Gemini 2.5 Pro Preview 05-06
Google · released May 7, 2025
Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...
Specification
- Context window
- 1.05M
- Max output
- 66K
- Knowledge cutoff
- Jan 31, 2025
- Parameters
- Undisclosed
- Licence
- Proprietary
- Serving providers
- 2
- Moderated
- No
- Uptime
- 98.2%
Intelligence
—
Not benchmarked
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
106 t/s
Median across providers
Latency
2.32s
Time to first token
Cost per task
$0.86
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
This model has not been put through the benchmark suite. Its speed, latency and pricing figures below are still measured.
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Claude Opus 5Anthropic63.1
- Claude Fable 5Anthropic62.1
- GPT-5.6 SolOpenAI60.9
- Kimi K3Moonshot AI59.7
- Qwen3.8 MaxQwen58.1
- Claude Opus 4.8Anthropic57.3
- Muse Spark 1.2Meta56.8
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Claude Opus 5 | 63.1 |
| Claude Fable 5 | 62.1 |
| GPT-5.6 Sol | 60.9 |
| Kimi K3 | 59.7 |
| Qwen3.8 Max | 58.1 |
| Claude Opus 4.8 | 57.3 |
| Muse Spark 1.2 | 56.8 |
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $1.25
- Output / 1M tokens
- $10
- Cached input / 1M
- $0.125
- Blended 3:1
- $3.44
One task, estimated
$0.86
- 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.