Gemini 2.5 Pro preview 05 06

Google Vertex AI chat Deprecated 337d ago
Heads-up: Google Vertex AI has scheduled Gemini 2.5 Pro preview 05 06 for deprecation on Dec 2, 2025. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

Gemini 2.5 Pro preview 05 06 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 $1.25/M tokens and output at $10.00/M tokens. Capabilities include function calling, vision, reasoning, prompt caching. Route Gemini 2.5 Pro preview 05 06 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 7, 2026 View source ↗
Cost calculator

Estimate Gemini 2.5 Pro preview 05 06 spend

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

~3K in / ~400 out · 5K req/day
3,000
01,048,576
400
065,535
5,000
01,000,000
cached @ $0.1250/M
Per request
$0.007750
in $0.003750 · out $0.004000
Per day
$38.75
5,000 requests
Per month
$1,179
152,188 requests

Estimate uses $1.25/M input · $10.00/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 May 7, 2026.

Input $1.25/M
Output $10.00/M
Blended (3:1 in:out) $3.44/M
Cross-model comparable price
Cached input $0.125/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 Pro preview 05 06

Data-driven from Gemini 2.5 Pro preview 05 06's percentile within chat peers.

Where it's strong

  • +pricing — cheaper than 76% of priced chat models on Future AGI
  • +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

  • !already deprecated — provider stopped accepting new traffic 337 days ago

Benchmarks pending

We haven't logged public benchmark scores for Gemini 2.5 Pro preview 05 06 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Gemini 2.5 Pro preview 05 06 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 2.5 Pro preview 05 06 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-pro-preview-05-06",
    messages=[{"role": "user", "content": "Hello, Gemini 2.5 Pro preview 05 06!"}],
)

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-pro-preview-05-06",
    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 ↗

Same model on other providers

gemini-2-5-pro-preview-05-06 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Google AI$1.25/M$10.00/MMay 7, 2026

Explore similar models

Gemini 2.5 Pro preview 05 06 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 2.5 Pro preview 05 06 cost?

Input is priced at $1.25 per 1M tokens and output at $10.00 per 1M tokens (Google Vertex AI, last verified May 7, 2026).

What is the context window of Gemini 2.5 Pro preview 05 06?

Gemini 2.5 Pro preview 05 06 supports a 1,048,576-token context window with up to 65,535 output tokens.

Does Gemini 2.5 Pro preview 05 06 support function calling?

Yes — Gemini 2.5 Pro preview 05 06 supports function (tool) calling, including parallel tool calls.

Is Gemini 2.5 Pro preview 05 06 good for production?

Gemini 2.5 Pro preview 05 06 is well-suited for pricing — cheaper than 76% of priced chat models on Future AGI and long-context tasks — context window in the top 2% of peers. Consider alternatives if you need already deprecated — provider stopped accepting new traffic 337 days ago.

How can I route to Gemini 2.5 Pro preview 05 06 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 Pro preview 05 06

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