Gemini 2.5 Flash
Google · released Jun 17, 2025
Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater...
Specification
- Context window
- 1.05M
- Max output
- 66K
- Knowledge cutoff
- Jan 31, 2025
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 2
- Moderated
- No
- Uptime
- 100.0%
Intelligence
14.2
46th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
92 t/s
Median across providers
Latency
516ms
Time to first token
Cost per task
$0.22
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro80.9%
- GPQA Diamond68.3%
- AIME 202560.3%
- LiveCodeBench49.5%
- AA-LCR (long context)48.0%
- IFBench39.0%
- SciCode29.1%
- τ²-bench (Telecom)14.9%
- Terminal-Bench Hard12.1%
- Humanity's Last Exam4.7%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 80.9% |
| GPQA Diamond | 68.3% |
| AIME 2025 | 60.3% |
| LiveCodeBench | 49.5% |
| AA-LCR (long context) | 48.0% |
| IFBench | 39.0% |
| SciCode | 29.1% |
| τ²-bench (Telecom) | 14.9% |
| Terminal-Bench Hard | 12.1% |
| Humanity's Last Exam | 4.7% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Mistral Medium 3.1Mistral AI14.7
- Solar Pro 3Upstage14.5
- Llama 4 MaverickMeta14.5
- 14.4
- Gemini 2.5 FlashGoogle14.2
- Ling-2.6-flashInclusionAI14.2
- 13.8
- 13.6
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Mistral Medium 3.1 | 14.7 |
| Solar Pro 3 | 14.5 |
| Llama 4 Maverick | 14.5 |
| Qwen3 VL 235B A22B Instruct | 14.4 |
| Gemini 2.5 Flash | 14.2 |
| Ling-2.6-flash | 14.2 |
| Qwen3 Next 80B A3B Instruct | 13.8 |
| Qwen3 Coder 30B A3B Instruct | 13.6 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.3
- Output / 1M tokens
- $2.5
- Cached input / 1M
- $0.03
- Blended 3:1
- $0.85
One task, estimated
$0.22
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).
Serving providers
Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.
Arena
Head-to-head generation quality
Elo from pairwise judgements, broken out by the kind of thing the model was asked to build.
- Overall Elo
- 1,097
- Win rate
- 45.7%
- Strongest at
- Websites
- Tournaments
- 5,472
Elo by category
Dot position on a 1,025–1,100 scale · Elo has no meaningful zero
- 1,097
- 1,081
- 1,076
- 1,045
- 1,040
Agent categories (full-stack apps, mobile apps) are only scored for models tested in agent harnesses.
View as table
| Category | Elo | Win rate |
|---|---|---|
| Websites | 1097 | 45.7% |
| Data visualisation | 1081 | 45.7% |
| 3D scenes | 1076 | 44.4% |
| Game development | 1045 | 43.5% |
| UI components | 1040 | 43.6% |