Gemini 2.5 Flash Native Audio preview 09 2025
Google AI realtimeGemini 2.5 Flash Native Audio preview 09 2025 is a Google AI realtime audio model.It supports a 1,048,576-token context windowwith up to 8,192 output tokens.Input is priced at $0.500/M tokens and output at $2.00/M tokens. Capabilities include audio input. Route Gemini 2.5 Flash Native Audio 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 Native Audio preview 09 2025 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.5000/M input · $2.00/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 | $0.500/M | |
| Output | $2.00/M | |
| Blended (3:1 in:out) | $0.875/M Cross-model comparable price |
Context & limits
- Context window
- 1,048,576 tokens
- Max input
- 1,048,576 tokens
- Max output
- 8,192 tokens
- Modalities
- audio_in, audio_out, text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input ✓ supported
- Audio output ✓ supported
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Strengths & caveats — when to pick Gemini 2.5 Flash Native Audio preview 09 2025
Data-driven from Gemini 2.5 Flash Native Audio preview 09 2025's percentile within realtime peers.
Where it's strong
- +long-context work — the largest context window of any realtime model on Future AGI (1,048,576 tokens)
Watch out for
- !high cost — input + output rates are in the top 87% of priced realtime peers; consider a cheaper sibling for high-volume workloads
Benchmarks pending
We haven't logged public benchmark scores for Gemini 2.5 Flash Native Audio preview 09 2025 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Gemini 2.5 Flash Native Audio 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 Native Audio 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="google/gemini-2-5-flash-native-audio-preview-09-2025",
messages=[{"role": "user", "content": "Hello, Gemini 2.5 Flash Native Audio 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="google/gemini-2-5-flash-native-audio-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 ↗Explore similar models
Gemini 2.5 Flash Native Audio preview 09 2025 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- Gemini 3.8 Live Extended ThinkingGoogle AI · $0.750/M in · $4.50/M out · 131,072 ctxView model →
- Gemini Live 2.5 Flash preview Native Audio 09 2025Google AI · $0.500/M in · $2.00/M out · 1,048,576 ctxView model →
- Gemini 2.5 Flash Native Audio preview 12 2025Google AI · $0.500/M in · $2.00/M out · 131,072 ctxView model →
- Gemini 3.1 Flash Live previewGoogle AI · $0.750/M in · $4.50/M out · 131,072 ctxView model →
FAQ
How much does Gemini 2.5 Flash Native Audio preview 09 2025 cost?
Input is priced at $0.500 per 1M tokens and output at $2.00 per 1M tokens (Google AI, last verified Oct 22, 2026).
What is the context window of Gemini 2.5 Flash Native Audio preview 09 2025?
Gemini 2.5 Flash Native Audio preview 09 2025 supports a 1,048,576-token context window with up to 8,192 output tokens.
Does Gemini 2.5 Flash Native Audio preview 09 2025 support function calling?
Gemini 2.5 Flash Native Audio preview 09 2025 does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is Gemini 2.5 Flash Native Audio preview 09 2025 good for production?
Gemini 2.5 Flash Native Audio preview 09 2025 is well-suited for long-context work — the largest context window of any realtime model on Future AGI (1,048,576 tokens). Consider alternatives if you need high cost — input + output rates are in the top 87% of priced realtime peers; consider a cheaper sibling for high-volume workloads.
How can I route to Gemini 2.5 Flash Native Audio 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 Native Audio 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.