Data as of October 19, 2026 · How the score is built
Gemini 3.5 Flash-Lite
Decision readingGemini 3.5 Flash-Lite scores 49.6 out of 100 and ranks #101 of 226. This profile shows 12 source-displayable benchmark rows; its strongest eligible category is Coding at #77. API pricing is $0.3 input and $2.5 output per million tokens, with cached input at $0.03.
Released Jul 21, 2026 — see all recent releases
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
49.6/100
field median 50#101 of 226 ranked models
Public
#101of 226
Verified #57 of 87
Price
$0.30input / $2.50 output
input median $0.75cached $0.030 · batch + cache $0.020 · blended $1.40
Speed
391tok/s
field median 94 tok/sFirst token 7.43 s
Context
1Mtokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Coding ranks #77. Particularly well-suited for software development and code generation tasks.
Validate before choosing
12 published rows leave some tracked benchmark slots empty. Knowledge is its lowest eligible category at #86.
Source-linked · 12 displayable benchmark rows
Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #81 of 130Percentile 38thWeight 22%3 benchmarksVerified | 35.6 | 3 benchmarks | Verified | |||
| CodingRank #77 of 151Percentile 49thWeight 20%4 benchmarksVerified | 35.8 | 4 benchmarks | Verified | |||
| ReasoningRank Not rankedWeight 17%1 benchmarkVerified | 70.9 | 1 benchmark | Verified | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeRank #86 of 186Percentile 54thWeight 12%2 benchmarksVerified | 49.1 | 2 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathRank Not rankedWeight 5%2 benchmarksVerified | 37.8 | 2 benchmarks | Verified |
12 of 686 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic3/3 verified
- Coding4/4 verified
- Reasoning1/1 verified
- MultimodalNot measured
- Knowledge2/2 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- Math2/2 verified
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Gemini 3.5 Flash-Lite category percentile values
- Agentic38th percentile
- Coding49th percentile
- ReasoningNot eligible
- MultimodalNot eligible
- Knowledge54th percentile
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#81/130
- Coding#77/151
- ReasoningNot ranked
- MultimodalNot ranked
- Knowledge#86/186
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score54.2% | Versus best verified row Best verified: Claude Opus 5.5 · 89.9% | Gap35.7 behind | Weight26% ref. weight | |
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | Score79.0% | Versus best verified row Best verified: Claude Fable 5.1 · 90.5% | Gap11.5 behind | Weight8% ref. weight | |
| Terminal-Bench 2.1 | Score54.0% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap38.8 behind | WeightScored in Agentic | |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score75.0% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap22 behind | WeightDisplay only | Verified |
Agentic3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.1 | Score54.0% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap38.8 behind | Weight8% ref. weight | |
| OSWorld-Verified | Score74% | Versus best verified row Best verified: Qwen3.8 Max · 86.1% | Gap12.1 behind | Weight7% ref. weight | |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | Score50.2% | Versus best verified row Best verified: GPT-6 Astra · 87.3% | Gap37.1 behind | Weight4% ref. weight |
Reasoning1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MRCRv2 | Score72.2% | Versus best verified row Best verified: Sakana Fugu-Ultra · 93.6% | Gap21.4 behind | WeightWeighted 20% |
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score85.8% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap6.6 behind | Weight6% ref. weight | Verified |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score83.8% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap11.7 behind | Weight2% ref. weight |
Math2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score25.965% | Versus best verified row Best verified: GPT-6 Astra · 93.684% | Gap67.7 behind | WeightWeighted 30% | Benchmark exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score0.000% | Versus best verified row Best verified: GPT-6.1 Sol · 100.000% | Gap100 behind | WeightWeighted 10% | Benchmark exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 12 rowsWhat it costs to get this score
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
Gemini 3.5 Flash-Lite · 49.6 score · $1.40 blended per million tokens
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Horizontal: blended price per million tokens, log scale · Vertical: public score
Lineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Base entry
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- gemini-3.5-flash-liteGoogle Gemini 3.5 Flash-Lite model documentation
- Context window
- 1MGoogle latest Gemini model guide
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- text, image, video, audio, pdfGoogle Gemini 3.5 Flash-Lite model documentation
- Output modalities
- textGoogle Gemini 3.5 Flash-Lite model documentation
- Parameters
- Not disclosed by the provider
- Availability
- Gemini API · Google AI StudioGoogle latest Gemini model guide
- Cloud regions
- Not tracked yet
- Lifecycle
- activeGoogle Gemini 3.5 Flash-Lite model documentation
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Published at $0.030 per million cached input tokensGoogle Gemini API pricing
- Self-host
- Weights are not published
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Gemini 3.5 Flash-Lite ranks #101 of 226 on the public leaderboard with a score of 49.61/100. Its source-verified position is #57 of 87.
Gemini 3.5 Flash-Lite is a proprietary model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Available through the Gemini API in Google AI Studio and Android Studio, Gemini Enterprise, and the Gemini app.
Its explicit predecessor is Gemini 3.1 Flash-Lite. 12 of 686 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #77, while its lowest eligible position is Knowledge at #86. particularly well-suited for software development and code generation tasks.
Last updated October 19, 2026. Runtime fields remain blank until a sourced snapshot exists.
Questions
How does Gemini 3.5 Flash-Lite perform overall in AI benchmarks?
Gemini 3.5 Flash-Lite ranks #101 out of 226 models on the public BenchAlign leaderboard, with a score of 49.61/100. Its evidence status is Supported, and this profile shows 12 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Gemini 3.5 Flash-Lite good for knowledge and understanding?
Gemini 3.5 Flash-Lite ranks #86 out of 186 eligible models for knowledge and understanding, with a public category score of 49.1/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Gemini 3.5 Flash-Lite good for coding and programming?
Gemini 3.5 Flash-Lite ranks #77 out of 151 eligible models for coding and programming, with a public category score of 35.8/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Gemini 3.5 Flash-Lite good for mathematics?
Gemini 3.5 Flash-Lite has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Gemini 3.5 Flash-Lite good for reasoning and logic?
Gemini 3.5 Flash-Lite has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Gemini 3.5 Flash-Lite good for agentic tool use and computer tasks?
Gemini 3.5 Flash-Lite ranks #81 out of 130 eligible models for agentic tool use and computer tasks, with a public category score of 35.6/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Does Gemini 3.5 Flash-Lite have full benchmark coverage on BenchLM?
No. Gemini 3.5 Flash-Lite currently has 26 source-displayable rows across 686 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Gemini 3.5 Flash-Lite?
Gemini 3.5 Flash-Lite has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.