Gemini exp 1206

Google AI chat

Gemini exp 1206 is a Google AI chat model.It supports a 2,097,152-token context windowwith up to 8,192 output tokens. Capabilities include function calling, vision. Route Gemini exp 1206 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: unknown Last verified: May 12, 2026 View source ↗
Pricing not yet public

We don't have verified per-token pricing for Gemini exp 1206 yet. If you have a source from Google AI's documentation, help us add it — your submission gets reviewed within 48 hours.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.

Input
Output

Limits

Context window
2,097,152 tokens
Max input
2,097,152 tokens
Max output
8,192 tokens
Modalities
vision, text

Capabilities

  • Function calling ✓ supported
  • 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 — not advertised
  • Reasoning — not advertised

Where it's strong

  • +long-context work — top-4 largest context window across 1913 chat models

Watch out for

  • No major caveats flagged from public spec.

Benchmarks pending

We haven't logged public benchmark scores for Gemini exp 1206 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Gemini exp 1206 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 exp 1206 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-exp-1206",
    messages=[{"role": "user", "content": "Hello, Gemini exp 1206!"}],
)

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-exp-1206",
    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 ↗

Compare with similar models

Gemini exp 1206 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Gemini exp 1206 cost?

Public per-token pricing for Gemini exp 1206 is not yet published. Submit a source on this page to help us add it.

What is the context window of Gemini exp 1206?

Gemini exp 1206 supports a 2,097,152-token context window with up to 8,192 output tokens.

Does Gemini exp 1206 support function calling?

Yes — Gemini exp 1206 supports function (tool) calling.

Is Gemini exp 1206 good for production?

Gemini exp 1206 is well-suited for long-context work — top-4 largest context window across 1913 chat models.

How can I route to Gemini exp 1206 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 exp 1206

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