What Is GPT-5.5? Benchmarks, Pricing, and How It Compares to Claude Opus 4.7 and Gemini 3.1 Pro
Introduction
On April 23, 2026, OpenAI released GPT-5.5 (reported by some outlets as codename "Spud"), its latest frontier model. Arriving roughly seven weeks after GPT-5.4 (released on March 5, 2026), the rapid cadence signals just how fiercely top AI labs are now competing for enterprise customers.
OpenAI positions GPT-5.5 as its "smartest and most intuitive-to-use model" yet. It posted a state-of-the-art 82.7% on Terminal-Bench 2.0 and shows notable gains in agentic coding, computer use, knowledge work, and early scientific research.
Key takeaways
- GPT-5.5 launched April 23, 2026, achieving 82.7% on Terminal-Bench 2.0 (SOTA)
- It scores 58.6% on SWE-Bench Pro — behind Claude Opus 4.7 (64.3%) — but third-party comparisons tally it as leading across 14 benchmarks overall
- Available now in ChatGPT Plus / Pro / Business / Enterprise; API rollout is "coming soon"
What Makes GPT-5.5 Stand Out?
GPT-5.5's core strength is autonomous, agentic work. OpenAI highlights improvements in "analyzing data, writing and debugging code, operating software, researching online, and creating documents and spreadsheets." It handles multi-step workflows more autonomously, requiring less user input.
Key benchmark highlights:
- Terminal-Bench 2.0: 82.7% (SOTA) — complex command-line workflows requiring planning, iteration, and tool coordination
- SWE-Bench Pro: 58.6% — multi-file GitHub issue resolution (Claude Opus 4.7 leads at 64.3%)
- Some third-party comparisons report 14 benchmark SOTAs among publicly available models, ahead of Claude Opus 4.7 and Gemini 3.1 Pro (see Lushbinary's summary; OpenAI's official post does not state this total explicitly)
Efficiency improvements are equally important. GPT-5.5 is "a faster, sharper thinker for fewer tokens" — it matches GPT-5.4's per-token latency while delivering significantly higher intelligence. For API users, this translates to real cost savings.
OpenAI says the model "shows meaningful gains on scientific and technical research workflows" and points to potential applications in areas like drug discovery. See the official announcement for full framing.
GPT-5.5 vs. Claude Opus 4.7 vs. Gemini 3.1 Pro
Frontier models are increasingly specializing. In short: agentic work → GPT-5.5, code quality → Claude Opus 4.7, with Gemini 3.1 Pro remaining a strong long-context and cost-sensitive option (verify current specs in the primary source).
| Metric | GPT-5.5 | Claude Opus 4.7 |
|---|---|---|
| SWE-Bench Pro | 58.6% | 64.3% (leads) |
| Terminal-Bench 2.0 | 82.7% (SOTA) | — |
| Context window | 1M tokens | 1M tokens |
| Max output | 128K | 128K |
| Input price (per 1M) | $5 | $5 |
| Output price (per 1M) | $30 | $25 |
At least on SWE-Bench Pro, Claude Opus 4.7 (64.3%) beats GPT-5.5 (58.6%). On Terminal-Bench 2.0, GPT-5.5 posts state-of-the-art 82.7%. Individual benchmarks split both ways — pick evaluations close to your actual workload rather than relying on a single "winner" framing.
Gemini 3.1 Pro still matters for long-context and cost-sensitive workloads. At the time of writing, primary sources for exact Gemini 3.1 Pro pricing and context window are limited — confirm the latest values on the Google DeepMind model page before planning a deployment.
API Pricing and Availability
GPT-5.5 API pricing is announced at $5 / $30 per 1M tokens (input/output). GPT-5.5 Pro is priced at $30 / $180 per 1M tokens. API availability is "very soon" per OpenAI — as of April 2026, access runs through the ChatGPT app first.
Compared to Claude Opus 4.7 ($5 / $25), GPT-5.5 output is 17% more expensive per token. However, because GPT-5.5 often uses fewer tokens per task, effective cost per task can favor GPT-5.5 depending on workload.
ChatGPT tier availability:
- GPT-5.5: Plus / Pro / Business / Enterprise
- GPT-5.5 Pro: Pro / Business / Enterprise
- Codex in ChatGPT: Powered by GPT-5.5
How Do You Start Using It?
ChatGPT users on supported plans can select GPT-5.5 from the model selector as of April 23, 2026. The free tier is not included — you'll need Plus or higher to try it.
For API access, wait for the official OpenAI announcement. The model is currently in partner safety review. If you want early access, reach out via the OpenAI partnership program.
Once the API opens, CLIs and IDE extensions are expected to support GPT-5.5 as well, but the exact model ID and supported tools should be confirmed via OpenAI's official release notes rather than assumed.
FAQ
Q. What's different from GPT-5.4?
A. Three main changes: (1) significantly improved agentic work, (2) better token efficiency at the same latency, and (3) state-of-the-art 82.7% on Terminal-Bench 2.0. GPT-5.5 matches GPT-5.4's per-token latency while being substantially smarter.
Q. Should I use GPT-5.5 or Claude Opus 4.7?
A. Depends on your use case. For code quality, Opus 4.7 has a 6.6-point lead on SWE-Bench Pro. For agentic autonomy and terminal workflows, GPT-5.5 leads. Many teams use both and route by task type.
Q. When is the API available?
A. OpenAI says "very soon." As of April 2026, it's not yet in the API — they're finalizing safety requirements with partners. Watch OpenAI's official announcements for the release date.
Q. Did pricing go up?
A. Yes. API pricing is $5 / $30 per 1M tokens, up from GPT-5.4's $2.50 / $15. However, since GPT-5.5 uses fewer tokens per task, real-world cost per task isn't always doubled.
Q. Can I try it on the free plan?
A. Not as of April 2026. You need ChatGPT Plus ($20/month) or higher to access GPT-5.5.
Summary
GPT-5.5 is OpenAI's stake in the agentic era. OpenAI frames it as a deliberate step toward the kind of computing expected in the future, with an emphasis on autonomous task execution rather than just answering questions.
With Claude Opus 4.7 winning on code quality, GPT-5.5 on agentic strength, and Gemini continuing to compete on long-context and cost (check current Gemini 3.1 Pro specs before committing), frontier AI is no longer a single-winner race — it's specialization. At ZenChAIne, we continue to track how these models evolve and which fits best for which real-world task.