Gemini 3.1 Flash-Lite is among the least intelligent models and somewhat expensive when comparing to other models of similar price. It's also notably quick per task and highly concise. The model supports text, image, speech, and video input, outputs text, and has a 1M tokens context window with knowledge up to January 2025.

Gemini 3.1 Flash-Lite scores 6 on the Artificial Analysis Intelligence Index, placing it at the lower end among comparable models (median: 24). When evaluating the Intelligence Index, it generated 32.4M tokens, which is very concise in comparison to the median of 107M.

Pricing for Gemini 3.1 Flash-Lite is $0.25 per 1M input tokens (somewhat expensive, median: $0.24) and $1.50 per 1M output tokens (expensive, median: $0.83). On average, it costs $0.19 per task to evaluate Gemini 3.1 Flash-Lite on the Intelligence Index.

At 0.9 minutes per Intelligence Index task, Gemini 3.1 Flash-Lite is notably quick compared with the median of 7.4 minutes for comparable models (lower is better).

Reasoning	Yes
Input modality	Supports: text, image, speech, and video
Output modality	Supports: text
Knowledge cutoff	Jan 1, 2025
Context window	1M ~1500 A4 pages of size 12 Arial font

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

No publicly available API

Time per Task

Inference time (minutes) per Intelligence Index task · Lower is better

No publicly available API

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

No publicly available API

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Finance & Accounting Index

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

Strategy & Ops Index

Incorporates 4 evaluations: AutomationBench-AA, AA-Briefcase v1.1, AA-Omniscience v1.1, GDP.pdf · Higher is better

Legal Index

Incorporates 5 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, HLE-Diamond, GDP.pdf, AutomationBench-AA · Higher is better

Healthcare & Medical Index

Incorporates 5 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, MLCR-AA, AutomationBench-AA, HLE-Diamond · Higher is better

Engineering Index

Incorporates 7 evaluations: AA-Omniscience v1.1, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1, HLE-Diamond, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Briefcase v1.1 · Higher is better

Economics Index

Incorporates 4 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, HLE-Diamond, GDP.pdf · Higher is better

Benchmarks

Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode-AA 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

EnterpriseOps-Gym-AA

Agentic business operations

GDPval-AA v2.1

Agentic real-world work tasks, Elo

MLCR-AAUpdated

Medical long context reasoning

MMMU-Pro

Visual reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Performance

Measured by time per task (minutes), output speed (tokens per second), time to first answer token and time to first token (seconds)

Time per Intelligence Index Task

Output tokens divided by end-to-end output speed (minutes per Intelligence Index task) · Speed at the model's own endpoint or the median across providers · Lower is better

The weighted average time (minutes) per Artificial Analysis Intelligence Index task: output tokens per task divided by output speed, weighted by each benchmark's weight in the Intelligence Index.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

Output Speed

Output tokens divided by total request time, including time to first token · Higher is better

Output tokens, including reasoning tokens, received per second over a request's full duration, from sending the request to receiving the final token. The headline value averages each Performance v3 workload over the past 5 days, then averages the workloads.

Time to First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Token Use

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Further details

								Weights
Gemini 3.1 Flash-Lite	Google	6	-	1M	$0.22	0.9	305	Not available

Frequently Asked Questions

Common questions about Gemini 3.1 Flash-Lite

Gemini 3.1 Flash-Lite was released on March 3, 2026.

Gemini 3.1 Flash-Lite was created by Google.

Gemini 3.1 Flash-Lite scores 6 on the Artificial Analysis Intelligence Index, placing it at the lower end among other models in a similar price tier (median: 24).

Gemini 3.1 Flash-Lite takes 0.9 minutes per Intelligence Index task (based on Google's API), which is among the quickest compared to other models in a similar price tier (median: 7.4 minutes; lower is better).

Gemini 3.1 Flash-Lite has a time to first answer token of 4.08s (based on Google's API), which is better than average compared to other models in a similar price tier (median: 8.81s).

Gemini 3.1 Flash-Lite costs $0.25 per 1M input tokens (somewhat higher than average, median: $0.24) and $1.50 per 1M output tokens (at the higher end, median: $0.83), based on Google's API.

Gemini 3.1 Flash-Lite costs $0.25 per 1M input tokens and $1.50 per 1M output tokens (based on Google's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.22 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Gemini 3.1 Flash-Lite generated 32.4M output tokens, which is very competitive compared to other models in a similar price tier (median: 107M).

Yes, Gemini 3.1 Flash-Lite is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Gemini 3.1 Flash-Lite supports text, image, speech, and video input.

Gemini 3.1 Flash-Lite supports text output.

Yes, Gemini 3.1 Flash-Lite supports image input and can analyze, describe, and answer questions about images.

Yes, Gemini 3.1 Flash-Lite is multimodal. It can process text, image, speech, and video input and generate text output.

Gemini 3.1 Flash-Lite has a context window of 1M tokens. This determines how much text and conversation history the model can process in a single request.

No, Gemini 3.1 Flash-Lite is proprietary. The model weights are not publicly available.

Gemini 3.1 Flash-Lite is a proprietary model and Google has not disclosed the model size or parameter count.

Gemini 3.1 Flash-Lite achieves a score of 6 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.

Gemini 3.1 Flash-Lite has a knowledge cutoff of January 2025. The model's training data includes information up to this date.

Yes, Gemini 3.1 Flash-Lite is available via API through 1 provider. Compare API providers

Gemini 3.1 Flash-Lite is available through 1 API provider. Compare providers

Gemini 3.1 Flash-Lite: Intelligence, Performance & Price Analysis

Model summary

IntelligenceUpdated

Technical specifications

164 models in this class

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Capability Indexes

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Performance

Time per Intelligence Index Task

Time per Intelligence Index Task

Time Period

Output Speed

End-to-End Output Speed

Time to First Answer Token

Time to First Answer Token

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Context Window

Context Window

Context Window for RAG

Context Window

Further details

More from Google

Frequently Asked Questions

When was Gemini 3.1 Flash-Lite released?

Who created Gemini 3.1 Flash-Lite?

How intelligent is Gemini 3.1 Flash-Lite?

How fast is Gemini 3.1 Flash-Lite?

What is the latency of Gemini 3.1 Flash-Lite?

How much does Gemini 3.1 Flash-Lite cost?

What is Gemini 3.1 Flash-Lite API pricing?

How verbose is Gemini 3.1 Flash-Lite?

Is Gemini 3.1 Flash-Lite a reasoning model?

What input modalities does Gemini 3.1 Flash-Lite support?

What output modalities does Gemini 3.1 Flash-Lite support?

Can Gemini 3.1 Flash-Lite process images?

Is Gemini 3.1 Flash-Lite multimodal?

What is the context window of Gemini 3.1 Flash-Lite?

Is Gemini 3.1 Flash-Lite open source?

How many parameters does Gemini 3.1 Flash-Lite have?

How does Gemini 3.1 Flash-Lite perform on benchmarks?

What is the knowledge cutoff date for Gemini 3.1 Flash-Lite?

Is Gemini 3.1 Flash-Lite available via API?

Where can I use Gemini 3.1 Flash-Lite?