Google, proprietary
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite by Google ranks 211th of 354 ranked models on the Noometry Index as of October 2026, with a score of 37.0. Its strongest category is agentic & tool use, where it ranks 96th. API pricing starts at $0.10 per million input tokens and $0.40 per million output tokens, with a 1.05M-token context window.
Last verified
Specifications
- Noometry rank
- #211 of 354
- Index score
- 37.0
- Evidence
- Confirmed 33 results
- Provider
Google
- Released
- June 17, 2025
- Weights
- Proprietary
- Reasoning
- Yes
- Context window
- 1.05M
- Max output
- 66K
- Input price
- $0.10 / M
- Output price
- $0.40 / M
- Blended price
- $0.18 / M
- Output speed
- 172 tokens/s Kagi
- Value
- #40 of 219
- Knowledge cutoff
- January 2025
- Input
- text, image, audio, video, pdf
Category scores
Each category score combines every public result we have in that category.
- Coding 38.5
- Agentic & Tool Use 28.0
- Reasoning 22.2
- Math 38.0
- Knowledge 32.5
- Multimodal 29.1
- Multilingual 49.3
- Instruction Following 70.0
- Long Context 33.3
- Writing & Preference 56.8
| Category | Score | Rank | Results |
|---|---|---|---|
| Coding | 38.5 | #173 | 2 |
| Agentic & Tool Use | 28.0 | #96 | 1 |
| Reasoning | 22.2 | #205 | 4 |
| Math | 38.0 | #144 | 2 |
| Knowledge | 32.5 | #210 | 4 |
| Multimodal | 29.1 | #114 | 2 |
| Multilingual | 49.3 | #134 | 1 |
| Instruction Following | 70.0 | #168 | 2 |
| Long Context | 33.3 | #262 | 2 |
| Writing & Preference | 56.8 | #135 | 4 |
Strengths and weaknesses
Categories where Gemini 2.5 Flash-Lite places highest and lowest among the models ranked in each, with its score against that category's median.
Strongest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Writing & Preference | 56.8 | +3.1 | #135 of 312, top 44% |
| Math | 38.0 | +1.4 | #144 of 327, top 45% |
| Multilingual | 49.3 | +1.9 | #134 of 297, top 46% |
Weakest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Multimodal | 29.1 | −9.4 | #114 of 128, top 90% |
| Long Context | 33.3 | −7.6 | #262 of 296, top 89% |
| Knowledge | 32.5 | −4.8 | #210 of 314, top 67% |
Closest competitors
The models ranked just above and below Gemini 2.5 Flash-Lite. When scores are this close, price and speed are often the better way to choose.
| Model | Rank | Score | Blended $/M | Speed | |
|---|---|---|---|---|---|
| Step 3.7 Flash | #207 | 37.3 | $0.42 | — | Compare |
| GPT-4.5 | #208 | 37.2 | — | — | Compare |
| Yi-Lightning | #209 | 37.1 | — | — | Compare |
| Qwen Plus | #210 | 37.1 | $0.60 | 37 | Compare |
| o3-mini | #212 | 36.7 | $1.93 | — | Compare |
| Llama 3.1 Nemotron Ultra 253b v1 | #213 | 36.7 | — | — | Compare |
| Granite 4.0 H Small | #214 | 36.5 | — | — | Compare |
| Command A | #215 | 36.5 | $4.38 | 28 | Compare |
Sponsored placements are available on pages like this one. Advertise on Noometry
Benchmark results
Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.
Coding
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| WeirdML | 35.2% | Epoch AI | |||
| WeirdML | 35.2% | #91 of 119, top 77% | 16K | Epoch AI | |
| LMArena Coding | 1372 | no-thinking | LMArena | 2026-10-08 | |
| LMArena Coding | 1373 | #155 of 294, top 53% | thinking | LMArena | 2026-10-08 |
| ALE-Bench | 325.9 | #97 of 105, top 93% | Epoch AI |
Agentic & Tool Use
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Berkeley Function Calling Leaderboard | 36.9% | #28 of 49, top 58% | fc | Berkeley Function Calling Leaderboard |
Reasoning
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Kagi LLM Benchmark | 40.5% | #76 of 99, top 77% | Kagi LLM Benchmark | ||
| LMArena Hard Prompts | 1377 | #140 of 297, top 48% | no-thinking | LMArena | 2026-10-08 |
| LMArena Hard Prompts | 1374 | thinking | LMArena | 2026-10-08 | |
| DTBench | 62.8% | #107 of 151, top 71% | Epoch AI | ||
| LMCA | 18.1% | #96 of 125, top 77% | Epoch AI | ||
| Epoch Capabilities Index | 133.94 | #129 of 213, top 61% | Epoch AI | 2025-06-17 |
Math
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Omni-MATH | 48% | #17 of 57, top 30% | HELM Capabilities | ||
| LMArena Math | 1373 | #147 of 285, top 52% | no-thinking | LMArena | 2026-10-08 |
| LMArena Math | 1363 | thinking | LMArena | 2026-10-08 |
Knowledge
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| MMLU-Pro | 53.7% | #50 of 58, top 87% | HELM Capabilities | ||
| Vectara Hallucination Rate (lower is better) | 3.3% | #2 of 96, top 3% | Vectara Hallucination Leaderboard | ||
| GPQA (HELM) | 30.9% | #52 of 57, top 92% | HELM Capabilities | ||
| LMArena Expert | 1373 | #139 of 273, top 51% | no-thinking | LMArena | 2026-10-08 |
| LMArena Expert | 1366 | thinking | LMArena | 2026-10-08 |
Multimodal
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Vision | 1198 | #79 of 122, top 65% | no-thinking | LMArena | 2026-10-09 |
| LMArena Vision | 1187 | thinking | LMArena | 2026-10-09 | |
| VPCT | 30% | #24 of 24, top 100% | Epoch AI |
Multilingual
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Non-English | 1369 | #134 of 297, top 46% | no-thinking | LMArena | 2026-10-08 |
| LMArena Non-English | 1360 | thinking | LMArena | 2026-10-08 | |
| LMArena Chinese | 1404 | #137 of 285, top 49% | no-thinking | LMArena | 2026-10-08 |
| LMArena Chinese | 1400 | thinking | LMArena | 2026-10-08 | |
| LMArena French | 1388 | #127 of 223, top 57% | no-thinking | LMArena | 2026-10-08 |
| LMArena French | 1387 | thinking | LMArena | 2026-10-08 | |
| LMArena German | 1389 | #105 of 231, top 46% | no-thinking | LMArena | 2026-10-08 |
| LMArena German | 1373 | thinking | LMArena | 2026-10-08 | |
| LMArena Japanese | 1359 | #92 of 211, top 44% | no-thinking | LMArena | 2026-10-08 |
| LMArena Japanese | 1350 | thinking | LMArena | 2026-10-08 | |
| LMArena Korean | 1350 | no-thinking | LMArena | 2026-10-08 | |
| LMArena Korean | 1360 | #95 of 213, top 45% | thinking | LMArena | 2026-10-08 |
| LMArena Russian | 1373 | #132 of 283, top 47% | no-thinking | LMArena | 2026-10-08 |
| LMArena Russian | 1364 | thinking | LMArena | 2026-10-08 | |
| LMArena Spanish | 1396 | #115 of 226, top 51% | no-thinking | LMArena | 2026-10-08 |
| LMArena Spanish | 1365 | thinking | LMArena | 2026-10-08 |
Instruction Following
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| IFEval | 81% | #39 of 57, top 69% | HELM Capabilities | ||
| LMArena Instruction Following | 1355 | no-thinking | LMArena | 2026-10-08 | |
| LMArena Instruction Following | 1367 | #134 of 298, top 45% | thinking | LMArena | 2026-10-08 |
Long Context
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Fiction.LiveBench | 47.2% | #36 of 47, top 77% | Epoch AI | ||
| LMArena Longer Query | 1373 | no-thinking | LMArena | 2026-10-08 | |
| LMArena Longer Query | 1373 | #136 of 291, top 47% | thinking | LMArena | 2026-10-08 |
Writing & Preference
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Text | 1379 | #136 of 297, top 46% | no-thinking | LMArena | 2026-10-08 |
| LMArena Text | 1369 | thinking | LMArena | 2026-10-08 | |
| LMArena Creative Writing | 1358 | no-thinking | LMArena | 2026-10-08 | |
| LMArena Creative Writing | 1367 | #113 of 295, top 39% | thinking | LMArena | 2026-10-08 |
| WildBench | 81.8% | #21 of 57, top 37% | HELM Capabilities | ||
| LMArena Multi-Turn | 1366 | #142 of 295, top 49% | no-thinking | LMArena | 2026-10-08 |
| LMArena Multi-Turn | 1360 | thinking | LMArena | 2026-10-08 |
API pricing by provider
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
|---|---|---|---|---|
| $0.10 | $0.40 | $0.01 | 2026-10-10 | |
| openrouter | $0.10 | $0.40 | $0.01 | 2026-10-10 |
| vertex | $0.10 | $0.40 | $0.01 | 2026-10-10 |
Compare Gemini 2.5 Flash-Lite
- Gemini 2.5 Flash-Lite vs Gemini 2.0 Flash-Lite
- Gemini 2.5 Flash-Lite vs Qwen Plus
- Gemini 2.5 Flash-Lite vs o3-mini
- Gemini 2.5 Flash-Lite vs Yi-Lightning
- Gemini 2.5 Flash-Lite vs Llama 3.1 Nemotron Ultra 253b v1
- Gemini 2.5 Flash-Lite vs GPT-4.5
- Gemini 2.5 Flash-Lite vs Granite 4.0 H Small
- Gemini 2.5 Flash-Lite vs GPT-6 Astra
- Gemini 2.5 Flash-Lite vs Claude Fable 5.1
- Gemini 2.5 Flash-Lite vs Kimi K3
- Gemini 2.5 Flash-Lite vs Grok 4.6
- Gemini 2.5 Flash-Lite vs Qwen3.8 Max
- Gemini 2.5 Flash-Lite vs GLM-5.3
- Gemini 2.5 Flash-Lite vs Muse Spark 1.3
Other Google models
Frequently asked questions
How good is Gemini 2.5 Flash-Lite?
Gemini 2.5 Flash-Lite by Google ranks 211th of 354 ranked models on the Noometry Index as of October 2026, with a score of 37.0. Its strongest category is agentic & tool use, where it ranks 96th. API pricing starts at $0.10 per million input tokens and $0.40 per million output tokens, with a 1.05M-token context window.
How much does Gemini 2.5 Flash-Lite cost?
Gemini 2.5 Flash-Lite costs $0.10 per million input tokens and $0.40 per million output tokens on Google's own API, with cached input at $0.01.
What is Gemini 2.5 Flash-Lite's context window?
Gemini 2.5 Flash-Lite accepts up to 1.05M tokens of input and can write up to 66K tokens in one response.
Is Gemini 2.5 Flash-Lite open source?
No. Gemini 2.5 Flash-Lite is proprietary and available only through Google's API and partner platforms.
How fast is Gemini 2.5 Flash-Lite?
Gemini 2.5 Flash-Lite generated about 172 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.
What are Gemini 2.5 Flash-Lite's strengths and weaknesses?
Relative to other ranked models, Gemini 2.5 Flash-Lite places best in writing & preference, math, multilingual and lowest in multimodal, long context, knowledge.
What is Gemini 2.5 Flash-Lite best at?
Its best category is agentic & tool use, where it ranks 96th on Noometry.