Gemini 3 Pro Image
Databricks chatGemini 3 Pro Image is a Databricks chat model.It supports a 65,536-token context windowwith up to 32,768 output tokens. Capabilities include vision. Route Gemini 3 Pro Image via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
We don't have verified per-token pricing for Gemini 3 Pro Image yet. If you have a source from Databricks's documentation, help us add it — your submission gets reviewed within 48 hours.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.
| Input | — | |
| Output | — |
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 — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Strengths & caveats — when to pick Gemini 3 Pro Image
Data-driven from Gemini 3 Pro Image's percentile within chat peers.
Where it's strong
- +document, chart, and screenshot understanding
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
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Gemini 3 Pro Image yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Gemini 3 Pro Image 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 3 Pro Image 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="databricks/databricks-gemini-3-pro-image",
messages=[{"role": "user", "content": "Hello, Gemini 3 Pro Image!"}],
)
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="databricks/databricks-gemini-3-pro-image",
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 3 Pro Image 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 3.5 Flash LiteDatabricks · $0.375/M in · $3.13/M out · 1,048,576 ctxView model →
- Gemini 3.1 ProDatabricks · $2.50/M in · $15.00/M out · 1,048,576 ctxView model →
- Gemini 3.5 FlashDatabricks · $1.88/M in · $11.25/M out · 1,048,576 ctxView model →
- Gemini 3.6 FlashDatabricks · $1.88/M in · $9.38/M out · 1,048,576 ctxView model →
FAQ
How much does Gemini 3 Pro Image cost?
Public per-token pricing for Gemini 3 Pro Image is not yet published. Submit a source on this page to help us add it.
What is the context window of Gemini 3 Pro Image?
Gemini 3 Pro Image supports a 65,536-token context window with up to 32,768 output tokens.
Does Gemini 3 Pro Image support function calling?
Gemini 3 Pro Image 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 3 Pro Image good for production?
Gemini 3 Pro Image is well-suited for document, chart, and screenshot understanding. 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 Gemini 3 Pro Image 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 3 Pro Image
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.