Low 1024 X 1024 GPT Image 1
Azure OpenAI image generationLow 1024 X 1024 GPT Image 1 is an Azure OpenAI image generation model. Route Low 1024 X 1024 GPT Image 1 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 Low 1024 X 1024 GPT Image 1 yet. If you have a source from Azure OpenAI'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
- —
- 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 Low 1024 X 1024 GPT Image 1 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Low 1024 X 1024 GPT Image 1 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.# Low 1024 X 1024 GPT Image 1 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="azure-openai/low-1024-x-1024-gpt-image-1",
messages=[{"role": "user", "content": "Hello, Low 1024 X 1024 GPT Image 1!"}],
)
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="azure-openai/low-1024-x-1024-gpt-image-1",
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
Low 1024 X 1024 GPT Image 1 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 Low 1024 X 1024 GPT Image 1 cost?
Public per-token pricing for Low 1024 X 1024 GPT Image 1 is not yet published. Submit a source on this page to help us add it.
What is the context window of Low 1024 X 1024 GPT Image 1?
Context window for Low 1024 X 1024 GPT Image 1 is not currently public.
Does Low 1024 X 1024 GPT Image 1 support function calling?
Low 1024 X 1024 GPT Image 1 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 Low 1024 X 1024 GPT Image 1 good for production?
Low 1024 X 1024 GPT Image 1 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 Low 1024 X 1024 GPT Image 1 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 Low 1024 X 1024 GPT Image 1
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.