  • Medium≤ 50%
  • Below avg> 50%
Speed
  • Top 1%≥ 345 tok/s
  • Top 5%≥ 237 tok/s
  • Top 10%≥ 196 tok/s
  • Good≥ 146 tok/s
  • Medium≥ 90 tok/s
  • Slow< 90 tok/s
Cost
  • Freeopen weights · self-host
  • Low< $1 / M out
  • Moderate$1–5 / M out
  • High≥ $5 / M out
Official ↗ Artificial Analysis ↗

Core Capabilities

Long Documents
Handles entire codebases, books, and multi-doc RAG.

Context Window

1M tokens
≈ entire codebase
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M This model 本模型
10M

Availability

API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales

Pricing Model

Pay per token
Input and output billed separately.
Pay-per-token

Capability / Performance

Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).

Lower 20% Upper 80% This model
Reasoning
AA Intelligence Index · scaled to 10
1.7
5.6
3.1
Coding
SciCode · scaled to 10
1.8
4.3
2.9
Agentic tasks
Terminal-Bench Hard · scaled to 10
0.2
3.6
1.3
Context / memory
Context window size · log-scaled
6.0
9.0
9.0
Cost efficiency
Input price ($/M tokens) · cheaper scores higher
6.2
10.0
10.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.

What it feels like

  • Faster/cheaper Flash tier of Gemini 2.5.
  • Long context is the headline — 91.5% on 128k and 83.1% on 1M long-context retrieval, ahead of every other frontier model
  • Independent testers confirm reliable retrieval up to ~800K tokens; the last 200K degrades
  • Genuine 'put a whole codebase or book in the prompt' workflow — not theatre
Reviews: Artificial Analysis — Gemini 2.5 Pro analysis ↗ · Google DeepMind — Gemini 2.5 technical report ↗ · DataCamp — Gemini 2.5 Pro review ↗

Best use cases

  • Whole-codebase analysis, refactors across many files at once
  • Long-form document QA (books, contracts, research papers)
  • Multi-doc RAG replacement when context budget is generous

Tools to try

Gemini app AI Studio Vertex AI Gemini CLI

Not ideal for

  • Pure agentic-coding leaderboards — Claude Opus 4.5 and DeepSeek V4 lead SWE-bench
  • Cost-bound bulk inference past 200K tokens (price doubles)