Google Proprietary Released Jul 21, 2026 Updated
Gemini 3.5 Flash-Lite
ToolsGuide Scores, rankings, test results and price.
Model summary
Summary
Gemini 3.5 Flash-Lite is Google's model, released Jul 21, 2026. It's ranked in 3 categories and does best at text: #63 of 419, with a ToolsGuide Score of 436 against a median of 252.
It also ranks #28 of 167 for vision (490), #90 of 151 for coding (294).
Its text and vision score uses votes only, because it has published fewer than two of the tests used for those categories.
It costs $0.30 per million input tokens and $2.50 per million output tokens, below the median text model ($0.44).
It has published results on 6 tests.
Specifications
- Creator
Google
- Licence
- Proprietary
- Released
- Jul 21, 2026
- Context window
- 1M tokens
- Input price
- $0.30 / 1M
- Output price
- $2.50 / 1M
- Tests reported
- 6
- Settings ranked
- Gemini 3.5 Flash-Lite
Model comparison
Against the ten best text models.
Text score
ToolsGuide Score out of 1,000 · Higher is better
Vision score
ToolsGuide Score out of 1,000 · Higher is better
Input price
USD per 1M input tokens · Lower is better
Leaderboards
Where Gemini 3.5 Flash-Lite sits among the top 20 in each category.
Text
#63 of 419 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
Vision
#28 of 167 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
Coding
#90 of 151 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
By task
Scored from votes on each kind of prompt alone, on the same 1,000-point scale.
Score against price
Text · the 10 priced models ranked closest · up and to the left is better value
Against its nearest rivals
Text prompts by kind · ToolsGuide Score from votes · hover for values
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Gemini 3.5 Flash-Lite
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GLM-5 -
Grok 4.6 (High)
Text
Gemini 3.5 Flash-Lite · hover a bar for its rank
Vision
Gemini 3.5 Flash-Lite · hover a bar for its rank
Coding
Gemini 3.5 Flash-Lite · hover a bar for its rank
Test results
Every test Gemini 3.5 Flash-Lite has a published result on, out of 100%, with its rank among the models that took it.
SWE-Bench Pro
#46 / 63Harder, longer software engineering tasks from real codebases.
54.2%
Self-reported · In the coding score
CharXiv-R
#41 / 61Reading and reasoning about charts from scientific papers.
76.5%
Self-reported
Terminal-Bench 2.1
#45 / 53Terminal-Bench 2.1 is an updated release of the Terminal-Bench benchmark that tests AI agents' ability to operate a computer via the terminal. It evaluates how well models handle real-world, end-to-end tasks autonomously, including compiling code, training models, setting up servers, system administration, data science workflows, and security tasks.
54.0%
Self-reported
MRCR v2 (8-needle)
#26 / 32MRCR v2 (8-needle) is a variant of the Multi-Round Coreference Resolution benchmark that includes 8 needle items to retrieve from long contexts. This tests models' ability to simultaneously track and reason about multiple pieces of information across extended conversations.
21.3%
Self-reported · In the text score
OSWorld-Verified
#16 / 28OSWorld-Verified is a verified subset of OSWorld, a scalable real computer environment for multimodal agents supporting task setup, execution-based evaluation, and interactive learning across Ubuntu, Windows, and macOS.
74.0%
Self-reported
MLE-Bench
#2 / 2MLE-Bench evaluates AI agents on machine learning engineering tasks by measuring their performance on Kaggle competitions.
39.2%
Unverified