Gemini 2.5 Flash-Lite

Best for high volume, cost efficient tasks

Introducing 2.5 Flash-Lite, a thinking model for those looking for low cost and latency.



Performance

2.5 Flash-Lite has all-round, significantly higher performance than 2.0 Flash-Lite on coding, math, science, reasoning and multimodal benchmarks.

Benchmark Notes Gemini 2.5 Flash-Lite Thinking Gemini 2.5 Flash-Lite Non-thinking Gemini 2.0 Flash
Reasoning & knowledge Humanity's Last Exam (no tools) 6.9% 5.1% 5.1%*
Mathematics AIME 2025 63.1% 49.8% 29.7%
Code generation LiveCodeBench (UI: 1/1/2025-5/1/2025) 34.3% 33.7% 29.1%
Code editing Aider Polyglot 27.1% 26.7% 21.3%
Agentic coding SWE-bench Verified single attempt 27.6% 31.6% 21.4%
multiple attempts 44.9% 42.6% 34.2%
Factuality SimpleQA 13.0% 10.7% 29.9%
Factuality FACTS grounding 86.8% 84.1% 84.6%
Visual reasoning MMMU 72.9% 72.9% 69.3%
Image understanding Vibe-Eval (Reka) 57.5% 51.3% 55.4%
Long context MRCR v2 128k (average) 30.6% 16.6% 19.0%
1M (pointwise) 5.4% 4.1% 5.3%
Multilingual performance Global MMLU (Lite) 84.5% 81.1% 83.4%

Methodology

Gemini results: All Gemini scores are pass @1."Single attempt" settings allow no majority voting or parallel test-time compute; "multiple attempts" settings allow test-time selection of the candidate answer. They are all run with the AI Studio API with default sampling settings. To reduce variance, we average over multiple trials for smaller benchmarks. Aider Polyglot score is the pass rate average of 3 trials. Vibe-Eval results are reported using Gemini as a judge. Google's scaffolding for "multiple attempts" for SWE-Bench includes drawing multiple trajectories and re-scoring them using model's own judgement. For Aider results differ from the official leaderboard due to a difference in the settings used for evaluation (non-default).

Result sources: Where provider numbers are not available we report numbers from leaderboards reporting results on these benchmarks: Humanity's Last Exam results are sourced from https://agi.safe.ai/ and https://scale.com/leaderboard/humanitys_last_exam, LiveCodeBench results are from https://livecodebench.github.io/leaderboard.html (1/1/2025 - 5/1/2025 in the UI), Aider Polyglot numbers come from https://aider.chat/docs/leaderboards/. FACTS come from https://www.kaggle.com/benchmarks/google/facts-grounding. For MRCR v2 which is not publically available yet we include 128k results as a cumulative score to ensure they can be comparable with other models and a pointwise value for 1M context window to show the capability of the model at full length. The methodology has changed in this table vs previously published results for MRCR v2 as we have decided to focus on a harder, 8-needle version of the benchmark going forward.

* these results are on an earlier HLE dataset, obtained from https://scale.com/leaderboard/humanitys_last_exam_preview


Model information

Name
2.5 Flash-Lite
Status
General availability
Input
  • Text
  • Image
  • Video
  • Audio
  • PDF
Output
  • Text
Input tokens
1M
Output tokens
64k
Knowledge cutoff
January 2025
Tool use
  • Search as a tool
  • Code execution
Best for
  • High volume, low-cost and low latency tasks
Availability
  • Google AI Studio
  • Gemini API
  • Vertex AI
Documentation
View developer docs
Model card
View model card
Technical report
View technical report