Gemini 2.5 Flash
ActiveFast and efficient multimodal model.
History
Gemini 2.5 Flash became available via the Google API on 2025-06-17.
Training & availability
Training data has a knowledge cutoff of 2025-01-31 — information about events after that date is unlikely to appear in the model's responses. Google has not released the underlying model weights — access is via their hosted API only.
Capabilities
-
Context window: 1.0M tokens.
-
Max output: 65K tokens.
-
Input modalities: text, image, audio, video.
Recommended for: vision, agentic, long-context.
Limitations
- The knowledge cutoff is 14 months old — this model will not know about recent events, releases, or API changes.
Quick start
Minimal example using the google API. Copy, paste, replace the key.
from google import genai
client = genai.Client(api_key="...")
resp = client.models.generate_content(
model="gemini-2.5-flash",
contents="Explain quantum computing in one sentence.",
)
print(resp.text)Providers & performance
3 providersMulti-provider inference routes for this model — sorted by throughput. Latency is time-to-first-token; throughput is output tokens per second. Data from OpenRouter, measured over the last 30 minutes.
| Provider | Throughput | Latency (TTFT) | Input $ / 1M | Output $ / 1M | Context | Quant | Supports |
|---|---|---|---|---|---|---|---|
| 74tok/s | 685ms | $0.3 | $2.5 | 1.0M | — | tools · json | |
| 60tok/s | 1.37s | $0.3 | $2.5 | 1.0M | — | tools · json | |
| Google AI Studio | 9tok/s |
Benchmarks
| Benchmark | Score | Source |
|---|---|---|
| LongBench v2Long context | 15% | Third-party llm-stats.com |
| SWE-bench VerifiedCoding | 53.5% resolved | Third-party Papers With Code |
Integrations & tooling support
- Tool calling
- Supported
- Structured outputs
- Supported
Price vs quality
Solid benchmark performance. Pricing not publicly available — check the provider.
- Quality percentile
- 41%
- Effective price
- —
- Pricing breakdown
- — in
— out
Community ratings
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