Sd3.5 Large Turbo
Stability AI image generationSd3.5 Large Turbo is a Stability AI image generation model. Route Sd3.5 Large Turbo via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Sd3.5 Large Turbo spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.000000/M input · $0.000000/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 12, 2026.
| Input | — | |
| Output | — | |
| Per image | $0.0400 |
Limits
- Context window
- —
- Max input
- —
- Max output
- —
- Modalities
- image, text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input — not advertised
- 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
Benchmarks pending
We haven't logged public benchmark scores for Sd3.5 Large Turbo yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Sd3.5 Large Turbo 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.# Sd3.5 Large Turbo 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="stability-ai/sd3-5-large-turbo",
messages=[{"role": "user", "content": "Hello, Sd3.5 Large Turbo!"}],
)
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="stability-ai/sd3-5-large-turbo",
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 ↗Compare with similar models
Sd3.5 Large Turbo 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 Sd3.5 Large Turbo cost?
Input is priced at $0.000000 per 1M tokens and output at $0.000000 per 1M tokens (Stability AI, last verified May 12, 2026).
What is the context window of Sd3.5 Large Turbo?
Context window for Sd3.5 Large Turbo is not currently public.
Does Sd3.5 Large Turbo support function calling?
Sd3.5 Large Turbo 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 Sd3.5 Large Turbo good for production?
Sd3.5 Large Turbo is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to Sd3.5 Large Turbo 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 Sd3.5 Large Turbo
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.