GPT-5.5 is OpenAI's most capable model, released on April 23, 2026. Codenamed "Spud," it is the first fully retrained base model since GPT-4.5 — all prior GPT-5 releases were post-training iterations on the same foundation. GPT-5.5 features a natively omnimodal architecture, a 1-million-token context window, and leads on 14 benchmarks at launch.
- Rebuilt Foundation: First complete base model retraining since GPT-4.5, delivering fundamental capability improvements rather than incremental post-training gains across reasoning, coding, and knowledge tasks.
- Natively Omnimodal: A single parameter pool handles text, images, audio, and video — enabling seamless cross-modal reasoning without separate encoder modules.
- 60% Hallucination Reduction: Achieves a 60% reduction in hallucination rate compared to GPT-5.4, and uses approximately 40% fewer output tokens on equivalent Codex tasks, improving both reliability and cost efficiency.
- Frontier Benchmark Performance: 88.7% on SWE-bench Verified, 92.4% on MMLU, 93.6% on GPQA Diamond, and 85.0% on ARC-AGI-2 (an 11.7-point jump over GPT-5.4).
- Complex Software Engineering: With 88.7% on SWE-bench Verified and strong tool-use integration, GPT-5.5 excels at end-to-end coding tasks including debugging, refactoring, and multi-file changes.
- Multimodal Workflows: The natively omnimodal architecture makes it uniquely suited for tasks that span text, images, audio, and video — such as analyzing meeting recordings, processing documents with figures, or building multimedia applications.
- Research and Analysis: Top scores on GPQA Diamond (93.6%) and MMLU (92.4%) make it the strongest choice for PhD-level science questions, complex reasoning, and knowledge-intensive research tasks.
| Capability | Description |
|---|
| Reasoning | 93.6% GPQA Diamond, 85.0% ARC-AGI-2, medium reasoning effort by default |
| Coding | 88.7% SWE-bench Verified; 40% fewer output tokens on Codex tasks vs GPT-5.4 |
| Multimodal | Natively omnimodal: text, image, audio, video in a single architecture |
| Response Speed | Medium reasoning effort by default; configurable for latency-sensitive workloads |
| Context Window | 1,050,000 tokens (2x pricing for input beyond 272K tokens) |
| Max Output | 128,000 tokens |
| Tool Use | Full function calling, tool search, hosted tools, prompt caching, and compaction support |
| Multilingual | Broad multilingual support across major languages |
- Significantly more expensive than open-source alternatives — roughly 50x the cost of DeepSeek-V4-Pro for input tokens.
- Lost the harder SWE-Bench Pro benchmark to Claude Opus 4.7 despite winning the standard SWE-Bench Verified headline.
- 2x input pricing for prompts exceeding 272K tokens increases costs substantially for long-context workloads.
| Model | Input (Credits/Token) | Output (Credits/Token) | Notes |
|---|
| GPT-5.5 | 5.00 | 30.00 | 2x input / 1.5x output beyond 272K tokens |
[1] OpenAI GPT-5.5 Announcement
[2] OpenAI API Pricing
