TL;DR — GPT-5.5 (April 2026) leads Terminal-Bench 2.0 (82.7% vs 75.1%) and the overall Artificial Analysis Intelligence Index (60 vs ~57 for Opus 4.7). Claude Opus 4.7 still leads SWE-Bench Pro (64.3% vs 58.6%) — i.e. real-world GitHub-issue resolution. At $5/$30 per 1M vs Opus's $15/$75, GPT-5.5 is 3× cheaper but not a strict replacement. Use GPT-5.5 for agentic / terminal workflows and long reasoning; use Opus 4.7 for real codebase edits and subtle debugging. A phase-aware router picks automatically.
What's new in GPT-5.5
OpenAI released GPT-5.5 on April 23, 2026 with a full retrain focused on agentic reliability — the same week DeepSeek V4 and Kimi K2.6 shipped, making this the hottest frontier-model week of the year. Headline numbers:
- Artificial Analysis Intelligence Index: 60 (new leader, ~3 pts ahead of Opus 4.7 and Gemini 3.1 Pro Preview).
- Terminal-Bench 2.0: 82.7% (up from 75.1% on GPT-5.4).
- GDPval: 84.9%.
- SWE-Bench Pro: 58.6% — still behind Claude Opus 4.7 (64.3%).
- Context window: 1M tokens.
- Pricing: $5 / $30 per 1M input/output — double the price of GPT-5.4. A "Pro" API tier carries $30 / $180 for extreme reasoning workloads.
The pitch: same response speed as GPT-5.4, fewer tokens burnt per task, and a stronger agent across long tool chains.
The benchmark tension
Headlines will tell you "GPT-5.5 is the smartest model ever." That's true for the composite Intelligence Index. But on the single coding metric most engineers care about — SWE-Bench Pro, which grades real GitHub-issue patches against a test suite — Opus 4.7 is still ahead:
| Model | SWE-Bench Pro | Terminal-Bench 2.0 | AA Intelligence Index | Input $/M | Output $/M | |---|---:|---:|---:|---:|---:| | Claude Opus 4.7 | 64.3% | 75.1% | 57 | $15 | $75 | | GPT-5.5 | 58.6% | 82.7% | 60 | $5 | $30 | | GPT-5.4 | 57.7% | 75.1% | 57 | $2.50 | $15 | | DeepSeek V4-Pro | 81% (SWE-bench Verified, different dataset) | — | — | $1.74 | $3.48 |
So which one "wins" depends entirely on your task shape.
When to reach for GPT-5.5
- Terminal / agent workflows. Running a coding agent that does
bash+read_file+editchains for dozens of steps? GPT-5.5's 7.6-point Terminal-Bench jump is the most practical improvement OpenAI has shipped in a year. - Long reasoning under ambiguity. Architecture decisions, design trade-off memos, "what's the best library for X given these constraints." Intelligence Index 60 shows here.
- 1M-token context. When you genuinely need to load a 400K-line codebase into a single prompt — Opus 4.7 caps at 200K.
- Budget-constrained flagship usage. $5/$30 is 3× cheaper than Opus 4.7's $15/$75. If the task doesn't specifically reward Opus, GPT-5.5 gets you "close enough" for a third of the cost.
When Opus 4.7 still wins
- Real codebase edits with subtle invariants. The 5.7-point SWE-Bench Pro gap is consistent across provider reports and independent evals. If your task involves "modify this existing function without breaking 40 callers," Opus catches edge cases GPT-5.5 misses.
- Tool-call format reliability. In our production routing data (see phase-aware routing explained), Anthropic models have the lowest retry-rate on strict JSON / function-call outputs. GPT-5.5 narrows the gap vs 5.4 but Opus is still the safest pick when a broken tool-call costs you the entire session.
- Plan mode in Claude Code. Native prompt-caching + plan-mode semantics are Anthropic-tuned. Opus 4.7 in Plan mode is still the strongest "design an approach to this ambiguous task" workflow.
The price math nobody shows you
Scenario: a mid-size engineering team running ~30M tokens/month for coding.
| Setup | Monthly bill | |---|---:| | 100% Opus 4.7 | $900 (rough, blended 70/30 input/output) | | 100% GPT-5.5 | $300 | | Phase-routed (Opus for plan/debug, GPT-5.5 for agent loops, DeepSeek V4-Flash for implement/test) | ~$60–90 |
The 100%-GPT-5.5 scenario saves 67% vs Opus 4.7. But the phase-routed setup saves 90%+ because 60–70% of real coding calls don't need either flagship — they're straightforward edits, test generation, and docs that DeepSeek V4-Flash at $0.14/$0.28 handles indistinguishably.
Why pick one when you don't have to
The traditional "which model" framing is a legacy of when tools like Cursor locked you to a single backend. Modern setup:
- Point your coding agent (Claude Code, Aider, Cursor, OpenClaw, Cline) at a phase-aware router.
- Let the router detect the phase (plan / implement / debug / test / refactor / docs / small-edit) and route to the best model for that phase.
- Opus 4.7 keeps the reasoning-heavy workload. GPT-5.5 handles agent loops and long-context reads. DeepSeek V4-Flash burns through the implementation grind. Kimi K2.6 takes multi-file refactors.
- You pay ~10% of what you'd pay pinning a flagship.
That's what CodeRouter does. No code changes in your agent — just a base_url swap.
Quick setup
If you're using Claude Code, the switch is two environment variables:
export ANTHROPIC_BASE_URL="https://api.coderouter.io/v1"
export ANTHROPIC_AUTH_TOKEN="cr_..."
Full guide for every agent: Claude Code router setup · Aider cost optimization · Cut your Cursor bill 70–90%.
Want raw passthrough without the phase router? Direct plan lets you hit gpt-5.5 or claude-opus-4-7 directly at provider list price × 1.15.
The answer
GPT-5.5 is a genuinely better reasoner than GPT-5.4 and ties or beats Opus 4.7 on many benchmarks. But "ties on benchmarks" ≠ "strictly better at coding" — Opus 4.7 still has a 5-point advantage on real-world patches, and OpenAI doubled the price.
Pragmatic take: Use GPT-5.5 for agent loops and long reasoning. Keep Opus 4.7 for sensitive edits and plan-mode work. Route both automatically via a phase-aware proxy and stop thinking about the choice.