All Models/GPT-4 Turbo
Benchmark:63.8
Speed:Standard
Training Cutoff:2023-12

Specs & Pricing

API pricing per 1M tokens and core technical specifications
📥
Input / 1M
$10.0000
📤
Output / 1M
$30.0000
⚡
Cache Price / 1M
-
📏
Context Window
128K
128,000 tokens
📝
Max Output
4K
4,096 tokens
🎯
Benchmark & Speed
63.8
Speed:Standard

Cost Calculator

Simulate estimated API usage cost based on your specific traffic and context size

Calculation Settings

Pricing Tier
▶ Detailed Settings
▶ Presets

Simulation Results

Input Cost$0.20
Output Cost$0.09
Total$0.29

Calculation Details

INPUT
10req × (2,000 / 1M) × $10 = $0.20
OUTPUT
10req × (300 / 1M) × $30 = $0.09

Alternative Models

Models with comparable benchmark score and pricing tier
ModelProviderContext WindowMax OutputTraining CutoffInput / 1MCache Price / 1MOutput / 1MBenchmarkSpeedTotal
OpenAI👑
1M128K2026-02$5.00$0.50$30.0097Standard$0.1498
OpenAI👑
1M128K2025-12$5.00$0.50$30.0093.25Standard$0.1439
OpenAI
200K100K2024-06$10.00$2.50$40.0077.5Very Slow$0.2432
xAI
131K--$3.00$0.75$15.0069.25Standard$0.0820
Anthropic
1M64K2025-03$3.00$0.30$15.0068.5Fast$0.0809

Provider

OpenAI👑

Benchmark

97

Speed

Standard

Training Cutoff

2026-02

Context Window

1M

Max Output

128K

Input / 1M

$5.00

Cache Price / 1M

$0.50

Output / 1M

$30.00

Total(試算)
In: $0.0488 / Out: $0.09
$0.1498

Provider

OpenAI👑

Benchmark

93.25

Speed

Standard

Training Cutoff

2025-12

Context Window

1M

Max Output

128K

Input / 1M

$5.00

Cache Price / 1M

$0.50

Output / 1M

$30.00

Total(試算)
In: $0.0488 / Out: $0.09
$0.1439

Provider

OpenAI

Benchmark

77.5

Speed

Very Slow

Training Cutoff

2024-06

Context Window

200K

Max Output

100K

Input / 1M

$10.00

Cache Price / 1M

$2.50

Output / 1M

$40.00

Total(試算)
In: $0.0976 / Out: $0.1200
$0.2432

Provider

xAI

Benchmark

69.25

Speed

Standard

Training Cutoff

-

Context Window

131K

Max Output

-

Input / 1M

$3.00

Cache Price / 1M

$0.75

Output / 1M

$15.00

Total(試算)
In: $0.0293 / Out: $0.045
$0.0820

Provider

Anthropic

Benchmark

68.5

Speed

Fast

Training Cutoff

2025-03

Context Window

1M

Max Output

64K

Input / 1M

$3.00

Cache Price / 1M

$0.30

Output / 1M

$15.00

Total(試算)
In: $0.0293 / Out: $0.045
$0.0809
⚡ Main Feature

All AI Models Pricing & Cost Simulator

Compare cutting-edge models from OpenAI, Anthropic, Google, and DeepSeek. Accurately simulate real-world API costs with prompt caching and custom context length.

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GPT-4 Turbo Frequently Asked Questions (FAQ)

Key insights into GPT-4 Turbo API pricing, context capacity, caching savings, and performance benchmarks

Q. What is the API pricing for GPT-4 Turbo? How much does it cost per 1M tokens?+

GPT-4 Turbo (OpenAI) is priced at $10.0000 per 1 million input tokens and $30.0000 per 1 million output tokens. The output rate is approximately 3.0x higher than the input rate. You can simulate your exact monthly API expenses tailored to your request volume and token length using the calculator above.

Q. What are the context window and maximum output limits of GPT-4 Turbo?+

GPT-4 Turbo features a 128K context window and a maximum output limit of 4K tokens per request. It provides sufficient capacity for standard chat interactions and structured data processing.

Q. What are the best practices for optimizing API costs with GPT-4 Turbo?+

Because output tokens ($30.0000/1M) cost significantly more than inputs, always configure a strict max_tokens ceiling and employ structured output schemas. Additionally, pairing GPT-4 Turbo with lightweight models for routine tasks via multi-LLM routing can substantially lower total infrastructure spend.

Q. What is the benchmark score (63.75) and performance profile of GPT-4 Turbo?+

GPT-4 Turbo achieves an overall benchmark score of 63.75/100 with a "Standard" speed rating. It is optimized for high-throughput, low-latency applications requiring budget-conscious performance.

Q. Which alternative models are comparable to GPT-4 Turbo?+

Key alternatives in similar price-to-performance tiers include GPT-5.6 Sol, GPT-5.5, o3-deep-research. We recommend comparing them based on your latency targets, context window needs, and prompt caching benefits using the full comparison tool on this site.