GPT-5.5 vs Claude Opus 4.7: Which Model Wins on Coding Benchmarks?

Apr 24, 2026

OpenAI released GPT-5.5 on April 23, 2026. I immediately compared its benchmark numbers against Claude Opus 4.7, the current coding benchmark leader. The results surprised me: Opus 4.7 still wins on SWE-Bench Pro, but GPT-5.5 dominates Terminal-Bench 2.0 by a 13-point margin.

The Short Answer

Claude Opus 4.7 remains better for pure GitHub issue resolution. GPT-5.5 wins for terminal-based agentic workflows and multi-step tool orchestration. The gap is meaningful enough that your choice should depend on what you actually do with the model.

[Resolving GitHub issues?]
  |-- YES --> Claude Opus 4.7 (64.3% on SWE-Bench Pro)
  |-- NO --> Continue

[Terminal/CLI coding agent?]
  |-- YES --> GPT-5.5 (82.7% on Terminal-Bench 2.0)
  |-- NO --> Continue

[Generating UI layouts?]
  |-- YES --> Claude Opus 4.7 (better visual hierarchy)
  |-- NO --> Test both on your specific workflow

The Benchmark Gap That Matters

I looked at the three coding benchmarks that actually differentiate frontier models in 2026. HumanEval is saturated at 95%+ for everyone. SWE-Bench Verified has become a historical baseline. The real signals are SWE-Bench Pro, Terminal-Bench 2.0, and OSWorld-Verified.

| Benchmark          | GPT-5.5 | Claude Opus 4.7 | GPT-5.4 | Gemini 3.1 Pro |
|--------------------|---------|-----------------|---------|----------------|
| SWE-Bench Pro      | 58.6%   | 64.3%           | 57.7%   | 54.2%          |
| Terminal-Bench 2.0 | 82.7%   | 69.4%           | 75.1%   | 68.5%          |
| SWE-Bench Verified | ~88%    | 87.6%           | —       | —              |
| OSWorld-Verified   | 78.7%   | 78.0%           | 75.0%   | —              |

Source: OpenAI launch post, BenchLM leaderboard (April 23, 2026)

The pattern is clear. GPT-5.5 trails Opus 4.7 by 5.7 percentage points on SWE-Bench Pro. But on Terminal-Bench 2.0, GPT-5.5 leads by 13.3 points. That’s a gap of nearly 20% relative improvement.

Why the Benchmarks Diverge

SWE-Bench Pro and Terminal-Bench measure different things. Understanding the difference explains why neither model is universally better.

SWE-Bench Pro tests real-world GitHub issue resolution. The model receives a bug report from a real open-source project, clones the repo, reads the code, identifies the problem, and writes a fix. It’s a single-shot test: one issue, one solution attempt.

Terminal-Bench 2.0 tests sustained command-line work. The model navigates directories, runs build commands, interprets error output, edits files, re-runs tests, and iterates through failures. It’s multi-step by design.

SWE-Bench Pro:
  - Single GitHub issue
  - One-shot fix attempt
  - Measures: code understanding + patch correctness

Terminal-Bench 2.0:
  - Multi-command sequences
  - Iteration through failures
  - Measures: tool orchestration + error recovery

Opus 4.7 excels at reading code and producing correct patches. GPT-5.5 excels at running tools and recovering from failed attempts. The benchmarks reflect different strengths.

Token Efficiency: GPT-5.5’s Real Advantage

OpenAI’s headline claim for GPT-5.5 isn’t benchmark scores. It’s token efficiency. The company says GPT-5.5 matches GPT-5.4 per-token latency while “using significantly fewer tokens to complete the same Codex tasks.”

I looked at the pricing to verify this claim. GPT-5.5 costs 2x GPT-5.4 on both input and output. If GPT-5.5 uses significantly fewer tokens, the cost per task might actually decrease.

| Model            | Input   | Cached Input | Output  |
|------------------|---------|--------------|---------|
| GPT-5.5          | $5.00   | $0.50        | $30.00  |
| GPT-5.5 Pro      | $30.00  | —            | $180.00 |
| GPT-5.4          | $2.50   | $0.25        | $15.00  |
| Claude Opus 4.7  | $5.00   | $0.50        | $25.00  |
| Claude Opus 4.5  | $5.00   | $0.50        | $25.00  |

GPT-5.5 base output costs $5 more per 1M tokens than Opus 4.7. Input is identical. If GPT-5.5 truly uses fewer tokens per task, the cost gap narrows. But OpenAI doesn’t publish specific token reduction percentages, so I can’t verify the claim independently.

The GPT-5.5 Pro variant is positioned for “higher-accuracy” work, not general prompting. At 7x the output cost of Opus 4.7, it’s a niche tool for high-value tasks where accuracy matters more than cost.

Where GPT-5.5 Actually Wins

Beyond Terminal-Bench 2.0, I found three areas where GPT-5.5 shows clear advantages:

1. Agentic Multi-Step Work

OpenAI’s launch post emphasizes sustained work, not single-shot generation. Early testers reported GPT-5.5 can:

Hold context across large systems and propagate changes through surrounding codebase
Reason through ambiguous failures and verify assumptions with tools instead of guessing
Predict testing and review needs without being asked

The infrastructure story supports this claim: Codex running GPT-5.5 wrote load balancing heuristics for OpenAI’s serving stack, increasing token generation speeds by over 20%. The model is improving the infrastructure that serves it.

2. FrontierMath Tier 4

On the hardest math benchmark tier, GPT-5.5 leads all competitors:

| Tier              | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|--------------------|---------|-----------------|----------------|
| FrontierMath T1-3 | 51.7%   | 43.8%           | 36.9%          |
| FrontierMath T4   | 35.4%   | 22.9%           | 16.7%          |

Tier 4 contains the most difficult problems. GPT-5.5’s 12.5-point lead over Opus 4.7 suggests stronger reasoning on novel, complex mathematical structures.

3. GDPval Win Rate

GDPval measures whether a model can win or tie against domain experts. GPT-5.5 scores 84.9%, Opus 4.7 scores 80.3%. The 4.6-point gap translates to roughly 23% more tasks where GPT-5.5 matches or exceeds expert performance.

Where Opus 4.7 Still Leads

Claude Opus 4.7 isn’t losing ground everywhere. I identified three areas where it remains clearly ahead:

1. SWE-Bench Pro

The 64.3% score is the highest among non-preview models. Only Claude Mythos Preview (77.8%) beats it, but Mythos is a preview model priced at $25 input / $125 output. For production-ready models, Opus 4.7 is the SWE-Bench Pro leader.

2. Humanity’s Last Exam (No Tools)

On reasoning benchmarks without tool access, Opus 4.7 scores 46.9%, GPT-5.5 scores 41.4%. The 5.5-point gap suggests Opus 4.7 has stronger pure reasoning when it can’t reach for external tools.

| Benchmark                   | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|-----------------------------|---------|-----------------|----------------|
| Humanity's Last Exam (no tools) | 41.4% | 46.9%          | 44.4%          |
| GPQA Diamond                | 93.6%   | 94.2%           | 94.3%          |

3. UI Generation

The Appwrite analysis identified a pattern I’ve noticed myself: GPT-5.5 still defaults to “cards on a soft background with generous padding” for UI generation. The benchmark improvement in reasoning hasn’t translated to visual design.

Opus 4.7 produces layouts with clearer hierarchy, tighter typography, and fewer reflexive card grids. If you’re generating UI from prompts, Opus 4.7 is still the stronger choice.

What’s Missing From Both

Neither model has solved the fundamental limitation of SWE-Bench Pro: single-shot testing. The benchmark allows one attempt. Real workflows iterate through failures, adjust approaches, and try alternative solutions.

The BenchLM leaderboard shows Claude Mythos Preview at 77.8% on SWE-Bench Pro, a 13.5-point lead over Opus 4.7. Mythos is Anthropic’s preview reasoning model, not production-ready. The gap suggests there’s headroom in SWE-Bench Pro that neither GPT-5.5 nor Opus 4.7 has captured.

My Analysis Process

I approached this comparison with a specific question: should I switch my coding agent from Opus 4.7 to GPT-5.5? I use Claude Code for daily development work, which involves:

Reading existing codebases and understanding architecture
Making targeted changes across multiple files
Running tests, interpreting failures, iterating fixes
Terminal-based commands: build, lint, test, deploy

I examined three data sources:

OpenAI’s launch post and system card for official benchmark claims
Appwrite’s independent analysis for pricing and practical observations
BenchLM’s leaderboard for context against other models

The pattern that emerged wasn’t a simple winner/loser split. It’s a capability trade-off:

| Capability              | Better Model    | Margin    |
|-------------------------|-----------------|-----------|
| GitHub issue resolution | Claude Opus 4.7 | +5.7 pts  |
| Terminal workflows      | GPT-5.5         | +13.3 pts |
| Hard math reasoning     | GPT-5.5         | +12.5 pts |
| Pure reasoning (no tools)| Claude Opus 4.7 | +5.5 pts |
| UI generation           | Claude Opus 4.7 | Qualitative|
| Token efficiency        | GPT-5.5         | Claimed   |

Who Should Choose What

Choose GPT-5.5 If:

You build terminal-based coding agents that iterate through failures
You need multi-step tool orchestration with error recovery
Your workflow involves sustained context across large systems
You value token efficiency for cost-sensitive applications
Frontier math or scientific reasoning is your primary use case

Choose Claude Opus 4.7 If:

You resolve real GitHub issues (SWE-Bench Pro scenario)
You generate UI layouts from prompts
Your tasks need pure reasoning without external tools
You want the highest non-preview SWE-Bench Pro score
You prefer lower output cost ($25 vs $30 per 1M tokens)

Avoid GPT-5.5 Pro If:

Cost matters: 7x Opus 4.7 output cost makes it niche-only
Your task is simple enough that base models suffice

Summary

GPT-5.5 isn’t a “dud” based on benchmarks. It’s a model optimized for different work. Terminal-Bench 2.0’s 82.7% score reflects genuine capability in sustained, tool-orchestrated coding. Opus 4.7’s 64.3% on SWE-Bench Pro reflects genuine capability in single-shot issue resolution.

The Reddit thread calling GPT-5.5 a disappointment based on SWE-Bench Pro alone missed the point. Terminal-Bench matters for agentic workflows. OSWorld-Verified matters for computer use. FrontierMath Tier 4 matters for hard reasoning. GPT-5.5 wins three of four.

But if you’re a developer looking for the best model to fix your GitHub issues, Opus 4.7 remains the answer. The benchmark gap is real, meaningful, and statistically significant.

Test both on your actual workflow. That’s the only way to know which model fits your specific pattern of work.

| Factor              | GPT-5.5           | Claude Opus 4.7    |
|---------------------|-------------------|--------------------|
| SWE-Bench Pro       | 58.6%             | 64.3% (winner)     |
| Terminal-Bench 2.0  | 82.7% (winner)    | 69.4%              |
| Output Cost         | $30/1M            | $25/1M (lower)     |
| Context Window      | 1M API / 400K Codex| 1M                 |
| UI Generation       | Card grid default | Better hierarchy   |
| Agentic Work        | Optimized         | Good               |
| Best For            | Terminal agents   | Issue resolution   |

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!