Deep Research Pro preview 12 2025
Google Vertex AI chatDeep Research Pro preview 12 2025 is a Google Vertex AI chat model.It supports a 65,536-token context windowwith up to 32,768 output tokens.Input is priced at $2.00/M tokens and output at $12.00/M tokens. Capabilities include vision, prompt caching. Route Deep Research Pro preview 12 2025 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Deep Research Pro preview 12 2025 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $2.00/M input · $12.00/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 | $2.00/M | |
| Output | $12.00/M | |
| Blended (3:1 in:out) | $4.50/M Cross-model comparable price | |
| Cached input | $0.200/M | |
| Batch input | $1.00/M | |
| Batch output | $6.00/M | |
| Per image | $0.001100 |
Context & limits
- Context window
- 65,536 tokens
- Max input
- 65,536 tokens
- Max output
- 32,768 tokens
- Modalities
- vision, text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input ✓ supported
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output ✓ supported
- Prompt caching ✓ supported
- Reasoning — not advertised
Strengths & caveats — when to pick Deep Research Pro preview 12 2025
Data-driven from Deep Research Pro preview 12 2025's percentile within chat peers.
Where it's strong
- +pricing — cheaper than 81% of priced chat models on Future AGI
Watch out for
- !limited context — 65,536-token window is in the bottom quartile; not ideal for long documents or large RAG
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
Benchmarks pending
We haven't logged public benchmark scores for Deep Research Pro preview 12 2025 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Deep Research Pro preview 12 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.# Deep Research Pro preview 12 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/deep-research-pro-preview-12-2025",
messages=[{"role": "user", "content": "Hello, Deep Research Pro preview 12 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/deep-research-pro-preview-12-2025",
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 ↗Same model on other providers
deep-research-pro-preview-12-2025 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 | $2.00/M | $12.00/M | Oct 22, 2026 |
Explore similar models
Deep Research Pro preview 12 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.
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FAQ
How much does Deep Research Pro preview 12 2025 cost?
Input is priced at $2.00 per 1M tokens and output at $12.00 per 1M tokens (Google Vertex AI, last verified Oct 22, 2026).
What is the context window of Deep Research Pro preview 12 2025?
Deep Research Pro preview 12 2025 supports a 65,536-token context window with up to 32,768 output tokens.
Does Deep Research Pro preview 12 2025 support function calling?
Deep Research Pro preview 12 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 Deep Research Pro preview 12 2025 good for production?
Deep Research Pro preview 12 2025 is well-suited for pricing — cheaper than 81% of priced chat models on Future AGI. Consider alternatives if you need limited context — 65,536-token window is in the bottom quartile; not ideal for long documents or large RAG.
How can I route to Deep Research Pro preview 12 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 Deep Research Pro preview 12 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.