GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro Coding (May 2026)
GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro Coding (May 2026)
Three frontier coding models shipped within ten days of each other in April 2026. By May 9, 2026 we have enough real-world data to write a defensible comparison. Here it is — benchmarks, token costs, and per-task winners — for developers who actually have to choose.
Last verified: May 9, 2026
The release timeline
| Model | Release | Status as of May 9, 2026 |
|---|---|---|
| Gemini 3.1 Pro | Developer preview February 2026; ongoing | GA in Workspace + Vertex AI |
| Claude Opus 4.7 | April 16, 2026 | GA on Claude, API, Bedrock, Vertex, Foundry |
| GPT-5.5 | April 23, 2026 (ChatGPT); April 24, 2026 (API) | GA across OpenAI surfaces |
Three weeks. Three frontier models. The benchmark wars were ferocious. The reality is more nuanced than the marketing slides suggest.
The benchmark scoreboard
Verified numbers as of May 9, 2026 (sourced from Anthropic, OpenAI, Google official reports plus llm-stats.com and Vellum AI cross-references):
| Benchmark | Claude Opus 4.7 | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|---|
| SWE-bench Verified | 87.6% | ~83% | 80.6% |
| SWE-bench Pro | 64.3% | 57.7% (5.4 Pro) | 54.2% |
| CursorBench | 70% | ~62% | ~58% |
| MCP-Atlas (tool use) | 77.3% | 68.1% (5.4) | 73.9% |
| OSWorld-Verified (computer use) | 78.0% | ~80% (5.5 estimated) | ~70% |
| GPQA Diamond (reasoning) | 94.2% | 94.4% (5.4 Pro) | 94.3% |
| Humanity’s Last Exam (no tools) | 46.9% | 42.7% (5.4 Pro) | 44.4% |
| Visual Acuity | 98.5% | ~92% | ~94% |
Notes:
- Claude Mythos (Anthropic’s research-preview-only model) scores 56.8% on Humanity’s Last Exam — meaningfully ahead of all three GA models — but is not generally available.
- GPT-5.5 vs GPT-5.4 Pro: where GPT-5.5 numbers are public they’re comparable to or slightly ahead of GPT-5.4 Pro on coding; the bigger 5.5 gains are on agentic tasks.
- Visual Acuity matters more than it sounds. Opus 4.7’s 98.5% (up from 54.5% on 4.6) drives its computer-use and screenshot-analysis lead.
What each model is actually best at
Claude Opus 4.7: the multi-file coding champion
Opus 4.7’s strengths cluster around complex code work that involves reading and modifying many files coherently:
- SWE-bench Pro lead (64.3%) — this is the real signal. SWE-bench Pro is harder than Verified and rewards models that can hold large state across multiple files and tools.
- MCP-Atlas lead (77.3%) — strongest tool orchestration of the three. Matters for agentic coding where the model is calling many MCP servers in sequence.
- CursorBench 70% — directly correlated with Cursor IDE performance.
- Vision uplift (54.5% → 98.5%) — drives computer-use and screenshot-analysis. The single biggest version-over-version improvement of the three releases.
- xhigh effort level + task budgets — finer control over reasoning vs latency tradeoff.
Use Opus 4.7 for: large monorepo refactors, multi-file migrations, agentic coding with MCP-heavy workflows, code review with visual context, document/PDF/screenshot analysis.
Caveats: new tokenizer means 1.0-1.35x more tokens for same content vs Opus 4.6. Same per-million pricing, higher effective bills. Plan accordingly.
GPT-5.5: the agentic terminal champion
GPT-5.5’s strengths cluster around autonomous task execution that involves driving external systems:
- OSWorld-Verified lead — the model that runs longest without losing the plot when driving a browser or shell.
- Agentic discount tiers on Bedrock and Foundry — economic advantage for high-volume agentic workloads.
- GPT-5.5 Instant as the new default for ChatGPT — reduced hallucination in sensitive fields, useful for customer-facing agents.
Use GPT-5.5 for: autonomous browser agents, terminal-driving agents, computer-use workflows, long-running multi-step tasks where the model needs to maintain coherence across many tool calls and external state changes.
Caveats: less strong than Opus 4.7 on multi-file precision coding. Best in agentic-tier deployments rather than standard API.
Gemini 3.1 Pro: the long-context analyst
Gemini 3.1 Pro’s strengths cluster around understanding very large bodies of content:
- Multimodal context window including direct video file uploads.
- Cheapest at scale especially for long-context analytical work.
- Strong on research-style tasks — summarize, plan, compare across hundreds of pages.
- Reasoning parity with the other two on GPQA Diamond.
Use Gemini 3.1 Pro for: analyzing 500K+ LOC codebases, video-based coding (UI walk-through to specs), research and architectural planning, cross-document analysis, long-context tasks where token cost dominates.
Caveats: trails Opus 4.7 on direct coding precision benchmarks. Best for the “understand and plan” half of the workflow rather than the “write the code” half.
Cost as of May 9, 2026
API-direct pricing:
| Model | Input | Output | Context | Notes |
|---|---|---|---|---|
| Claude Opus 4.7 | $5/M | $25/M | 1M | 128k max output. New tokenizer = 1.0-1.35x more tokens. |
| GPT-5.5 | ~$5/M (standard) | ~$25/M (standard) | 1M+ | Agentic tier discount 20-40% on Bedrock/Foundry. |
| Gemini 3.1 Pro | Volume-tiered, often <$3/M | Volume-tiered | 2M+ | Cheapest at scale. |
Subscription tiers:
| Tier | Claude | OpenAI | |
|---|---|---|---|
| Pro / Plus / Premium | $20/mo | $20/mo | varies |
| Max / Team / Enterprise | $100-200/mo | $25-200+/mo | enterprise contract |
For active agentic coding workloads, expect $50-300/developer/month in API or Pro+ tier costs at standard usage. Heavy parallel-agent workflows (Cursor 3 Power, Claude Code Max with agent teams) regularly land in the $300-800/developer/month range.
Per-task picker for May 2026
If you’re not using Cursor 3’s Best-of-N or IBM Bob’s auto-routing, here’s the manual heuristic:
| Task | Best model | Why |
|---|---|---|
| Multi-file refactor across 20+ files | Claude Opus 4.7 | SWE-bench Pro 64.3%, holds state across files |
| Code migration (e.g., Vue 2 → Vue 3) | Claude Opus 4.7 | Same — multi-file coherence |
| Autonomous browser agent | GPT-5.5 | OSWorld-Verified lead, agentic tier pricing |
| Terminal-driving agent (CI, deployment) | GPT-5.5 | Same — long-running coherence |
| Analyze 1M+ LOC codebase | Gemini 3.1 Pro | Long context + cheapest at scale |
| Plan a large architectural change | Gemini 3.1 Pro | Long-context analytical work |
| Tool-heavy MCP orchestration | Claude Opus 4.7 | MCP-Atlas 77.3% |
| Code review with screenshots / PDFs | Claude Opus 4.7 | Visual Acuity 98.5% |
| Security analysis (Snyk integration) | Claude Opus 4.7 | Snyk+Claude shipped May 7, 2026 |
| Generic single-file code completion | Any | Differences below noise on simple tasks |
| Video-to-code (UI walk-through) | Gemini 3.1 Pro | Native multimodal video |
| Computer-use / screenshot analysis | Claude Opus 4.7 or GPT-5.5 | Both strong; depends on tooling |
How to actually deploy this in May 2026
The naive approach — “pick one model, use it for everything” — is leaving 20-40% productivity on the table in May 2026. Better approaches:
Option 1: Cursor 3 Best-of-N
Cursor 3’s Agents Window has native Best-of-N — send the same prompt to all three models simultaneously, see all outputs, accept the best. Costs roughly 3x per prompt, but for important tasks the productivity gain is worth it.
Option 2: IBM Bob auto-routing
If you’re an enterprise customer running IBM Bob, the platform routes tasks to the best model automatically. Trade developer transparency for less manual choosing.
Option 3: Multi-agent specialization
Run Claude Code agent teams with model-per-specialist:
- Frontend specialist → Claude Opus 4.7 (visual + multi-file)
- Backend specialist → Claude Opus 4.7 (MCP-heavy)
- Test specialist → Gemini 3.1 Pro (long context across test suites)
- Computer-use specialist → GPT-5.5
Option 4: Workload-based defaults
Establish team defaults per workload type:
- IDE coding (Cursor / Claude Code) → Opus 4.7 default
- Long-running autonomous agents → GPT-5.5 default
- Codebase analysis / research → Gemini 3.1 Pro default
The honest caveat: benchmarks ≠ production
Benchmark gaps narrow quickly in real workflows. Three weeks of hands-on usage matters more than any single benchmark. Common surprises:
- Opus 4.7’s instruction-following changed. Prompts tuned for 4.6 sometimes need re-tuning. Opus 4.7 takes instructions more literally.
- GPT-5.5’s agentic tier matters more than the standard tier. Standard-tier GPT-5.5 looks similar to 5.4. Agentic-tier on Bedrock or Foundry is where the real productivity is.
- Gemini 3.1 Pro’s long context wins compound. When you can fit your entire codebase in context, you stop needing to engineer chunking and retrieval. The productivity gain isn’t visible in single-prompt benchmarks.
The honest May 2026 answer: use all three, route by task type, and stop pretending there’s one winner. The marketing wars want you to standardize. The benchmarks say you shouldn’t.
Related on andrew.ooo
- Cursor 3 Agents Window vs Claude Code Parallel Agents
- GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.1 Pro (April 2026 benchmarks)
- IBM Bob vs Claude Code vs Cursor 3 (Enterprise SDLC, May 2026)
- Best AI coding tools with spec-driven mode (May 2026)
Sources: Anthropic Claude Opus 4.7 release notes (anthropic.com/news/claude-opus-4-7), OpenAI GPT-5.5 announcements, Google Gemini 3.1 Pro documentation, llm-stats.com, Vellum AI benchmark cross-references, Mashable, livemint coverage. Last verified May 9, 2026.