Gemini 2.5 Pro

Google's proven reasoning model for coding, math, and multimodal analysis
- Context
- 1.0M
- Released
- 1y ago
- Input / 1M tokens
- $1.25
- Output / 1M tokens
- $10.00
- Cached input / 1M tokens
- $0.13
- Cache write / 1M tokens
- $0.38
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
Other published benchmarks
| Evaluation | Reported score and source |
|---|---|
| ARC-AGI-2 | 4.0% Third-party evaluator · gemini-2.5-pro_16K Thinking budget: 16K External evaluation via Epoch AIOriginal-source terms apply |
| ARC-AGI-2 | 4.9% Third-party evaluator · gemini-2.5-pro_32K Thinking budget: 32K External evaluation via Epoch AIOriginal-source terms apply |
| ARC-AGI-2 | 2.9% Third-party evaluator · gemini-2.5-pro_8K Thinking budget: 8K External evaluation via Epoch AIOriginal-source terms apply |
| FrontierMath-2025-02-28-Private | |
| FrontierMath-Tier-4-2025-07-01-Private | |
| FrontierMath-Tier-4-v2-Private | |
| FrontierMath-Tiers-1-3-v2-Private | |
| GPQA diamond | |
| OTIS Mock AIME 2024-2025 | |
| SWE-Bench verified | |
| Terminal Bench | 19.6% Terminal-Bench v2 Leaderboard · gemini-2.5-pro Agent: Gemini CLI Terminal-Bench v2 LeaderboardOriginal-source terms apply |
| Terminal Bench | 26.1% Terminal-Bench v2 Leaderboard · gemini-2.5-pro Agent: Mini-SWE-Agent Terminal-Bench v2 LeaderboardOriginal-source terms apply |
| Terminal Bench | 16.4% Terminal-Bench v2 Leaderboard · gemini-2.5-pro Agent: OpenHands Terminal-Bench v2 LeaderboardOriginal-source terms apply |
| Terminal Bench | 32.6% Terminal-Bench v2 Leaderboard · gemini-2.5-pro Agent: Terminus 2 Terminal-Bench v2 LeaderboardOriginal-source terms apply |
| Terminal Bench | 19.6% https://www.tbench.ai/leaderboard/terminal-bench/2.0 · gemini-2.5-pro Agent: Gemini CLI https://www.tbench.ai/leaderboard/terminal-bench/2.0Original-source terms apply |
| Terminal Bench | 26.1% https://www.tbench.ai/leaderboard/terminal-bench/2.0 · gemini-2.5-pro Agent: Mini-SWE-Agent https://www.tbench.ai/leaderboard/terminal-bench/2.0Original-source terms apply |
| Terminal Bench | 16.4% https://www.tbench.ai/leaderboard/terminal-bench/2.0 · gemini-2.5-pro Agent: OpenHands https://www.tbench.ai/leaderboard/terminal-bench/2.0Original-source terms apply |
| Terminal Bench | 32.6% https://www.tbench.ai/leaderboard/terminal-bench/2.0 · gemini-2.5-pro Agent: Terminus 2 https://www.tbench.ai/leaderboard/terminal-bench/2.0Original-source terms apply |
Benchmark compilation: Epoch AI, Capabilities & benchmarking (CC BY attribution). Scores are converted to percentages and model names matched to the Vertical Tabs catalog. Individual evaluations are credited to their original sources. External results may carry separate licences and evaluation configurations; do not interpret scores from different harnesses as directly comparable.
Specifications
- Developer
- Google DeepMind
- Model family
- Gemini
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- June 17, 2025
- Model ID
google/gemini-2.5-pro- Openness
- Closed
- License
- —
- Input modalities
- text, image, file, audio, video
- Output modalities
- text
- Modality
- text+image+file+audio+video->text
- Tokenizer
- Gemini
- Max output tokens
- 66K
- Knowledge cutoff
- 2025-01
- Hugging Face
- —
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- google/gemini-2.5-pro
- Source family
- gemini-pro
- Last updated
- 2025-06-17
- Knowledge cutoff
- 2025-01
- Model context
- 1.0M
- Model output limit
- 66K
- Open weights
- No
- File attachments
- Yes
- Reasoning
- Yes
- Tool calling
- Yes
- Structured output
- Yes
- Temperature control
- Yes
Published benchmarks
Complete source record (JSON)
{
"id": "google/gemini-2.5-pro",
"name": "Gemini 2.5 Pro",
"description": "Google's proven reasoning model for coding, math, and multimodal analysis",
"family": "gemini-pro",
"last_updated": "2025-06-17",
"knowledge": "2025-01",
"attachment": true,
"reasoning": true,
"tool_call": true,
"structured_output": true,
"temperature": true,
"modalities": {
"input": [
"text",
"image",
"audio",
"video",
"pdf"
],
"output": [
"text"
]
},
"limit": {
"context": 1048576,
"output": 65536
},
"benchmarks": [
{
"name": "Aider Polyglot",
"score": 83.1,
"source": "https://aider.chat/docs/leaderboards/",
"metric": "percent correct",
"date": "2025-06-06"
},
{
"name": "Artificial Analysis Coding Index",
"score": 32,
"source": "https://openrouter.ai/google/gemini-2.5-pro/benchmarks",
"metric": "index",
"date": "2026-06-02"
},
{
"name": "SciCode",
"score": 42.8,
"source": "https://openrouter.ai/google/gemini-2.5-pro/benchmarks",
"metric": "percent correct",
"date": "2026-06-02"
},
{
"name": "Terminal-Bench Hard",
"score": 26.5,
"source": "https://openrouter.ai/google/gemini-2.5-pro/benchmarks",
"metric": "success rate",
"date": "2026-06-02"
}
],
"release_date": "2025-06-17",
"open_weights": false
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| google-ai-studio/flex | 1.0M | $0.63 | $5.00 | $0.06 | $0.19 | 97.22% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-ai-studio/flexBatch pricing | 1.0M | $0.63 | $5.00 | $0.06 | $0.19 | 97.22% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/global | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 96.32% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/eu | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 56.91% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/us | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 92.77% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/globalBatch pricing | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 97.92% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/euBatch pricing | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 45.45% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/usBatch pricing | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 94.12% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-ai-studio | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 99.30% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-ai-studioBatch pricing | 1.0M | $1.25 | $10.00 | $0.13 | $0.38 | 99.23% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/global/priority | 1.0M | $2.25 | $18.00 | $0.22 | $0.68 | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-vertex/global/priorityBatch pricing | 1.0M | $2.25 | $18.00 | $0.22 | $0.68 | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-ai-studio/priority | 1.0M | $2.25 | $18.00 | $0.22 | $0.68 | — | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| google-ai-studio/priorityBatch pricing | 1.0M | $2.25 | $18.00 | $0.22 | $0.68 | — | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Text provenance
Gemini app/webGoogle SynthID text watermarking
Google DeepMind says text generated in the Gemini app and web experience is watermarked with SynthID by adjusting token probabilities.
Official explanation