Intelligence
25 scoreGoogle · Flagships Analysis
Gemini 3.1 Flash-Lite
Intelligence, Performance & Price Analysis
Canonical slug: gemini-3-1-flash-lite-preview · Canonical model registry at build time
Speed
339.0 output tokens/secLatency
5.32s TTFTInput Price
$0.25 / 1M tokensOutput Price
$1.50 / 1M tokensVerbosity
55M Output tokens from Intelligence Index 2 out of 4 units for Verbosity . CompaExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Strong general-purpose model
Gemini 3.1 Flash-Lite scores 25 on the Artificial Analysis Intelligence Index, placing it above average among other reasoning models in a similar price tier (median: 15). Gemini 3.1 Flash-Lite generates output at 339.0 tokens per second (based on Google's API), which is well above average compared to other reasoning models in a similar price tier (median: 99.1 t/s). Gemini 3.1 Flash-Lite costs $0.25 per 1M input tokens (better than average, median: $0.25) and $1.50 per 1M output tokens (somewhat higher than average, median: $0.87), based on Google's API.
Suitable for most production tasks, but high-volume or repetitive work should still be compared against cheaper routes.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | optimal | Intelligence score 25 supports capable reasoning, but very hard tasks may benefit from higher-tier models. |
| High-volume chat & customer-facing | optimal | Output speed 339.0 tokens/sec and capable intelligence make this suitable for real-time chat at scale. |
| Latency-sensitive applications | viable | TTFT 5.32s — latency may be noticeable in interactive use. |
| Cost-sensitive pipelines | optimal | Output pricing at $1.50 is reasonable for moderate volume. |
Cost Pressure Analysis
Medium
Pricing is moderate — input $0.25, output $1.50. Costs accumulate at volume but are manageable for valuable tasks.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | Yes | faq |
| Input modalities | Gemini 3.1 Flash-Lite supports text, image, speech, and video input. | faq |
| Output modalities | Gemini 3.1 Flash-Lite supports text output. | faq |
| Context window | Gemini 3.1 Flash-Lite has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request. | faq |
| Open weights / source | No, Gemini 3.1 Flash-Lite is proprietary. The model weights are not publicly available. | faq |
| Parameters | Gemini 3.1 Flash-Lite is a proprietary model and Google has not disclosed the model size or parameter count. | faq |
| Knowledge cutoff | Gemini 3.1 Flash-Lite has a knowledge cutoff of January 2025. The model's training data includes information up to this date. | faq |
| API availability | Yes, Gemini 3.1 Flash-Lite is available via API through 1 provider. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Tokens per Intelligence Index Task
Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Gemini 3.1 Flash-Lite.
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
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 25 on the Artificial Analysis Intelligence Index, placing it above average among other reasoning models in a similar price tier (median: 15).
Gemini 3.1 Flash-Lite generates output at 339.0 tokens per second (based on Google's API), which is well above average compared to other reasoning models in a similar price tier (median: 99.1 t/s).
Gemini 3.1 Flash-Lite has a time to first token (TTFT) of 5.32s (based on Google's API), which is at the higher end compared to other reasoning models in a similar price tier (median: 1.95s).
Gemini 3.1 Flash-Lite costs $0.25 per 1M input tokens (better than average, median: $0.25) and $1.50 per 1M output tokens (somewhat higher than average, median: $0.87), 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.
When evaluated on the Intelligence Index, Gemini 3.1 Flash-Lite generated 55M output tokens, which is better than average compared to other reasoning models in a similar price tier (median: 55M).
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 1.0M 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 25 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
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.
Gemini 3.1 Flash-Lite is available through 1 API provider.