Google · Efficient
Gemini 3.1 Flash Lite
Google's Gemini 3.1 Flash Lite. Optimized for high-volume, cost-efficient reasoning and multimodal workloads.
- Reasoning
- Tools
- Vision
- Structured output
- PDF input
- Audio input
- Video input
- Input
- Text, Images, Video, Audio
- Output
- Text
- Reasoning effort
- minimal · low · medium · high
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $0.25
- Output
- $1.50
- Cache read
- $0.025
- Cache write
- $0.0833
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 65.5K
- Average output (7 days)
- 669 tokens
- Longer than 59% of 465 models
Side by side
Gemini 3.1 Flash Lite compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Gemini 3.1 Flash LiteGoogleThis model | Kimi K2 0905MoonshotRemove | |
|---|---|---|
| Provider | Moonshot | |
| Discovery tier | Efficient | Open weights |
| Added | Mar 3, 2026 | Sep 25, 2025 |
| Limits | ||
| Context window | 1.05MBest | 262.14K |
| Max output | 65.54K | 100.35KBest |
| Average output | 669 tokens | 677 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.25Best | $0.40 |
| Output | $1.50Best | $1.80 |
| Cache read | $0.025 | — |
| Cache write | $0.0833 | — |
| Performance | ||
| Throughput | — | 51.7 tok/s |
| Time to first token | — | 3.6 s |
| Request success (24h) | 100.0% | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 15.6Best | 15.3 |
| Coding Index | 34.7 | — |
| Math Index | — | 57.3 |
| Capabilities | ||
| Reasoning | Yes | No |
| Tools | Yes | Yes |
| Vision | Yes | No |
| Structured output | Yes | Yes |
| PDF input | Yes | No |
| Audio input | Yes | No |
| Video input | Yes | No |
| Reasoning effort | minimal, low, medium, high | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Gemini 3.1 Flash Lite
- Kimi K2 0905
Intelligence Index
- Gemini 3.1 Flash Lite15.6
- Kimi K2 090515.3
Gemini 3.1 Flash Lite: Better than 39% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Gemini 3.1 Flash Lite: 34.7
- Kimi K2 0905: —
Math Index
Composite of the math evaluations
- Gemini 3.1 Flash Lite: —
- Kimi K2 0905: 57.3
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemini 3.1 Flash Lite: 6.8%
- Kimi K2 0905: —
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Gemini 3.1 Flash Lite: 0.5%
- Kimi K2 0905: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemini 3.1 Flash Lite: 207 Elo
- Kimi K2 0905: —
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemini 3.1 Flash Lite: 430 Elo
- Kimi K2 0905: —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemini 3.1 Flash Lite: 7.8%
- Kimi K2 0905: —
AA-LCRv1.1
Reasoning across long documents
- Gemini 3.1 Flash Lite: 74.3%Top
- Kimi K2 0905: 53.0%
MLCR-AA
Long-context reasoning over medical records
- Gemini 3.1 Flash Lite: 5.0%
- Kimi K2 0905: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemini 3.1 Flash Lite: 17.2%Top
- Kimi K2 0905: 6.4%
GPQA Diamond
Graduate-level science questions
- Gemini 3.1 Flash Lite: 82.2%Top
- Kimi K2 0905: 76.7%
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemini 3.1 Flash Lite: —
- Kimi K2 0905: 81.9%
IFBench
Following precise output instructions
- Gemini 3.1 Flash Lite: 77.2%Top
- Kimi K2 0905: 41.7%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemini 3.1 Flash Lite: 0.5%
- Kimi K2 0905: —
SciCode
Scientific computing in Python
- Gemini 3.1 Flash Lite: 43.4%
- Kimi K2 0905: —
LiveCodeBench
Recent competitive programming problems
- Gemini 3.1 Flash Lite: —
- Kimi K2 0905: 61.0%
Math
AIME 2025
Competition mathematics, 2025 problems
- Gemini 3.1 Flash Lite: —
- Kimi K2 0905: 57.3%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Gemini 3.1 Flash Lite: 24.2%Top
- Kimi K2 0905: 23.5%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Gemini 3.1 Flash Lite: 31.3%
- Kimi K2 0905: 73.4%Top
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemini 3.1 Flash Lite: 74.3%Top
- Kimi K2 0905: 53.0%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- Gemini 3.1 Flash Lite
- Kimi K2 0905
| Leaderboard | Gemini 3.1 Flash Lite | Kimi K2 0905 |
|---|---|---|
| Vision Style Control · Captioning | #7 · 1225.5 | Not in top 20 |
| Vision Style Control · Entity Recognition | #3 · 1271.1 | Not in top 20 |
Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena
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
- 41.0%
- About $0.16 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- 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": "google/gemini-3.1-flash-lite",
"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="google/gemini-3.1-flash-lite",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Related models
Other Google models, starting with the same family.