Does GPT-5.5 beat Opus 4.7? It depends on what you need
GPT-5.5 dropped on April 23, one week after Opus 4.7. Both claim to be the smartest model available. Neither is entirely right. The real question is: what are you using it for?
GPT-5.5 wins on speed and token efficiency. Opus 4.7 wins on depth and reliability. The gap between them is narrow, but consistent: pick based on your workload, not the leaderboard.
Where we’re headed
Two flagship models dropped within a week of each other, and the internet immediately turned it into a cage match. The truth is less dramatic but more useful: these models were built with opposite philosophies, and the benchmarks reflect that. GPT-5.5 is optimized for getting things done fast. Opus 4.7 is optimized for getting things done right. We’ll walk through the numbers, the trade-offs, the pricing, and what it all means depending on what you actually do with these models.
The story
Two models, two philosophies
The headline benchmarks tell a clear story, but it’s not the story most people expect.
| Benchmark | GPT-5.5 | Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|
| SWE-Bench Pro | 58.6% | 64.3% | 54.2% |
| Terminal-Bench 2.0 | 82.7% | 69.4% | 68.5% |
| ARC-AGI-1 (High) | 94.5% | 92.0% | - |
| ARC-AGI-2 (High) | 83.3% | 68.3% | 77.1% |
| BrowseComp | 84.4% | 79.3% | 85.9% |
| GPQA Diamond | 93.6% | 94.2% | 94.3% |
| OSWorld-Verified | 78.7% | 78.0% | - |
| Humanity’s Last Exam (no tools) | 40.6% | 31.2% | - |
| Humanity’s Last Exam (w/ tools) | 52.2% | 54.7% | - |
| MCP-Atlas (tool use) | - | 77.3% | 73.9% |
| BigLaw Bench | - | 90.9% | - |
GPT-5.5 dominates where structured execution and web navigation matter. Opus 4.7 wins where deep reasoning and multi-turn tool orchestration count. This isn’t random. It’s the direct result of how each model was built.
The pragmatist vs. the methodologist
GPT-5.5 is the pragmatist. OpenAI optimized it for task completion: fewer tokens spent, more direct answers, less overthinking. Compared to GPT-5.4, the same work gets done with fewer tokens (from ~18,000 to ~14,500 on Codex) and better results. It solves the problem and stops, without digressing. This efficiency shows in execution benchmarks: Terminal-Bench and ARC-AGI are dominated by an approach that doesn’t waste cycles.
Opus 4.7 is the methodologist. Anthropic designed it to reason before answering. It verifies its own outputs before presenting them, a new behavior in LLMs. On SWE-Bench Pro it resolves more real GitHub issues because it analyzes problems with more depth. On MCP-Atlas it handles complex tool call sequences better. The cost is verbosity: in practical tests, Opus generates about 67% more tokens than GPT-5.5.
In practice, on single-shot tests for UI/web, games, and simulations, the quality of the code produced is comparable. But Opus completes the same tasks about 2-2.5x faster, while GPT-5.5 consumes fewer tokens but takes longer. It’s a trade-off between latency and per-token cost.
The pricing reality
| Model | Input ($/M tokens) | Output ($/M tokens) | Context |
|---|---|---|---|
| GPT-5.5 | 5 | 30 | 1M |
| GPT-5.5 Pro | 30 | 180 | 1M |
| Opus 4.7 | 5 | 25 | 1M |
| Gemini 3.1 Pro | 2 | 12 | 2M |
GPT-5.5 costs twice what GPT-5.4 costs in output ($30 vs $15), but consumes fewer tokens for the same task. The net result depends on your use case: short prompts with long responses make the output price doubling hurt. Structured tasks where GPT-5.5 is more token-efficient can compensate.
Opus 4.7 costs less per output token ($25 vs $30) but generates more of them. In practical tests: a task costing $0.50 with GPT-5.5 runs about $0.79 with Opus. Not dramatic, but it adds up at scale.
The real cost advantage is Gemini 3.1 Pro: $2/$12 with 2M context. It doesn’t win many benchmarks, but for tasks that don’t require the flagship model, it’s the best value for money.
The missing context: business models and what comes next
The GPT-5.5 vs Opus 4.7 race isn’t just technical. It’s also about opposing business models.
OpenAI keeps pushing mass distribution: 4 million weekly developers on Codex, ChatGPT as a consumer app, GPT-5.5 included in the Pro subscription. The model is high volume, accessible pricing, ecosystem lock-in. The risk is that the commercial incentive pushes optimization for visible benchmarks rather than deep reliability.
Anthropic pursues the opposite path: fewer users, higher price, focus on safety and interpretability. Opus 4.7 verifies its own outputs before returning them, a behavior that costs tokens (and therefore money) but reduces hallucinations. It’s an explicit trade-off between speed and reliability. BigLaw Bench at 90.9% is not a coincidence: Opus is the model you choose when errors cost more than tokens.
And then there’s Gemini 3.1 Pro as the third contender. Google isn’t competing on the same terms: it offers 2M context at a third of the price. It doesn’t win many benchmarks, but for tasks requiring long context (document analysis, extensive codebases), it’s the only one that can actually load everything into memory without chunking.
What’s next? Both models are targeting agentic computing: not chatbots, but agents that operate on your PC for hours. GPT-5.5 with Codex, Opus 4.7 with Claude Code. The real difference will show not on academic benchmarks, but on how many consecutive hours an agent can work without losing the thread or making catastrophic errors.
For the technically curious
From here on, we’re getting into the weeds. If you don’t care about benchmark methodology and caveats, skip to the takeaway.
Benchmark caveats
SWE-Bench, Terminal-Bench, and friends are measured on specific datasets with standardized prompts. In the real world, with ambiguous prompts and messy codebases, results can shift significantly.
GPT-5.5 is a full retraining (not a refinement of GPT-5), but the naming suggests an incremental update. OpenAI chose 5.5 instead of 6.0 precisely because there’s no architectural leap. If you’re waiting for a generational jump, this isn’t it.
Mythos, Anthropic’s most powerful model, is not publicly accessible. The benchmarks we see for Opus 4.7 are for the released version. With Mythos, the gap could be very different, but nobody can verify that.
Finally, practical coding tests show that for an average user the difference is minimal. The real difference emerges on complex problems, specific codebases, and research domains. If you’re not in those fields, the cheapest model that works well is almost always the right choice.
The takeaway
- GPT-5.5 excels at fast, token-efficient execution; Opus 4.7 excels at deep, verified reasoning
- Opus costs ~58% more per task in practice due to higher token generation, despite lower per-token pricing
- The “best model” depends entirely on your workload: automation vs. analysis, speed vs. reliability
- Gemini 3.1 Pro remains the value play for anything that doesn’t need flagship performance
Two philosophies, two price points, two sets of trade-offs. Pick the one that matches the job, not the one that wins the most benchmarks on paper.