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Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite is a reasoning model from Google in the Gemini 2.5 Flash family. 12 benchmarks count toward its index, in 7 categories.

  • Closed weights
  • Reasoning
  • Input: text · image · file · audio · video
  • Released 2025-07
RankedEnough results for an index.
IndexAll categories, out of 100.27.1±5.2Median 51.0
CoverageIndex weight that has results.95%
SpeedOutput tokens per second.140/sMedian 67/s
First tokenSeconds to the first output token.0.5 sMedian 1.3 s
Input / 1MUS dollars per 1M input tokens.$0.10Median $0.60
Output / 1MUS dollars per 1M output tokens.$0.40Median $2.55
ContextMaximum tokens in one request.1.05MMedian 262K
EloLMArena rating and rank.—Median 1422

Details

From OpenRouter
API IDThe model ID on OpenRouter.
google/gemini-2.5-flash-lite
Max outputMaximum output tokens per request.
66K
Knowledge cutoffLast date of training data.
31 Jan 2025
ToolsTool calls on OpenRouter.
Yes
JSON outputOutput follows a given JSON schema.
Yes
Cache read / 1MUS dollars per 1M cached tokens.
$0.01
WeightsLink to the published weights.
—
RetiresDate OpenRouter removes the model.
—

Where to run

APIs that serve this model.
3 providers
ProviderTypeMaker, cloud, host or router.InputOutputContext
Google Vertex AIDeprecatedMaker$0.10$0.401.05M
OpenRouterDeprecatedRouter$0.10$0.401.05M
SAP AI CoreDeprecatedCloud$0.10$0.401.05M

US dollars per 1M tokens, list price. From models.dev (opens in a new tab) (MIT).

Capabilities

Index per category, out of 100.
Out of 100
AgenticMulti-step tasks with tools.1 counted15.4—
CodingCode writing and repair.1 counted24.5—
ReasoningLogic problems and puzzles.2 counted33.1197 / 227
MultimodalTasks with images and text.2 counted33.279 / 84
KnowledgeFacts and expert knowledge.4 counted27.1250 / 268
MultilingualTasks in many languages.None counted——
InstructionTasks with strict prompt rules.1 counted18.5—
MathMath problems.1 counted26.0—

Results

12 counted
BenchmarkThe benchmark name.ResultThe score from the source.PlacePlace among results on this benchmark.
Massive Multi-discipline Multimodal UnderstandingMultimodal72.9%16 / 28
Critical Physics TasksReasoning0.0%150 / 214
Graduate-Level Google-Proof Q&AKnowledge64.6%104 / 141
Measuring Short-Form Factuality in Large Language ModelsKnowledge10.7%11 / 14
American Invitational Mathematics Examination 2025Math49.8%42 / 53
Artificial Analysis Long Context ReasoningReasoning32.0%177 / 217
Artificial Analysis MMMU-ProMultimodal54.0%98 / 113
Artificial Analysis IFBenchInstruction31.5%131 / 146
τ²-Bench Tool-Agent-User EvaluationAgentic19.0%134 / 149
Artificial Analysis GPQA DiamondKnowledge47.4%179 / 199
Artificial Analysis Omniscience AccuracyKnowledge15.4%151 / 164
Artificial Analysis Humanity's Last ExamKnowledge3.7%208 / 222
Software Engineering Benchmark VerifiedCoding31.6%110 / 114
MedScribeHealthcare72.8% ±2.086 / 109
MedCodeHealthcare27.1% ±1.8101 / 106

Sources

BenchLMCC BY-NC 4.0OpenRouterTerms restrict reusemodels.devMIT

History

5 releases
Index27.8 → 27.1
23 Oct 202612 Nov 2026
Output / 1M$0.40 → $0.40
23 Oct 202612 Nov 2026

Questions

How much does Gemini 2.5 Flash-Lite cost?
$0.10 per 1M input tokens, $0.40 per 1M output tokens.
How good is Gemini 2.5 Flash-Lite?
Its index is 27.1 out of 100. That is place 260 of 283 ranked models.
What is Gemini 2.5 Flash-Lite best at?
Reasoning: 33.1 out of 100, place 197 of 227.
How fast is Gemini 2.5 Flash-Lite?
140 output tokens per second, 0.5 s to the first token.
What is the context window of Gemini 2.5 Flash-Lite?
1.05M tokens.
Is Gemini 2.5 Flash-Lite open source?
No. Its weights are not published.
When was Gemini 2.5 Flash-Lite released?
July 2025.

Badge

Gemini 2.5 Flash-Lite on Inference360: place 260 of 283, index 27.1
HTML
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Markdown
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