GPT-4
Up to 30%Deprecated 23 Oct 2026Call GPT-4 through LLM.API when you want OpenAI-family text generation with unified auth, provider choice, and production-friendly defaults.
What is GPT-4?
GPT-4 is an OpenAI chat model exposed on LLM.API under id `gpt-4`. Original GPT-4 model with strong reasoning and tool use capabilities. Teams use it when they need reliable text generation with text, image inputs and text outputs over an OpenAI-compatible API.
Providers
LLM.API routes GPT-4 to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| — | 8K tokens | vision, tools, streaming, JSON |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test GPT-4 right here — free to start.
Suggestions for your first prompt
Code snippet
Call GPT-4 through the OpenAI-compatible API — POST /v1/chat/completions.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
)
print(resp.choices[0].message.content){
"model": "gpt-4",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Instruction following
Follows detailed system and user instructions with strong adherence to format and tone. Grounded in the model's chat role rather than generic chat claims.
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces. Reflects OpenAI positioning for this endpoint.
Tool-ready dialogue
Works well in agent loops that call functions, browsers, or retrieval APIs.
Multi-step reasoning
Breaks down complex problems into intermediate steps before answering. Grounded in the model's chat role rather than generic chat claims.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Grounded in the model's chat role rather than generic chat claims.
6 Most Valuable Use Cases
- Sales and success email drafting with CRM context with GPT-4
- Internal knowledge assistants grounded with your retrieval layer
- Data extraction into JSON for downstream systems with GPT-4
- Product analytics narration and anomaly explanations
- Policy Q&A bots with careful refusal behavior with GPT-4
- Coding agents for refactors, tests, and PR explanations
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run GPT-4 on LLM.API?
Unified AI Routing
Reach GPT-4 and sibling models through one OpenAI-compatible endpoint.
Cost Control
Practical: Compare provider price points and keep spend visible as you scale GPT-4.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Practical: Trace prompts, tokens, and errors for GPT-4 alongside the rest of your stack.
Drop-in SDKs
Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Swap GPT-4 for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You need provider failover options exposed for this model id (GPT-4)
- You want OpenAI-compatible chat completions through a single LLM.API key (GPT-4)
- You need a general-purpose text model for assistants, agents, or content workflows (GPT-4)
- Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (GPT-4)
Avoid if...
- You need guaranteed real-time hard latency SLAs without benchmarking the provider
- You need pure embedding, OCR, or media generation instead of chat
- Your use case depends on unpublished proprietary benchmarks not listed here
- You require on-prem only deployment with no cloud inference
BENCHMARKS
GPT-4 benchmark scores
Intelligence index
Scale: 0-100 index points
Reference price per 1M tokens
Bars compare input and output list prices for this model.
Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.
COMMUNITY
What developers say about OpenAI models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to GPT-4.
- Long-horizon coding reports on GPT-6 Astra describe a clear step up over the GPT-5.x line, with the higher reasoning tiers seen as the sweet spot for planning and implementation in large (100K+ LOC) codebases.
- The same field reports note that fast/ultra modes burn quota quickly and that very large refactors still stall, so teams tend to mix a cheap tier for routine calls with a reasoning tier for hard steps.
- Community threads temper the hype: capability gains are acknowledged, but developers still report the usual failure modes on obscure reverse-engineering and modding work.
SOURCES
Frequently Asked Questions
Is GPT-4 a chat model?
Yes—GPT-4 is exposed as a chat/completions-style endpoint on LLM.API.
Can I use tools or structured outputs with GPT-4?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
What are limitations of GPT-4?
Like other API models, GPT-4 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Where is the canonical page for GPT-4?
https://llmapi.ai/models/openai-gpt-4/
What modalities does GPT-4 support?
GPT-4 accepts text, image and produces text according to its architecture metadata on LLM.API.
How is GPT-4 priced on LLM.API?
Listed pricing metadata shows: In $30.00 / 1M tokens · Out $60.00 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
What is the context length for GPT-4?
Reported context for GPT-4 is 8K tokens. Always verify the active provider row if multiple providers are listed.
How do I call GPT-4 via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "gpt-4" and your LLM.API key. See the code snippet on this page.
Does GPT-4 support streaming?
Yes—at least one listed provider advertises streaming.
What is GPT-4?
Original GPT-4 model with strong reasoning and tool use capabilities. On LLM.API it is addressed as `gpt-4`.
Which providers serve GPT-4?
LLM.API currently lists: openai. Availability can vary by region and account.
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