Specifications
Context Window
128K tokens
Input Price/1M
$10.00
Output Price/1M
$30.00
Parameters
—
Speed
30 tok/s
Latency (TTFT)
1.5s
Max Output
4K tokens
Benchmarks
GPT-4 Turbo results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SciCode | 32.0 | 100.0 | — |
| LiveCodeBench | 29.0 | 100.0 | — |
| AA Coding Index | 21.5 | 100.0 | — |
Knowledge
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 69.0 | 100.0 | — |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 100.0 | — |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| HLE | 3.0 | 100.0 | — |
Information
- Release date
- November 06, 2023
- Tool Calling
- ✅ Supported
- Vision
- ✅ Supported
- Audio
- ❌ Not supported
Full Analysis: GPT-4 Turbo
What is GPT-4 Turbo?
GPT-4 Turbo is an AI model developed by OpenAI, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As a proprietary model, it is available via OpenAI's cloud API. With a context window of 128K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Pricing & Costs in 2026
GPT-4 Turbo is usage-based, priced at $10/1M input tokens and $30/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The premium pricing reflects the model's frontier capabilities, recommended for complex reasoning, analysis, and generation tasks where quality takes priority over cost.
Benchmarks & Performance
GPT-4 Turbo was evaluated on 6 different benchmarks, covering categories like Coding, Knowledge, overall, Reasoning. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
GPT-4 Turbo is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), image and visual document analysis (OCR, diagrams, screenshots), multimodal processing combining text and images, text generation, summarization, translation, and general assistance.
Comparison with Alternatives
In the 2026 AI model ecosystem, GPT-4 Turbo competes directly with similarly capable models. Key competitors include Claude (Anthropic), Gemini (Google), and open source models like Llama (Meta) and Qwen (Alibaba). The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is GPT-4 Turbo?
GPT-4 Turbo is an AI model developed by OpenAI. It is a language model (LLM), with multimodal support (text, image and more).
How much does GPT-4 Turbo cost?
GPT-4 Turbo costs $10/1M input tokens and $30/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does GPT-4 Turbo compare with other models?
In available benchmarks, GPT-4 Turbo scored: SciCode: 32/100, LiveCodeBench: 29/100, AA Coding Index: 21.5/100. See the full table above for a detailed comparison.
Is GPT-4 Turbo open source?
No, GPT-4 Turbo is a proprietary model from OpenAI. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is GPT-4 Turbo best for?
GPT-4 Turbo excels at multimodal tasks including text and vision. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: June 01, 2026 • View methodology →