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GPT-4 Turbo vs GPT-5.5

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GPT-4 Turbo and GPT-5.5.

OpenAI

GPT-4 Turbo

The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling. Training data: up to December 2023.

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OpenAI

GPT-5.5

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...

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Technical Specifications

SpecificationGPT-4 TurboGPT-5.5
ProviderOpenAIOpenAI
Context Window128,000 tokens1,050,000 tokens
Agent SuitabilityN/A95/100
Time to First Token (TTFT)N/A380 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-04-092026-04-24

API Pricing Comparison

Input Price per Million Tokens

GPT-4 Turbo

$10.00

GPT-5.5

$5.00

Output Price per Million Tokens

GPT-4 Turbo

$30.00

GPT-5.5

$30.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8640.0%vs9420.0%
GPT-4 Turbo
GPT-5.5
HumanEvalPython coding & logic synthesis
8800.0%vs9680.0%
GPT-4 Turbo
GPT-5.5
MATHComplex mathematical problem solving
7300.0%vs9350.0%
GPT-4 Turbo
GPT-5.5
GPQAGraduate-level expert reasoning
5000.0%vs8420.0%
GPT-4 Turbo
GPT-5.5
HellaSwagCommonsense reasoning and inference
8600.0%vs9900.0%
GPT-4 Turbo
GPT-5.5
MT-BenchMulti-turn conversation flow quality
880.0%vs970.0%
GPT-4 Turbo
GPT-5.5

GPT-4 Turbo Quirks & Gotchas

No developer gotchas reported.

GPT-5.5 Quirks & Gotchas

  • Best for JSON schema adherence — strict mode available via response_format parameter
  • Requires explicit tool_choice for deterministic function calling