GPT-5 Chat
OpenAI chat Deprecated 104d agomodel fallback routing to swap models without code changes.
GPT-5 Chat is an OpenAI chat model.It supports a 128,000-token context windowwith up to 16,384 output tokens.Input is priced at $1.25/M tokens and output at $10.00/M tokens. Capabilities include vision, reasoning, prompt caching. Route GPT-5 Chat via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate GPT-5 Chat spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $1.25/M input · $10.00/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 | $1.25/M | |
| Output | $10.00/M | |
| Blended (3:1 in:out) | $3.44/M Cross-model comparable price | |
| Cached input | $0.125/M |
Context & limits
- Context window
- 128,000 tokens
- Max input
- 128,000 tokens
- Max output
- 16,384 tokens
- Modalities
- vision, pdf, text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input ✓ supported
- Audio input — not advertised
- Audio output — not advertised
- PDF input ✓ supported
- Streaming ✓ supported
- Structured output ✓ supported
- Prompt caching ✓ supported
- Reasoning ✓ supported
Strengths & caveats — when to pick GPT-5 Chat
Data-driven from GPT-5 Chat's percentile within chat peers.
Where it's strong
- +pricing — cheaper than 76% of priced chat models on Future AGI
- +PDF input — only 18% of chat models on Future AGI advertise this
Watch out for
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !already deprecated — provider stopped accepting new traffic 104 days ago
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call GPT-5 Chat 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.# GPT-5 Chat 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="openai/gpt-5-chat",
messages=[{"role": "user", "content": "Hello, GPT-5 Chat!"}],
)
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="openai/gpt-5-chat",
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 ↗Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
- model: gpt-5-chat
provider: openai
weight: 80
fallbacks:
- model: gemini-3-pro-preview
provider: google
- model: gpt-5-6-sol
provider: azure-openai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Same model on other providers
gpt-5-chat is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Azure OpenAI | $1.25/M | $10.00/M | Oct 22, 2026 |
Explore similar models
Grouped by Chatbot Arena tier (GPT-5 Chat sits at 1426 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- Gemini 3 Pro PreviewGoogle AI · $2.00/M in · $12.00/M out · 1,048,576 ctxView model →
- GPT-5.6 SolAzure OpenAI · $4.00/M in · $20.00/M out · 922,000 ctxView model →
- Gemini 3.6 FlashGoogle Vertex AI · $0.750/M in · $3.75/M out · 1,048,576 ctxView model →
- GPT-6.1 SolAzure AI Foundry · $2.00/M in · $10.00/M out · 922,000 ctxView model →
≥30 ELO higher
- Gemini 4 ArgonGoogle AI · $2.00/M in · $10.00/M out · 1,048,576 ctxView model →
- Claude Opus 5.5Azure AI Foundry · $4.00/M in · $20.00/M out · 1,000,000 ctxView model →
- Claude Fable 5Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctxView model →
- Claude Fable 5.1Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctxView model →
25–100 ELO lower, ≤50% of price
- GPT-5 nanoAzure OpenAI · $0.0500/M in · $0.400/M out · 272,000 ctxView model →
- GPT-5 nano (2025-08-07)Azure OpenAI · $0.0500/M in · $0.400/M out · 272,000 ctxView model →
- Gemini 2.0 FlashGoogle AI · $0.1000/M in · $0.400/M out · 1,048,576 ctxView model →
- DeepSeek V3DeepSeek · $0.270/M in · $1.10/M out · 65,536 ctxView model →
FAQ
How much does GPT-5 Chat cost?
Input is priced at $1.25 per 1M tokens and output at $10.00 per 1M tokens (OpenAI, last verified Oct 22, 2026).
What is the context window of GPT-5 Chat?
GPT-5 Chat supports a 128,000-token context window with up to 16,384 output tokens.
Does GPT-5 Chat support function calling?
GPT-5 Chat 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 GPT-5 Chat good for production?
GPT-5 Chat is well-suited for pricing — cheaper than 76% of priced chat models on Future AGI and PDF input — only 18% of chat models on Future AGI advertise this. Consider alternatives if you need agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback.
How can I route to GPT-5 Chat 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 GPT-5 Chat
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Start here — vendor docs are the only authoritative spec.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.