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 context window (922K input, 128K output) with support for text and image inputs, enabling large-scale reasoning, coding, and multimodal workflows within a single system.
Modalities
In / Out Price
$2.50 / $15per 1M
Context
1.1M
Released
Apr 24, 2026
Knowledge Cutoff
Dec 2025
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows where this model lands among all models on OpenRouter.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | GPT-5.5 (Non-reasoning) Coding Index | 56.5 |
| Artificial Analysis | GPT-5.5 (Non-reasoning) GPQA Diamond | 76.8% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) HLE | 13.7% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) IFBench | 46.1% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) τ²-Bench Telecom | 69.3% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) AA-LCR | 64.0% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) GDPval-AA | 27.7% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) CritPt | 1.4% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) Terminal-Bench Hard | 49.2% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) AA-Omniscience Accuracy | 45.6% |
| Artificial Analysis | GPT-5.5 (Non-reasoning) AA-Omniscience Non-Hallucination Rate | 7.3% |
| Artificial Analysis | GPT-5.5 (low) Coding Index | 60.9 |
| Artificial Analysis | GPT-5.5 (low) GPQA Diamond | 91.0% |
| Artificial Analysis | GPT-5.5 (low) HLE | 32.7% |
| Artificial Analysis | GPT-5.5 (low) IFBench | 64.4% |
| Artificial Analysis | GPT-5.5 (low) τ²-Bench Telecom | 83.9% |
| Artificial Analysis | GPT-5.5 (low) AA-LCR | 81.0% |
| Artificial Analysis | GPT-5.5 (low) GDPval-AA | 30.6% |
| Artificial Analysis | GPT-5.5 (low) CritPt | 8.0% |
| Artificial Analysis | GPT-5.5 (low) Terminal-Bench Hard | 52.3% |
| Artificial Analysis | GPT-5.5 (low) AA-Omniscience Accuracy | 54.8% |
| Artificial Analysis | GPT-5.5 (low) AA-Omniscience Non-Hallucination Rate | 12.0% |
| Artificial Analysis | GPT-5.5 (medium) Intelligence Index | 34.2 |
| Artificial Analysis | GPT-5.5 (medium) Coding Index | 71.5 |
| Artificial Analysis | GPT-5.5 (medium) Agentic Index | 30.6 |
| Artificial Analysis | GPT-5.5 (medium) GPQA Diamond | 92.6% |
| Artificial Analysis | GPT-5.5 (medium) HLE | 42.4% |
| Artificial Analysis | GPT-5.5 (medium) IFBench | 71.0% |
| Artificial Analysis | GPT-5.5 (medium) τ²-Bench Telecom | 91.8% |
| Artificial Analysis | GPT-5.5 (medium) AA-LCR | 83.0% |
| Artificial Analysis | GPT-5.5 (medium) GDPval-AA | 39.4% |
| Artificial Analysis | GPT-5.5 (medium) CritPt | 18.6% |
| Artificial Analysis | GPT-5.5 (medium) SciCode | 54.5% |
| Artificial Analysis | GPT-5.5 (medium) Terminal-Bench Hard | 57.6% |
| Artificial Analysis | GPT-5.5 (medium) AA-Omniscience Accuracy | 56.8% |
| Artificial Analysis | GPT-5.5 (medium) AA-Omniscience Non-Hallucination Rate | 10.4% |
| Artificial Analysis | GPT-5.5 (high) Intelligence Index | 37.3 |
| Artificial Analysis | GPT-5.5 (high) Coding Index | 71.6 |
| Artificial Analysis | GPT-5.5 (high) Agentic Index | 35.4 |
| Artificial Analysis | GPT-5.5 (high) GPQA Diamond | 93.2% |
| Artificial Analysis | GPT-5.5 (high) HLE | 45.0% |
| Artificial Analysis | GPT-5.5 (high) IFBench | 71.6% |
| Artificial Analysis | GPT-5.5 (high) τ²-Bench Telecom | 93.0% |
| Artificial Analysis | GPT-5.5 (high) AA-LCR | 84.3% |
| Artificial Analysis | GPT-5.5 (high) GDPval-AA | 43.6% |
| Artificial Analysis | GPT-5.5 (high) CritPt | 25.4% |
| Artificial Analysis | GPT-5.5 (high) SciCode | 56.1% |
| Artificial Analysis | GPT-5.5 (high) Terminal-Bench Hard | 59.8% |
| Artificial Analysis | GPT-5.5 (high) AA-Omniscience Accuracy | 57.0% |
| Artificial Analysis | GPT-5.5 (high) AA-Omniscience Non-Hallucination Rate | 10.9% |
| Artificial Analysis | GPT-5.5 (xhigh) Intelligence Index | 38.6 |
| Artificial Analysis | GPT-5.5 (xhigh) Coding Index | 74.9 |
| Artificial Analysis | GPT-5.5 (xhigh) Agentic Index | 37.3 |
| Artificial Analysis | GPT-5.5 (xhigh) GPQA Diamond | 93.5% |
| Artificial Analysis | GPT-5.5 (xhigh) HLE | 45.8% |
| Artificial Analysis | GPT-5.5 (xhigh) IFBench | 75.9% |
| Artificial Analysis | GPT-5.5 (xhigh) τ²-Bench Telecom | 93.9% |
| Artificial Analysis | GPT-5.5 (xhigh) AA-LCR | 84.3% |
| Artificial Analysis | GPT-5.5 (xhigh) GDPval-AA | 44.8% |
| Artificial Analysis | GPT-5.5 (xhigh) CritPt | 27.1% |
| Artificial Analysis | GPT-5.5 (xhigh) SciCode | 55.8% |
| Artificial Analysis | GPT-5.5 (xhigh) Terminal-Bench Hard | 60.6% |
| Artificial Analysis | GPT-5.5 (xhigh) AA-Omniscience Accuracy | 58.0% |
| Artificial Analysis | GPT-5.5 (xhigh) AA-Omniscience Non-Hallucination Rate | 11.0% |
| Design Arena | GPT-5.5 Agents Arena Agenticgamedev Elo | 1179 |
| Design Arena | GPT-5.5 Agents Arena Agentichtmlslides Elo | 1084 |
| Design Arena | GPT-5.5 Agents Arena Agenticslides Elo | 1150 |
| Design Arena | GPT-5.5 Agents Arena Agenticslides(Html) Elo | 1077 |
| Design Arena | GPT-5.5 Agents Arena Agenticslides(Python-Pptx) Elo | 1155 |
| Design Arena | GPT-5.5 Agents Arena Androidnative Elo | 1176 |
| Design Arena | GPT-5.5 Agents Arena Full Stack Elo | 1099 |
| Design Arena | GPT-5.5 Agents Arena Godotgamedev Elo | 1210 |
| Design Arena | GPT-5.5 Agents Arena Htmlslides Elo | 1070 |
| Design Arena | GPT-5.5 Agents Arena Mobile Apps Elo | 1169 |
| Design Arena | GPT-5.5 Agents Arena Pptxslides Elo | 1157 |
| Design Arena | GPT-5.5 Agents Arena Python-Pptxslides Elo | 1152 |
| Design Arena | GPT-5.5 Agents Arena Python-Pptxslides Elo | 1108 |
| Design Arena | GPT-5.5 Agents Arena Webapps Elo | 1132 |
| Design Arena | GPT-5.5 Models Arena 3D Elo | 1226 |
| Design Arena | GPT-5.5 Models Arena Asciiart Elo | 1275 |
| Design Arena | GPT-5.5 Models Arena Code Categories Elo | 1274 |
| Design Arena | GPT-5.5 Models Arena Data Visualization Elo | 1275 |
| Design Arena | GPT-5.5 Models Arena Game Development Elo | 1319 |
| Design Arena | GPT-5.5 Models Arena SVG Elo | 1255 |
| Design Arena | GPT-5.5 Models Arena UI Component Elo | 1272 |
| Design Arena | GPT-5.5 Models Arena Website Elo | 1269 |
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Token volume and request traffic to this model over time.
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
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 context window (922K input, 128K output) with support for text and image inputs, enabling large-scale reasoning, coding, and multimodal workflows within a single system.
GPT-5.5 (batch) costs $2.50/M input tokens and $15.00/M output tokens, with separate rates for Cache Read at $0.25/M tokens and Web Search at $10.00/1K calls.
GPT-5.5 (batch) has a 1,050,000 token context window. It supports up to 128,000 completion tokens.
Yes. GPT-5.5 (batch) accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
GPT-5.5 (batch) accepts files such as PDFs, images, and text as input and returns text.
GPT-6 Astra, GPT-6 Astra Pro, GPT-5.6 Luna Pro and 55 more are other text models from OpenAI.
GPT-5.5 (batch) was released on April 24, 2026. Its knowledge cutoff is December 1, 2025.
