Claude Opus 4.7 vs GPT-5.4: Agentic Coding Compared
Claude Opus 4.7 beats GPT-5.4 on SWE-bench Pro, tool use, and computer use. Full agentic coding benchmark comparison with migration guidance.
SWE-bench Pro Lead
MCP-Atlas Lead
Opus 4.7 Price
Released
Key Takeaways
What Changed on April 16, 2026
Anthropic released Claude Opus 4.7 on April 16, 2026, and for the first time since GPT-5.4 shipped, the state-of-the-art crown on agentic coding has meaningfully moved. Opus 4.7 now leads on SWE-bench Pro, MCP-Atlas scaled tool use, OSWorld-Verified computer use, Finance Agent v1.1, and CyberGym — while holding essentially tied on pure reasoning and ceding ground on agentic search. This post breaks the comparison down benchmark by benchmark and draws out what it means for agency teams choosing between the two models.
For the full Opus 4.7 release context — pricing, migration notes, API breaking changes, and partner reports — see our Claude Opus 4.7 complete guide. For broader context on frontier model comparisons, see the Qwen 3.6 Plus vs Claude Opus 4.6 vs GPT-5.4 comparison.
About the data: Every number in this comparison comes from Anthropic's official Opus 4.7 release table. Scores are third-party verified where stated, and GPT-5.4 figures are from OpenAI's reported results — mostly on their GPT-5.4 Pro tier. Source: Anthropic announcement.
Benchmark-by-Benchmark Breakdown
The following table isolates the directly comparable benchmarks between Opus 4.7 and GPT-5.4 from Anthropic's release data. SWE-bench Verified and CharXiv Reasoning lack a published GPT-5.4 score and are excluded from the head-to-head tally.
| Benchmark | Opus 4.7 | GPT-5.4 | Winner |
|---|---|---|---|
| SWE-bench Pro (agentic coding) | 64.3% | 57.7% | Opus 4.7 (+6.6) |
| Terminal-Bench 2.0 (terminal coding) | 69.4% | 75.1%* | GPT-5.4 (caveated) |
| Humanity's Last Exam (no tools) | 46.9% | 42.7% | Opus 4.7 (+4.2) |
| Humanity's Last Exam (with tools) | 54.7% | 58.7% | GPT-5.4 (+4.0) |
| BrowseComp (agentic search) | 79.3% | 89.3% | GPT-5.4 (+10.0) |
| MCP-Atlas (scaled tool use) | 77.3% | 68.1% | Opus 4.7 (+9.2) |
| OSWorld-Verified (computer use) | 78.0% | 75.0% | Opus 4.7 (+3.0) |
| Finance Agent v1.1 | 64.4% | 61.5% | Opus 4.7 (+2.9) |
| CyberGym (vuln reproduction) | 73.1% | 66.3% | Opus 4.7 (+6.8) |
| GPQA Diamond (graduate reasoning) | 94.2% | 94.4% | Tie (+0.2) |
* Terminal-Bench 2.0 for GPT-5.4 is self-reported on OpenAI's own harness rather than an Anthropic-run evaluation. Cross-harness scores are directional, not like-for-like.
Counting only the directly comparable rows: Opus 4.7 wins 6, GPT-5.4 wins 3, and GPQA Diamond is effectively tied. The size of the wins also matters — Opus 4.7's biggest margins (MCP-Atlas +9.2, CyberGym +6.8, SWE-bench Pro +6.6) are larger than GPT-5.4's (BrowseComp +10.0, HLE w/ tools +4.0, Terminal-Bench 2.0 +5.7 caveated).
Where Opus 4.7 Pulls Ahead
The five benchmarks where Opus 4.7 has a clear lead all cluster around the same theme: long-horizon agentic work with substantial tool use. These are the workloads that dominate modern AI-assisted engineering, and the margins are not narrow.
The industry standard for agentic coding evaluation. Opus 4.7's 6.6-point lead over GPT-5.4 is backed by partner reports — Cursor's CursorBench jumped from 58% on Opus 4.6 to over 70% on 4.7, and GitHub measured a 13% lift on their 93-task benchmark.
The largest margin in Opus 4.7's favor. Measures how well a model orchestrates many tools across many MCP servers — the exact workload production agents run. Critical for any agency building Claude-powered automation.
Measures end-to-end computer-use capability. Combined with Opus 4.7's 2,576px vision and 1:1 coordinate mapping, browser and desktop automation becomes materially more reliable than GPT-5.4's harness.
A newer benchmark focused on multi-step financial analysis tasks with calculation tools, document lookups, and chain reasoning. Relevant for agencies building fintech automations or deal-room copilots.
Legitimate security research, with Opus 4.7's Cyber Verification Program unlocking the capability behind real-time safeguards. GPT-5.4 is 6.8 points behind on reproducing known vulnerabilities for defensive work.
Humanity's Last Exam without tools is the purest test of built-in reasoning. The 4.2-point lead suggests Opus 4.7 carries more useful knowledge and reasoning internally, before either model reaches for a tool.
Where GPT-5.4 Still Leads
Opus 4.7 did not win everything. Three benchmarks still favor GPT-5.4, and one of them (BrowseComp) is by a meaningful margin that teams running web-research agents need to take seriously.
BrowseComp: 89.3% vs 79.3% (GPT-5.4 +10.0)
The single largest gap in OpenAI's favor. BrowseComp measures agentic web search and synthesis, and Opus 4.7 actually regressed from Opus 4.6's 83.7% here. For production research pipelines that lean heavily on browsing, synthesizing across many sources, and maintaining source grounding, GPT-5.4 Pro is still the stronger default. Multi-model setups that route browse-heavy queries to GPT-5.4 and coding-heavy queries to Opus 4.7 are a reasonable pattern.
Terminal-Bench 2.0: 75.1% vs 69.4% (GPT-5.4 +5.7 caveated)
Terminal-Bench 2.0 is a benchmark where the agent harness does a lot of the work, and OpenAI's reported 75.1% is on their own harness rather than an Anthropic-run evaluation. The 5.7-point gap is directional rather than a like-for-like measure. In practice, SWE-bench Pro and MCP-Atlas are more representative of the shell-plus-tools work real coding agents do, and Opus 4.7 leads on both.
HLE (with tools): 58.7% vs 54.7% (GPT-5.4 +4.0)
GPT-5.4 Pro still edges Opus 4.7 on Humanity's Last Exam when tools are available. Interesting because Opus 4.7 leads on the no-tools variant — GPT-5.4 closes the gap and pulls ahead once allowed to use calculators and search. For research or analyst workflows where tool access is the norm, GPT-5.4 Pro is slightly stronger.
What This Means for Agency Coding Work
For agencies running AI-assisted delivery, the shape of Opus 4.7's advantages maps directly onto the workloads that dominate client projects. A few concrete implications:
- Coding copilots and PR review: SWE-bench Pro leadership, combined with partner reports from Cursor, GitHub, CodeRabbit, and Warp, makes Opus 4.7 the default for any agency embedding an AI coding layer in client delivery.
- Multi-tool agents and MCP integrations: the 9.2-point MCP-Atlas lead matters most here. If you're building a client agent that orchestrates many tools, Opus 4.7 behaves meaningfully more reliably than GPT-5.4.
- Computer-use and RPA-style automation: Opus 4.7 pairs 2,576px image resolution and 1:1 pixel-coordinate mapping with its OSWorld-Verified lead. Browser and desktop automation that was unreliable on Opus 4.6 becomes viable.
- Financial and analyst workflows: Finance Agent v1.1 leadership and improved .docx redlining and .pptx editing make Opus 4.7 a natural fit for document-heavy fintech, legal, and consulting clients.
- Web-research and content-synthesis pipelines: keep or A/B test GPT-5.4 here. BrowseComp is the one place OpenAI holds real ground.
Building AI-assisted client workflows on the new models? Digital Applied's AI Digital Transformation service maps model strengths to specific client workloads, from prompt engineering to production rollout.
Pricing and Throughput
Both models are premium tier, but the pricing dynamics are worth looking at carefully because benchmark scores alone do not determine total cost of ownership.
| Dimension | Claude Opus 4.7 | GPT-5.4 Pro |
|---|---|---|
| Input price (per 1M tokens) | $5 | Tier-dependent (Pro higher) |
| Output price (per 1M tokens) | $25 | Tier-dependent (Pro higher) |
| Context window | 1M tokens, standard pricing | Tier-dependent |
| Tokenizer | New; 1.0–1.35x tokens vs Opus 4.6 | GPT-5 family tokenizer |
| Effort control | low / medium / high / xhigh / max | Reasoning effort parameter |
| Task budgets | Public beta, advisory cap | — |
Two practical notes for cost modelling. First, Opus 4.7's new tokenizer can map the same English input to up to 35% more tokens than Opus 4.6, which directly raises input cost and context usage. Anthropic's internal evaluations show net-favorable token economics on coding workloads, but measure on your own traffic before committing budget. Second, most of OpenAI's published GPT-5.4 benchmark numbers are on the Pro tier, which is the expensive end of OpenAI's pricing — the base GPT-5.4 tier is cheaper but benchmarks correspondingly lower.
Opus 4.7's task budgets feature, which lets developers give the model an advisory token cap across a full agentic loop, has no direct equivalent at GPT-5.4. For long-running agents where bounding cost per task matters, that's a meaningful operational advantage for Anthropic.
Recommendation: Which to Use When
The one-liner: Opus 4.7 is the default for agentic coding, tool-heavy agents, and computer-use workflows, while GPT-5.4 Pro keeps the lead on browse-heavy research. Beyond the one-liner, the detailed call:
| Workload | Recommended | Reason |
|---|---|---|
| Coding copilots, PR review, refactor bots | Opus 4.7 | +6.6 on SWE-bench Pro; partner wins from Cursor, GitHub, CodeRabbit |
| Multi-tool agents (MCP, API orchestration) | Opus 4.7 | +9.2 on MCP-Atlas — the largest gap in the comparison |
| Computer-use / UI automation | Opus 4.7 | OSWorld lead plus 2576px vision and 1:1 coordinates |
| Financial analysis, document redlining | Opus 4.7 | Finance Agent lead + docx/pptx improvements |
| Agentic web research and synthesis | GPT-5.4 Pro | +10.0 on BrowseComp — a real gap |
| Tool-assisted research / analyst queries | GPT-5.4 Pro (narrow) | +4.0 on HLE with tools; measure both for your workload |
| Pure graduate-level reasoning | Either | GPQA Diamond 94.2% vs 94.4% — effectively tied |
For agencies running more than one workload — which is most of them — a multi-model routing setup often beats picking one provider. Opus 4.7 for coding and multi-tool work, GPT-5.4 Pro for browse-heavy synthesis, and smaller models like Haiku or GPT-5.4-Mini for low-stakes classification tasks is a reasonable baseline stack.
Conclusion
Claude Opus 4.7's release on April 16, 2026 moves the state-of-the-art on agentic coding back to Anthropic. Opus 4.7 wins 6 of 9 directly comparable benchmarks against GPT-5.4, ties on the seventh, and the size of the wins — +9.2 on MCP-Atlas, +6.8 on CyberGym, +6.6 on SWE-bench Pro — is larger than GPT-5.4's wins outside of BrowseComp. For the long-horizon, tool-heavy coding work that dominates modern AI-assisted engineering, Opus 4.7 is the new default.
GPT-5.4 still holds real ground on agentic search, so the right answer for most production stacks is not a full swap but a measured, workload-by-workload routing decision. Teams already invested in GPT-5.4 should run a structured pilot before flipping traffic; teams greenfielding new Claude or OpenAI projects should default to Opus 4.7 for anything coding or tool-use shaped.
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