OpenAI has launched GPT-5.4, the latest iteration of its GPT-5 family, and, as per them, it’s their most capable model to date. But is it the best coding model? Where does it rank among the best of the best? On the SWE-bench leaderboard, Claude Opus 4.6 (Thinking) currently leads at 79.2%, with GPT-5.4 sitting just behind at 77.2%. That 2-point gap sounds small, and in some ways it is, but the full picture is more complicated than any single number can tell you. We brought up Claude Opus 4.6 here because it’s widely considered the best model for coding. But does GPT-5.4 threaten its position? Let’s find that out in this direct GPT-5.4 vs Opus 4.6 coding comparison.
GPT-5.4 vs Opus 4.6 Overview – What These Models Actually Are
GPT-5.4 launched on March 5, 2026, and it is OpenAI’s first general-purpose model to incorporate the coding capabilities of GPT-5.3-Codex. Before this release, developers had to choose between a coding-specialized model or a general reasoning model. GPT-5.4 collapses that choice into one system. It combines frontier coding, reasoning, computer use, and agentic workflows. It rolls out simultaneously across ChatGPT, the API, and the Codex platform.
Claude Opus 4.6 arrived earlier, on February 5, 2026, as Anthropic’s flagship model following Opus 4.5. It introduced adaptive thinking, a 1M-token context window in beta, 128K maximum output tokens, and the highest agentic coding scores Anthropic had published to date. Both models launched into roughly the same market window, and both are clearly targeting the same professional development audience.
Here’s how Opus 4.6 compared against GPT-5.3-Codex:
GPT-5.4 vs Claude Opus 4.6 – Benchmark Comparison
Here is how the two models compare across the benchmarks that matter most for coding and agentic work:
| </> | GPT-5.4 | Claude Opus 4.6 |
| SWE-bench (vals.ai) | 77.2% | 79.2% |
| SWE-bench Verified | ~80.0% | 80.8% |
| SWE-Bench Pro | 57.7% | ~45.9% |
| Terminal-Bench 2.0 | ~65% | 65.4% |
| OSWorld-Verified | 75.0% | 72.7% |
| GPQA Diamond | ~92% | 91.3% |
| ARC-AGI-2 | ~52.9% | 68.8% |
| </> | GPT-5.4 Pro | Claude Opus 4.6 | Difference |
|---|---|---|---|
| GDPval (Knowledge work tasks) | 82.0% | 78.0% | +4.0 |
| BrowseComp (Agentic browsing) | 89.3% | 84.0% | +5.3 |
| GPQA Diamond (Expert scientific reasoning) | 94.4% | 91.3% | +3.1 |
| FrontierMath (Tier 1–3) | 50.0% | 40.7% | +9.3 |
| FrontierMath (Tier 4) | 38.0% | 22.9% | +15.1 |
On SWE-bench, Opus 4.6 holds a narrow lead in real-world GitHub issue resolution. GPT-5.4 counters on SWE-Bench Pro, the harder private-codebase variant, where it scores 57.7% against estimates placing Opus 4.6 closer to the 45-46% range. SWE-Bench Pro was designed to resist benchmark contamination, so that gap is meaningful. On OSWorld, GPT-5.4 edges past human performance at 75.0%, while Opus 4.6 scores 72.7%. Both scores are strong, but GPT-5.4 has a clear lead in desktop navigation. Opus 4.6 dominates on ARC-AGI-2, where its 68.8% nearly doubles GPT-5.2’s 52.9%, reflecting a deeper capacity for novel reasoning that hasn’t yet been matched.
Where GPT-5.4 Is Stronger for Coding
GPT-5.4 has several concrete advantages that matter in daily development work.
- Native computer use in a general model. GPT-5.4 is the first OpenAI general-purpose model with native computer use. It can operate browsers, desktop apps, and software environments through both Playwright code and direct mouse/keyboard commands. This means that coding agents can visually debug web apps in real-time without switching to a separate, specialized model.
- Tool Search cuts token costs by 47%. A new API feature lets GPT-5.4 receive a lightweight tool list and look up full definitions on demand, rather than loading all tool definitions upfront. Across 250 tasks in Scale’s MCP Atlas benchmark, this reduced total token usage by 47% with no accuracy loss. For teams running heavy agentic workflows, that is a direct operating cost reduction.
- Frontend development strength. OpenAI’s internal evaluations show GPT-5.4 beats GPT-5.3-Codex on frontend web development 70% of the time. Partners like Vercel called it the best frontend AI model on both aesthetic and code quality dimensions.
- SWE-Bench Pro performance. On the harder, private-codebase variant of the software engineering benchmark, GPT-5.4 achieves 57.7%. This is the metric that best approximates what production codebases actually look like, and GPT-5.4 currently leads here.
- Speed. In Codex, the /fast mode delivers up to 1.5x faster token velocity using the same model intelligence. For interactive coding workflows like pair programming or rapid iteration, the speed difference is tangible.
Where Claude Opus 4.6 Is Stronger for Coding
Opus 4.6 leads in a different set of dimensions, and they are the ones that tend to matter most for complex, multi-file, longer-horizon work.
- SWE-bench Verified lead. At 80.8% on SWE-bench Verified and 79.2% on the vals.ai leaderboard, Opus 4.6 currently holds the top position for real-world GitHub issue resolution. This benchmark directly tests whether a model can write a valid patch that passes unit tests on a real open-source repository.
- Multi-file reasoning and architectural understanding. Developer evaluations consistently distinguish the two models here. A quote circulated widely from researcher Nathan Lambert after testing both: switching from Opus 4.6 to GPT-5.3-Codex required more detailed prompting for even routine tasks. Opus takes initiative; it infers intent across large, interconnected codebases without needing as much hand-holding.
- ARC-AGI-2 novel reasoning. Opus 4.6’s 68.8% score nearly doubles the best published GPT-5.x result. This matters for the kind of abstract problem decomposition that complex software architecture requires, specifically situations without a memorized solution pattern.
- Agent Teams and multi-agent coordination. Opus 4.6 introduced a native Agent Teams feature for coordinating multiple subagents on a shared task. In Anthropic’s internal testing, combining multi-agent techniques boosted deep research performance by nearly 15 percentage points on their evaluation.
- Long-context reliability at scale. On MRCR v2’s 8-needle 1M variant, Opus 4.6 achieves 76% mean match ratio. For context, Sonnet 4.5 scores 18.5% on the same test. If your codebase is large enough to push past 200K tokens, Opus 4.6 is the only model that consistently maintains reasoning quality across the full window.
Head-to-Head: Real-World Coding Tasks
Third-party evaluations break the picture down further. CodeRabbit ran GPT-5 (GPT-5.0) against Opus 4.x models across 300 pull requests of varying difficulty and found GPT-5 identified 254 out of 300 bugs — about 85% — while other models found between 200 and 207. That is a 16% to 22% gap in bug detection on real code. GPT-5.4 carries forward that code review strength and extends it with better tool use. On the 16x Eval platform’s practical coding tests, results are split by task type: Opus performs better on tasks requiring a nuanced understanding of developer intent, while GPT-5.x edges ahead on structured, well-specified visualization tasks. Neither model dominates everything.
Stack Overflow’s 2025 developer survey adds useful context here. GPT holds 82% overall usage across all developer types, but Claude sits at 45% among professional developers specifically. That breakdown reflects something real: Claude tends to attract developers working on harder, more ambiguous tasks where reasoning depth pays off, while GPT’s larger install base reflects its dominance in everyday, structured coding scenarios.
Building a Landing Page with GPT-5.4 vs Opus 4.6
Using the same prompt, here’s what we got with both GPT-5.4 (Thinking) and Claude Opus 4.6 (Extended Thinking)
Claude Opus 4.6 stands its ground and delivers better results. More consistent, creative, and functional.
GPT-5.4 vs Claude Opus 4.6 Pricing Comparison
Cost matters at scale, and both models carry flagship price tags.
- Claude Opus 4.6 is priced at $5 per million input tokens and $25 per million output tokens.
- GPT-5.4 is priced at $2.50 per million input tokens and $15 per million output tokens on OpenRouter, though enterprise contract pricing varies.
- GPT-5.4 Pro is the most expensive model of the bunch, priced at $30.0 per million input tokens and $180 per million output tokens, making it one of the costliest frontier models to date.
- Claude Sonnet 4.6 is the strong budget alternative here: $3/$15 per million tokens, with a 79.6% SWE-bench score that sits within 1.2 points of Opus 4.6. For teams that want Claude’s reasoning style without the Opus premium, Sonnet 4.6 handles 80% or more of coding tasks at near-identical quality.
For teams already embedded in the OpenAI ecosystem through GitHub Copilot, Cursor, or Codex, GPT-5.4 is a natural upgrade with no friction. For teams using Claude Code, the case for staying on Opus 4.6 remains strong, particularly for complex architectural work, large-context analysis, and agentic workflows involving multiple coordinated agents.
The Bottom Line
There’s no tie between GPT-5.4 and Opus 4.6. Claude Opus 4.6 dominates repository-level benchmarks such as SWE-bench. Choose it for large codebases, deep debugging, and complex reasoning across entire projects.
GPT-5.4 wins when you want aggressive automation, seamless tool chaining, and agent workflows that run pipelines with minimal supervision. No single “best” model exists.
Teams managing large repositories prefer Claude. Automation-heavy teams pick GPT-5.4. But at the same time, single-model loyalty is a handicap. Use them as a matched pair.