Gemini Live 2.5 Flash Native Audio

Google Vertex AI realtime Deprecates in 39d (Dec 13, 2026)
Heads-up: Google Vertex AI has scheduled Gemini Live 2.5 Flash Native Audio for deprecation on Dec 13, 2026. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

Gemini Live 2.5 Flash Native Audio is a Google Vertex AI realtime audio model.It supports a 131,072-token context windowwith up to 65,536 output tokens.Input is priced at $0.500/M tokens and output at $2.00/M tokens. Capabilities include function calling, vision, audio input. Route Gemini Live 2.5 Flash Native Audio via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate Gemini Live 2.5 Flash Native Audio spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0131,072
400
065,536
5,000
01,000,000
Per request
$0.002300
in $0.001500 · out $0.000800
Per day
$11.50
5,000 requests
Per month
$350
152,188 requests

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
131,072 tokens
Max input
131,072 tokens
Max output
65,536 tokens
Modalities
vision, audio_in, audio_out, text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls ✓ supported
  • Vision input ✓ supported
  • 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 Live 2.5 Flash Native Audio

Data-driven from Gemini Live 2.5 Flash Native Audio's percentile within realtime peers.

Where it's strong

  • +long-context work — top-4 largest context window across 45 realtime models
  • +long-form generation — 65,536-token max output, top-0% of peers
  • +vision — only 13% of realtime models on Future AGI advertise this

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
  • !scheduled deprecation on Dec 13, 2026 (39 days)

Benchmarks pending

We haven't logged public benchmark scores for Gemini Live 2.5 Flash Native Audio yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Gemini Live 2.5 Flash Native Audio 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.

SDK
Native Future AGI client (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 Live 2.5 Flash Native Audio 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-live-2-5-flash-native-audio",
    messages=[{"role": "user", "content": "Hello, Gemini Live 2.5 Flash Native Audio!"}],
)

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-live-2-5-flash-native-audio",
    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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Explore similar models

Gemini Live 2.5 Flash Native Audio 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.

FAQ

How much does Gemini Live 2.5 Flash Native Audio cost?

Input is priced at $0.500 per 1M tokens and output at $2.00 per 1M tokens (Google Vertex AI, last verified Oct 22, 2026).

What is the context window of Gemini Live 2.5 Flash Native Audio?

Gemini Live 2.5 Flash Native Audio supports a 131,072-token context window with up to 65,536 output tokens.

Does Gemini Live 2.5 Flash Native Audio support function calling?

Yes — Gemini Live 2.5 Flash Native Audio supports function (tool) calling, including parallel tool calls.

Is Gemini Live 2.5 Flash Native Audio good for production?

Gemini Live 2.5 Flash Native Audio is well-suited for long-context work — top-4 largest context window across 45 realtime models and long-form generation — 65,536-token max output, top-0% of peers. 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 Live 2.5 Flash Native Audio 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 Live 2.5 Flash Native Audio

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.