Gemini 2.5 Flash preview 09 2025
Google Vertex AI chatGemini 2.5 Flash preview 09 2025 is a Google Vertex AI chat model.It supports a 1,048,576-token context windowwith up to 65,535 output tokens.Input is priced at $0.300/M tokens and output at $2.50/M tokens. Capabilities include function calling, vision, reasoning, prompt caching. Route Gemini 2.5 Flash preview 09 2025 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Gemini 2.5 Flash preview 09 2025 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.3000/M input · $2.50/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.
| Input | $0.300/M | |
| Output | $2.50/M | |
| Blended (3:1 in:out) | $0.850/M Cross-model comparable price | |
| Cached input | $0.0300/M |
Context & limits
- Context window
- 1,048,576 tokens
- Max input
- 1,048,576 tokens
- Max output
- 65,535 tokens
- Modalities
- vision, pdf, text
Capabilities
- Function calling ✓ supported
- Parallel tool calls ✓ supported
- Vision input ✓ supported
- Audio input — not advertised
- Audio output — not advertised
- PDF input ✓ supported
- Streaming ✓ supported
- Structured output ✓ supported
- Prompt caching ✓ supported
- Reasoning ✓ supported
Strengths & caveats — when to pick Gemini 2.5 Flash preview 09 2025
Data-driven from Gemini 2.5 Flash preview 09 2025's percentile within chat peers.
Where it's strong
- +long-context tasks — context window in the top 2% of peers
- +parallel tool calls — only 18% of chat models on Future AGI advertise this
- +PDF input — only 18% of chat models on Future AGI advertise this
Watch out for
- No major caveats flagged from public spec.
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call Gemini 2.5 Flash preview 09 2025 via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Gemini 2.5 Flash preview 09 2025 via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="vertex-ai/gemini-2-5-flash-preview-09-2025",
messages=[{"role": "user", "content": "Hello, Gemini 2.5 Flash preview 09 2025!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="vertex-ai/gemini-2-5-flash-preview-09-2025",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
- model: gemini-2-5-flash-preview-09-2025
provider: vertex-ai
weight: 80
fallbacks:
- model: gemini-3-1-flash-lite-preview
provider: vertex-ai
- model: kimi-k2-6
provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Same model on other providers
gemini-2-5-flash-preview-09-2025 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Google AI | $0.300/M | $2.50/M | Aug 6, 2026 |
Explore similar models
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≥30 ELO higher
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25–100 ELO lower, ≤50% of price
- Gemini 1.5 FlashGoogle Vertex AI · $0.0750/M in · $0.300/M out · 1,000,000 ctxView model →
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- GPT-5 nano (2025-08-07)Azure OpenAI · $0.0500/M in · $0.400/M out · 272,000 ctxView model →
- GPT-4.1 nano (2025-04-14)Azure OpenAI · $0.1000/M in · $0.400/M out · 1,047,576 ctxView model →
FAQ
How much does Gemini 2.5 Flash preview 09 2025 cost?
Input is priced at $0.300 per 1M tokens and output at $2.50 per 1M tokens (Google Vertex AI, last verified Oct 22, 2026).
What is the context window of Gemini 2.5 Flash preview 09 2025?
Gemini 2.5 Flash preview 09 2025 supports a 1,048,576-token context window with up to 65,535 output tokens.
Does Gemini 2.5 Flash preview 09 2025 support function calling?
Yes — Gemini 2.5 Flash preview 09 2025 supports function (tool) calling, including parallel tool calls.
Is Gemini 2.5 Flash preview 09 2025 good for production?
Gemini 2.5 Flash preview 09 2025 is well-suited for long-context tasks — context window in the top 2% of peers and parallel tool calls — only 18% of chat models on Future AGI advertise this.
How can I route to Gemini 2.5 Flash preview 09 2025 with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Gemini 2.5 Flash preview 09 2025
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.