Google · Efficient
Gemini 2.5 Flash Lite Preview
Ultra-lightweight and fast variant of Gemini 2.5 Flash. Preview release optimized for speed and efficiency.
- Reasoning
- Vision
- Audio input
- Input
- Text, Images, Audio
- Output
- Text
- Reasoning effort
- low · medium · high
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $0.15
- Output
- $0.60
- Cache read
- $0.015
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 65.5K
Side by side
Gemini 2.5 Flash Lite Preview compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Gemini 2.5 Flash Lite PreviewGoogleThis model | Gemini Flash Lite LatestGoogleRemove | |
|---|---|---|
| Provider | ||
| Discovery tier | Efficient | Efficient |
| Added | Jun 17, 2025 | Mar 29, 2026 |
| Limits | ||
| Context window | 1.05M | 1.05M |
| Max output | 65.54K | 65.54K |
| Pricing, USD per 1M tokens | ||
| Input | $0.15Best | $0.30 |
| Output | $0.60Best | $2.50 |
| Cache read | $0.015Best | $0.03 |
| Cache write | — | $0.0833 |
| Performance | ||
| Request success (24h) | 99.5% | 99.8%Best |
| Benchmark indexes | ||
| Intelligence Index | 6.7 | 22.2Best |
| Coding Index | — | 49.3 |
| Math Index | 35.3 | — |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Vision | Yes | Yes |
| Structured output | No | Yes |
| PDF input | No | Yes |
| Audio input | Yes | Yes |
| Video input | No | Yes |
| Reasoning effort | low, medium, high | minimal, low, medium, high |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Gemini 2.5 Flash Lite Preview
- Gemini Flash Lite Latest
Intelligence Index
- Gemini 2.5 Flash Lite Preview6.7
- Gemini Flash Lite Latest22.2
Gemini 2.5 Flash Lite Preview: Better than 6% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 49.3
Math Index
Composite of the math evaluations
- Gemini 2.5 Flash Lite Preview: 35.3
- Gemini Flash Lite Latest: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 25.0%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 3.3%
Harvey LAB-AAv1.1
Legal tasks passed in full, with no material hallucination
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 0.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 650 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 985 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 13.6%
AA-LCRv1.1
Reasoning across long documents
- Gemini 2.5 Flash Lite Preview: 32.0%
- Gemini Flash Lite Latest: 76.0%Top
MLCR-AA
Long-context reasoning over medical records
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 10.6%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemini 2.5 Flash Lite Preview: 3.7%
- Gemini Flash Lite Latest: 18.8%Top
GPQA Diamond
Graduate-level science questions
- Gemini 2.5 Flash Lite Preview: 47.4%
- Gemini Flash Lite Latest: 83.8%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemini 2.5 Flash Lite Preview: 72.4%
- Gemini Flash Lite Latest: —
IFBench
Following precise output instructions
- Gemini 2.5 Flash Lite Preview: 31.5%
- Gemini Flash Lite Latest: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 1.0%
SciCode
Scientific computing in Python
- Gemini 2.5 Flash Lite Preview: —
- Gemini Flash Lite Latest: 41.3%
LiveCodeBench
Recent competitive programming problems
- Gemini 2.5 Flash Lite Preview: 40.0%
- Gemini Flash Lite Latest: —
Math
AIME 2025
Competition mathematics, 2025 problems
- Gemini 2.5 Flash Lite Preview: 35.3%
- Gemini Flash Lite Latest: —
AIME
Competition mathematics
- Gemini 2.5 Flash Lite Preview: 50.0%
- Gemini Flash Lite Latest: —
MATH-500
Competition math problems
- Gemini 2.5 Flash Lite Preview: 92.6%
- Gemini Flash Lite Latest: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Gemini 2.5 Flash Lite Preview: 2.3%
- Gemini Flash Lite Latest: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Gemini 2.5 Flash Lite Preview: 19.0%
- Gemini Flash Lite Latest: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemini 2.5 Flash Lite Preview: 32.0%
- Gemini Flash Lite Latest: 76.0%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Prompt cache hits
- 0.0%
- About $0.15 per 1M input tokens with caching
- Request success (24h)
- 99.5%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-2.5-flash-lite-preview-06-17",
"reasoning_effort": "medium",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="gemini-2.5-flash-lite-preview-06-17",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Related models
Other Google models, starting with the same family.