GPT-5.5 landed April 23, 2026. I've been in the benchmark data since the moment it dropped — and I need to tell you the number OpenAI didn't put in any headline:
GPT-5.5 has an 86% hallucination rate on independent evals. That's 2.5× higher than Claude Opus 4.7.
That number changes how you architect AI systems. Everything else in this post builds from it.
What GPT-5.5 Actually Is (Architecture First)
Every GPT-5.x release from 5.1 through 5.4 was a post-training iteration layered on the same base model. GPT-5.5 is not that. It's the first fully retrained base model since GPT-4.5 — architecture, pretraining corpus, and objectives all rebuilt from scratch with one explicit goal: autonomous agent execution.
OpenAI didn't ship another chat model that can do agentic tasks. They shipped a model designed from the ground up to plan, execute, check its own work, and keep going without re-prompting. That distinction matters for every benchmark below.
The Numbers (With Context Nobody's Giving You)
Terminal-Bench 2.0 — Autonomous CLI task completion
GPT-5.5: 82.7% | Claude Opus 4.7: 69.4% | Gemini 3.1 Pro: 68.5%
A 13-point lead. Not noise — a structural capability gap in autonomous terminal execution. This is GPT-5.5's clearest win.
Expert-SWE — Real engineering tasks with 20-hour median human completion time
GPT-5.5: 73.1% | GPT-5.4: 68.5%
73% pass rate on tasks that take a skilled engineer 20 hours. That's production-grade autonomous execution, not a benchmark trick.
SWE-Bench Pro — Fix a real GitHub issue in a real codebase
Claude Opus 4.7: 64.3% | GPT-5.5: 58.6%
Claude wins here. This benchmark maps directly to day-to-day dev work and the 5.7-point gap survived GPT-5.5's full architectural rework.
MRCR v2 Long-Context Retrieval at 512K–1M tokens
GPT-5.5: 74.0% | GPT-5.4: 36.6% | Claude Opus 4.7: 32.2%
This is the most architecturally significant number in the release. GPT-5.5 doubled its own long-context retrieval score and left every competitor behind.
AA-Omniscience — Hallucination rate under factual pressure (Artificial Analysis)
Claude Opus 4.7: 36% | Gemini 3.1 Pro: 50% | GPT-5.5: 86%
GPT-5.5 confidently answers questions it doesn't know the answer to at 2.5× the rate of Claude. This is the number that should be in every headline about this release.
MCP-Atlas — Scaled multi-tool orchestration
Claude Opus 4.7: 77.3% | GPT-5.5: 75.3%
Claude is the more reliable MCP orchestrator. Narrow but consistent.
Artificial Analysis Intelligence Index (composite score)
GPT-5.5 xhigh: 60 | Claude Opus 4.7: 57 | Gemini 3.1 Pro: 57
First time in months any model has broken the three-way tie at the top.
The Crown Jewel: What 82.7% on Terminal-Bench Actually Means
Early reports from developers testing GPT-5.5 described merging branches with hundreds of frontend and refactor changes — against a main branch that had also diverged — resolved autonomously in under 25 minutes. GPT-5.4 couldn't complete the same task.
For my workflows: multi-file refactoring, CLI automation, spec-to-code execution — GPT-5.5 in Codex CLI is now the right tool. The Terminal-Bench lead translates directly to the kind of work developers actually do in terminals.
The Expert-SWE number reinforces this: 73.1% on 20-hour engineering tasks means the model is handling entire implementation cycles, not just autocompleting lines.
The Number That Should Be in Every Headline
Let's sit with the hallucination data for a second because I don't think the implications are landing yet.
According to Artificial Analysis independent evals:
Claude Opus 4.7 hallucinates on 36% of AA-Omniscience questions.
Gemini 3.1 Pro: 50%.
GPT-5.5: 86%.
OpenAI's own description of this model is "the smartest and most intuitive." Fast, confident, high intent-understanding. That personality profile is exactly the one that hallucinates — high confidence, fast inference, low epistemic caution.
Here's what this means in practice for agentic systems:
Use GPT-5.5 to execute code tasks → great, the output is a verifiable artifact.
Use GPT-5.5 to synthesize research → it will fabricate sources confidently.
Use GPT-5.5 to analyze emails or documents → it will confabulate details it didn't read.
Use GPT-5.5 to reason about your architecture → it may invent APIs that don't exist.
The model is a world-class executor. It is a dangerous reasoner about facts. Respecting that shape is the entire game.
Long-Context: The Architectural Unlock
MRCR v2 at 512K–1M tokens: 74.0% for GPT-5.5 vs 32.2% for Claude Opus 4.7 and 36.6% for GPT-5.4.
That's not incremental. Doubling long-context retrieval accuracy changes what's architecturally possible:
"Find every place this function is called across the monorepo"
"What's inconsistent between my OpenAPI spec and my Pydantic models?"
"Trace this bug from the frontend component down to the database layer"
When you load an entire codebase into context, you now get double the retrieval accuracy vs anything that existed last week.
Practical caveat: 1M context is API-only. Codex users get 400K. At $5 per million input tokens, filling the full window costs $5 in input alone before any output. This is a precision tool, not a default. Use it when the task specifically requires it.
The Routing Architecture (How I Actually Use This)
This is what changed in my stack this week. I run a research intelligence agent that digests newsletters, synthesizes AI news, and generates structured summaries via a Claude API route in a Next.js app.
Here's the routing decision I now make for every task:

// Simplified routing logic from my /api/agent/route.ts
// Task type determines which model gets the call

const MODEL_ROUTER = {
  // Execution tasks: terminal work, refactoring, implementation
  // GPT-5.5 wins Terminal-Bench by 13 points
  execution: "gpt-5.5",

  // Research synthesis, email analysis, summarization
  // 86% hallucination rate makes GPT-5.5 dangerous here
  // Claude Sonnet 4.6 stays at 36% error rate
  research: "claude-sonnet-4-20250514",

  // Real bug fixes, GitHub issue resolution
  // Claude Opus 4.7 leads SWE-Bench Pro by 5.7 points
  debugging: "claude-opus-4-7",

  // Multi-tool MCP pipelines (Gmail, Notion, GitHub)
  // Claude leads MCP-Atlas 77.3% vs 75.3%
  orchestration: "claude-opus-4-7",

  // Full codebase reasoning — only when needed
  // MRCR v2: 74% vs 32% is a real architectural unlock
  longContext: "gpt-5.5", // API only, 1M token window

  // Lightweight subagents, scaffolding, classification
  // Don't burn frontier tokens on simple tasks
  lightweight: "gpt-5.4-mini"
};

Senior engineers don't pick one frontier model. They compose them. GPT-5.5 is the right answer for about 40% of what I do — execution-heavy tasks. Claude handles the other 60% — anything where factual accuracy or code quality matters most.
Token Efficiency: The Math That Makes It Defensible
GPT-5.5 doubled GPT-5.4's API price from $2.50 to $5 per million input tokens. That sounds bad until you see the efficiency data.
Artificial Analysis measured approximately 40% fewer output tokens per equivalent task completion. The net result: per-task cost for most Codex workflows is roughly flat vs GPT-5.4 despite the price increase.
The intelligence-per-dollar comparison from their workload modeling: GPT-5.5 at medium effort reaches the same composite intelligence score as Claude Opus 4.7 at maximum effort — at approximately one-quarter of the cost per equivalent workload. Gemini 3.1 Pro Preview hits similar scores at lower cost, so GPT-5.5 isn't the budget pick. But it's not the outlier its $5 input price implies.
Where Claude Still Wins
I want to be direct here because I use Claude as my primary model and this post isn't a GPT-5.5 promotional piece.
SWE-Bench Pro (real GitHub issue → real patch):
Claude Opus 4.7: 64.3% | GPT-5.5: 58.6%
This gap survived a full architectural rework. For the actual day-to-day work of debugging failing tests, resolving GitHub issues, and generating PRs that work — Claude is still more reliable.
MCP tool orchestration (77.3% vs 75.3%) — Claude edges GPT-5.5 on scaled tool pipelines. If you're building agents that chain Gmail, Notion, GitHub, and other tools together via MCP, Claude is the safer orchestrator.
Hallucination rate — 36% vs 86%. For any task where information accuracy is the product, this isn't a close decision.
The Variant Stack
gpt-5.5 standard — Default for agentic coding, multi-file work, CLI tasks.
gpt-5.5 Thinking — Architectural decisions and complex spec writing before you touch code.
gpt-5.5 Pro — Frontier math and deep research problems. Overkill for most dev work.
Fast Mode in Codex — 1.5× speed at 2.5× cost. For time-sensitive CI/CD loops.
gpt-5.4-mini — Subagents, scaffolding, lightweight ops. Keep frontier tokens for frontier tasks.
The Meta Point
GPT-5.5 is a genuine architectural step forward. The base model retrain shows — this isn't a fine-tune and the capability delta is bigger than the version number implies.
But it's a model with a specific personality: fast, confident, action-oriented, and factually unreliable under pressure. That personality is extremely useful if you respect its shape. It becomes dangerous if you deploy it across task types it wasn't designed for.
The era of picking one frontier model and using it for everything is over.
Route by task type. Compose across models. Verify agent outputs.
I'm a high school developer building AI agents, research tools, and productivity systems using Claude, Gemini, and GPT. If you're building agentic systems and routing across models, drop a comment — would like to compare architectures.