GPT-5.5 vs Claude Opus 4.7: No Single Winner, Just Better Fits
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi... Claude’s public strengths cluster around GPQA, Humanity’s Last Exam, SWE Bench Pro, MCP Atlas an...
Published byEdited with GPT-5.5Images generated with GPT Image 2
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi...
Claude’s public strengths cluster around GPQA, Humanity’s Last Exam, SWE Bench Pro, MCP Atlas and FinanceAgent; GPT 5.5’s strengths cluster around BrowseComp, CyberGym, OSWorld Verified and Terminal Bench 2.0.[3][14]
BenchLM lists both models at $5 input per 1 million tokens, with Claude Opus 4.7 at $25 output and GPT 5.5 at $30 output; production teams should still benchmark accuracy, token consumption and end to end latency on t...
GPT-5.5 vs Claude Opus 4.7 基準測試比較：沒有單一贏家AI 生成示意圖：本文比較 GPT-5.5 與 Claude Opus 4.7 的公開 benchmark、價格與選型訊號。
AI Prompt
Create a landscape editorial hero image for this Studio Global article: GPT-5.5 vs Claude Opus 4.7 基準測試比較：沒有單一贏家. Article summary: 目前公開資料不支持宣布絕對勝負：LLM Stats 稱 Claude Opus 4.7 在 10 個共同回報 benchmark 中領先 6 項、GPT 5.5 領先 4 項，但分數多為 high reasoning tier 自報，BenchLM 也認為重疊資料不足。. Topic tags: ai, ai benchmarks, openai, anthropic, gpt 5 5. Reference image context from search candidates: Reference image 1: visual subject "# GPT-5.5 vs Claude Opus 4.7: Pricing, Speed, Benchmarks. I compared GPT-5.5 against Claude Opus 4.7 on every shared benchmark. Opus 4.7 leads on 6 of 10, GPT-5.5 on 4, with margin" source context "GPT-5.5 vs Claude Opus 4.7: Pricing, Speed, Benchmarks - LLM Stats" Reference image 2: visual subject "# Claude Opus 4.7 vs GPT 5.5: Full Comparison (April 2026). claude-opus-4-7-vs-gpt-5-5. Anthropic dropped Claude Opus 4.7 on April 16. Both with 1M token context windows. Both clai" source
openai.com
The useful question is not which model wins every leaderboard. It is which model fits your workload.
LLM Stats’ research post says that, across 10 benchmarks both providers report, Claude Opus 4.7 leads 6 and GPT-5.5 leads 4. But it also warns that the scores are self-reported at each provider’s high reasoning tier, making them comparable in broad shape rather than in strict methodology. BenchLM is even more cautious: it says it has only partial data and not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Quick verdict
Test Claude Opus 4.7 first for hard reasoning, finance, code repair and review-grade work. LLM Stats lists GPQA, Humanity’s Last Exam, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1 among Claude Opus 4.7’s stronger signals.
Studio Global AI
Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
What is the short answer to "GPT-5.5 vs Claude Opus 4.7: No Single Winner, Just Better Fits"?
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi...
What are the key points to validate first?
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi... Claude’s public strengths cluster around GPQA, Humanity’s Last Exam, SWE Bench Pro, MCP Atlas and FinanceAgent; GPT 5.5’s strengths cluster around BrowseComp, CyberGym, OSWorld Verified and Terminal Bench 2.0.[3][14]
What should I do next in practice?
BenchLM lists both models at $5 input per 1 million tokens, with Claude Opus 4.7 at $25 output and GPT 5.5 at $30 output; production teams should still benchmark accuracy, token consumption and end to end latency on t...
GPT-5.5 vs Claude Opus 4.7: No Single Winner, Just Better Fits
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi... Claude’s public strengths cluster around GPQA, Humanity’s Last Exam, SWE Bench Pro, MCP Atlas an...
Published byEdited with GPT-5.5Images generated with GPT Image 2
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi...
Claude’s public strengths cluster around GPQA, Humanity’s Last Exam, SWE Bench Pro, MCP Atlas and FinanceAgent; GPT 5.5’s strengths cluster around BrowseComp, CyberGym, OSWorld Verified and Terminal Bench 2.0.[3][14]
BenchLM lists both models at $5 input per 1 million tokens, with Claude Opus 4.7 at $25 output and GPT 5.5 at $30 output; production teams should still benchmark accuracy, token consumption and end to end latency on t...
GPT-5.5 vs Claude Opus 4.7 基準測試比較：沒有單一贏家AI 生成示意圖：本文比較 GPT-5.5 與 Claude Opus 4.7 的公開 benchmark、價格與選型訊號。
AI Prompt
Create a landscape editorial hero image for this Studio Global article: GPT-5.5 vs Claude Opus 4.7 基準測試比較：沒有單一贏家. Article summary: 目前公開資料不支持宣布絕對勝負：LLM Stats 稱 Claude Opus 4.7 在 10 個共同回報 benchmark 中領先 6 項、GPT 5.5 領先 4 項，但分數多為 high reasoning tier 自報，BenchLM 也認為重疊資料不足。. Topic tags: ai, ai benchmarks, openai, anthropic, gpt 5 5. Reference image context from search candidates: Reference image 1: visual subject "# GPT-5.5 vs Claude Opus 4.7: Pricing, Speed, Benchmarks. I compared GPT-5.5 against Claude Opus 4.7 on every shared benchmark. Opus 4.7 leads on 6 of 10, GPT-5.5 on 4, with margin" source context "GPT-5.5 vs Claude Opus 4.7: Pricing, Speed, Benchmarks - LLM Stats" Reference image 2: visual subject "# Claude Opus 4.7 vs GPT 5.5: Full Comparison (April 2026). claude-opus-4-7-vs-gpt-5-5. Anthropic dropped Claude Opus 4.7 on April 16. Both with 1M token context windows. Both clai" source
openai.com
The useful question is not which model wins every leaderboard. It is which model fits your workload.
LLM Stats’ research post says that, across 10 benchmarks both providers report, Claude Opus 4.7 leads 6 and GPT-5.5 leads 4. But it also warns that the scores are self-reported at each provider’s high reasoning tier, making them comparable in broad shape rather than in strict methodology. BenchLM is even more cautious: it says it has only partial data and not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Quick verdict
Test Claude Opus 4.7 first for hard reasoning, finance, code repair and review-grade work. LLM Stats lists GPQA, Humanity’s Last Exam, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1 among Claude Opus 4.7’s stronger signals.
Studio Global AI
Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
What is the short answer to "GPT-5.5 vs Claude Opus 4.7: No Single Winner, Just Better Fits"?
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi...
What are the key points to validate first?
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping benchmarks while GPT 5.5 leads 4, but the scores are mostly provider reported at a high reasoning tier; BenchLM also says overlapping data is still too limi... Claude’s public strengths cluster around GPQA, Humanity’s Last Exam, SWE Bench Pro, MCP Atlas and FinanceAgent; GPT 5.5’s strengths cluster around BrowseComp, CyberGym, OSWorld Verified and Terminal Bench 2.0.[3][14]
What should I do next in practice?
BenchLM lists both models at $5 input per 1 million tokens, with Claude Opus 4.7 at $25 output and GPT 5.5 at $30 output; production teams should still benchmark accuracy, token consumption and end to end latency on t...
Test GPT-5.5 first for browsing, terminal, OS, tool-use and long-running agent workflows. LLM Stats lists BrowseComp, CyberGym, OSWorld-Verified and Terminal-Bench 2.0 among GPT-5.5’s stronger signals.
On listed token pricing, Claude is cheaper for output-heavy jobs, while GPT-5.5 has more visible API specs in the sources used here. BenchLM lists both models at $5 per 1 million input tokens, with Claude Opus 4.7 at $25 per 1 million output tokens and GPT-5.5 at $30; OpenAI’s model page gives GPT-5.5’s context window, max output, latency label and supported tools.
At a glance
Dimension
GPT-5.5
Claude Opus 4.7
What to do with it
Public benchmark signal
LLM Stats says GPT-5.5 leads 4 of the 10 overlapping reported benchmarks.
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping reported benchmarks.
Treat Claude as having a slight overall signal, not a clean sweep; the scores are mostly provider-reported at high reasoning tier.
Stronger task types
BrowseComp, CyberGym, OSWorld-Verified and Terminal-Bench 2.0.
FinanceAgent, GPQA, Humanity’s Last Exam, MCP Atlas and SWE-Bench Pro.
Pick by workflow category rather than by a single overall rank.
Listed token pricing
$5 input and $30 output per 1 million tokens.
$5 input and $25 output per 1 million tokens.
Context and output
OpenAI lists a 1M context window and 128K max output tokens for GPT-5.5.
BenchLM lists a 1M context window for Claude Opus 4.7.
Both are listed with 1M context in these sources; only GPT-5.5’s official max output appears in the provided source set.
Tools and latency
OpenAI lists Functions, Web search, File search and Computer use, with latency marked Fast.
BenchLM marks speed and TTFT latency as N/A.
Do not infer that Claude is faster or slower from these fields alone.
What the benchmarks actually suggest
The pattern matters more than the headline count. LLM Stats says Claude Opus 4.7’s leads cluster in reasoning-heavy and review-grade tests, including GPQA Diamond, Humanity’s Last Exam, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1. GPT-5.5’s leads cluster in long-running tool-use tests, including Terminal-Bench 2.0, BrowseComp, OSWorld-Verified and CyberGym.
That split points to different production use cases. If your application needs difficult analysis, financial reasoning, code repair or careful review-style judgement, Claude Opus 4.7 is the more obvious first model to test. If your application depends on web browsing, terminal work, desktop or OS actions, tool calls or multi-step agent loops, GPT-5.5 is the more obvious first model to test.
Anthropic’s own Claude Opus 4.7 announcement adds another useful but limited signal: on its internal research-agent benchmark, Claude Opus 4.7 tied for the top overall score across six modules at 0.715, and its General Finance score improved from 0.767 on Opus 4.6 to 0.813. That supports the finance and research-agent story for Claude, but it is an Anthropic internal comparison, not a direct public head-to-head with GPT-5.5.
Score examples: useful direction, not final ranking
Webreactiva’s comparison lists the following benchmark examples. They line up with the broader category split, but they should still be read alongside the data-quality cautions from BenchLM and LLM Stats.
Benchmark
GPT-5.5
Claude Opus 4.7
Listed leader
Terminal-Bench 2.0
82.7%
69.4%
GPT-5.5
OSWorld-Verified
78.7%
78.0%
GPT-5.5
BrowseComp
84.4%
79.3%
GPT-5.5
SWE-Bench Pro
58.6%
64.3%
Claude Opus 4.7
MCP Atlas
75.3%
79.1%
Claude Opus 4.7
The takeaway is directional: GPT-5.5 looks stronger in terminal, browsing and OS-style tasks, while Claude Opus 4.7 looks stronger in SWE-Bench Pro, MCP, reasoning and finance-oriented tasks. Because the public numbers are not a perfectly controlled, same-methodology head-to-head suite, they should not be treated as a final purchasing decision.
Pricing and API visibility
BenchLM lists both models at $5 per 1 million input tokens. For output, it lists GPT-5.5 at $30 per 1 million tokens and Claude Opus 4.7 at $25 per 1 million tokens. LLM Stats’ comparison page also summarizes Claude Opus 4.7 as about 1.1x cheaper per token.
OpenAI’s model page gives more detail for GPT-5.5: model ID gpt-5.5, positioning as a new class of intelligence for coding and professional work, reasoning effort options of none, low, medium, high and xhigh, a 1M context window, 128K max output tokens, Fast latency, and support for Functions, Web search, File search and Computer use.
Still, listed token price is not the whole production cost. OpenAI’s GPT-5.5 API guide says tool-heavy or long-running workflows should be benchmarked against other models on accuracy, token consumption and end-to-end latency. In practice, that means your own evaluation should measure not just price per token, but how many tokens the model uses, how often it needs retries, how often it fails, and how long the whole workflow takes.
How to choose for your workflow
Start with GPT-5.5 if your product is tool-heavy
GPT-5.5 should be high on your test list if your workflow involves long-running tool use, browsing, terminal sessions, automated OS actions or computer-use tasks. LLM Stats places GPT-5.5’s stronger signals in long-running tool-use tests, and OpenAI’s model page lists support for Functions, Web search, File search and Computer use.
Start with Claude Opus 4.7 if your product is reasoning-heavy
Claude Opus 4.7 should be high on your test list if the work is closer to difficult reasoning, financial analysis, code repair or review-grade evaluation. LLM Stats lists GPQA, Humanity’s Last Exam, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1 as areas where Claude Opus 4.7 shows stronger public signals.
Claude also has the listed price advantage when output tokens dominate the bill: BenchLM lists Claude Opus 4.7 at $25 per 1 million output tokens, compared with $30 for GPT-5.5.
The safest answer is still your own benchmark
Public benchmarks are best used to decide what to test first, not what to buy automatically. A reliable internal evaluation should use real tasks, fixed prompts, the same data, the same tool permissions, the same reasoning settings and a consistent scoring rubric. LLM Stats’ warning about high-reasoning-tier self-reported scores is exactly why those controls matter.
At minimum, compare success rate, error types, token consumption, retry cost and end-to-end latency. OpenAI’s GPT-5.5 guide explicitly recommends benchmarking tool-heavy or long-running workflows against other models on accuracy, token consumption and end-to-end latency.
The final architecture may not be either-or. If your own evaluation confirms the public pattern, you could route reasoning, finance and difficult code-repair tasks to Claude Opus 4.7, while sending browsing, terminal, OS-action and tool-intensive agent workflows to GPT-5.5. That kind of task routing is closer to what the benchmark split suggests than chasing one overall leaderboard rank.
Bottom line
The most defensible reading is that Claude Opus 4.7 has a slight overall edge in current third-party benchmark summaries, while GPT-5.5 looks stronger in long-running tool-use and agent-style workflows. The public data does not support a blanket claim that either model wins across the board.
If you need a starting point, test Claude Opus 4.7 first for reasoning, finance, SWE-Bench Pro-style code work and MCP-style tasks. Test GPT-5.5 first for terminal, browsing, OS operations and tool-heavy agent flows. The production decision should come from your data, your cost model, your latency requirements and your own controlled evaluation.
Test GPT-5.5 first for browsing, terminal, OS, tool-use and long-running agent workflows. LLM Stats lists BrowseComp, CyberGym, OSWorld-Verified and Terminal-Bench 2.0 among GPT-5.5’s stronger signals.
On listed token pricing, Claude is cheaper for output-heavy jobs, while GPT-5.5 has more visible API specs in the sources used here. BenchLM lists both models at $5 per 1 million input tokens, with Claude Opus 4.7 at $25 per 1 million output tokens and GPT-5.5 at $30; OpenAI’s model page gives GPT-5.5’s context window, max output, latency label and supported tools.
At a glance
Dimension
GPT-5.5
Claude Opus 4.7
What to do with it
Public benchmark signal
LLM Stats says GPT-5.5 leads 4 of the 10 overlapping reported benchmarks.
LLM Stats says Claude Opus 4.7 leads 6 of the 10 overlapping reported benchmarks.
Treat Claude as having a slight overall signal, not a clean sweep; the scores are mostly provider-reported at high reasoning tier.
Stronger task types
BrowseComp, CyberGym, OSWorld-Verified and Terminal-Bench 2.0.
FinanceAgent, GPQA, Humanity’s Last Exam, MCP Atlas and SWE-Bench Pro.
Pick by workflow category rather than by a single overall rank.
Listed token pricing
$5 input and $30 output per 1 million tokens.
$5 input and $25 output per 1 million tokens.
Context and output
OpenAI lists a 1M context window and 128K max output tokens for GPT-5.5.
BenchLM lists a 1M context window for Claude Opus 4.7.
Both are listed with 1M context in these sources; only GPT-5.5’s official max output appears in the provided source set.
Tools and latency
OpenAI lists Functions, Web search, File search and Computer use, with latency marked Fast.
BenchLM marks speed and TTFT latency as N/A.
Do not infer that Claude is faster or slower from these fields alone.
What the benchmarks actually suggest
The pattern matters more than the headline count. LLM Stats says Claude Opus 4.7’s leads cluster in reasoning-heavy and review-grade tests, including GPQA Diamond, Humanity’s Last Exam, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1. GPT-5.5’s leads cluster in long-running tool-use tests, including Terminal-Bench 2.0, BrowseComp, OSWorld-Verified and CyberGym.
That split points to different production use cases. If your application needs difficult analysis, financial reasoning, code repair or careful review-style judgement, Claude Opus 4.7 is the more obvious first model to test. If your application depends on web browsing, terminal work, desktop or OS actions, tool calls or multi-step agent loops, GPT-5.5 is the more obvious first model to test.
Anthropic’s own Claude Opus 4.7 announcement adds another useful but limited signal: on its internal research-agent benchmark, Claude Opus 4.7 tied for the top overall score across six modules at 0.715, and its General Finance score improved from 0.767 on Opus 4.6 to 0.813. That supports the finance and research-agent story for Claude, but it is an Anthropic internal comparison, not a direct public head-to-head with GPT-5.5.
Score examples: useful direction, not final ranking
Webreactiva’s comparison lists the following benchmark examples. They line up with the broader category split, but they should still be read alongside the data-quality cautions from BenchLM and LLM Stats.
Benchmark
GPT-5.5
Claude Opus 4.7
Listed leader
Terminal-Bench 2.0
82.7%
69.4%
GPT-5.5
OSWorld-Verified
78.7%
78.0%
GPT-5.5
BrowseComp
84.4%
79.3%
GPT-5.5
SWE-Bench Pro
58.6%
64.3%
Claude Opus 4.7
MCP Atlas
75.3%
79.1%
Claude Opus 4.7
The takeaway is directional: GPT-5.5 looks stronger in terminal, browsing and OS-style tasks, while Claude Opus 4.7 looks stronger in SWE-Bench Pro, MCP, reasoning and finance-oriented tasks. Because the public numbers are not a perfectly controlled, same-methodology head-to-head suite, they should not be treated as a final purchasing decision.
Pricing and API visibility
BenchLM lists both models at $5 per 1 million input tokens. For output, it lists GPT-5.5 at $30 per 1 million tokens and Claude Opus 4.7 at $25 per 1 million tokens. LLM Stats’ comparison page also summarizes Claude Opus 4.7 as about 1.1x cheaper per token.
OpenAI’s model page gives more detail for GPT-5.5: model ID gpt-5.5, positioning as a new class of intelligence for coding and professional work, reasoning effort options of none, low, medium, high and xhigh, a 1M context window, 128K max output tokens, Fast latency, and support for Functions, Web search, File search and Computer use.
Still, listed token price is not the whole production cost. OpenAI’s GPT-5.5 API guide says tool-heavy or long-running workflows should be benchmarked against other models on accuracy, token consumption and end-to-end latency. In practice, that means your own evaluation should measure not just price per token, but how many tokens the model uses, how often it needs retries, how often it fails, and how long the whole workflow takes.
How to choose for your workflow
Start with GPT-5.5 if your product is tool-heavy
GPT-5.5 should be high on your test list if your workflow involves long-running tool use, browsing, terminal sessions, automated OS actions or computer-use tasks. LLM Stats places GPT-5.5’s stronger signals in long-running tool-use tests, and OpenAI’s model page lists support for Functions, Web search, File search and Computer use.
Start with Claude Opus 4.7 if your product is reasoning-heavy
Claude Opus 4.7 should be high on your test list if the work is closer to difficult reasoning, financial analysis, code repair or review-grade evaluation. LLM Stats lists GPQA, Humanity’s Last Exam, SWE-Bench Pro, MCP Atlas and FinanceAgent v1.1 as areas where Claude Opus 4.7 shows stronger public signals.
Claude also has the listed price advantage when output tokens dominate the bill: BenchLM lists Claude Opus 4.7 at $25 per 1 million output tokens, compared with $30 for GPT-5.5.
The safest answer is still your own benchmark
Public benchmarks are best used to decide what to test first, not what to buy automatically. A reliable internal evaluation should use real tasks, fixed prompts, the same data, the same tool permissions, the same reasoning settings and a consistent scoring rubric. LLM Stats’ warning about high-reasoning-tier self-reported scores is exactly why those controls matter.
At minimum, compare success rate, error types, token consumption, retry cost and end-to-end latency. OpenAI’s GPT-5.5 guide explicitly recommends benchmarking tool-heavy or long-running workflows against other models on accuracy, token consumption and end-to-end latency.
The final architecture may not be either-or. If your own evaluation confirms the public pattern, you could route reasoning, finance and difficult code-repair tasks to Claude Opus 4.7, while sending browsing, terminal, OS-action and tool-intensive agent workflows to GPT-5.5. That kind of task routing is closer to what the benchmark split suggests than chasing one overall leaderboard rank.
Bottom line
The most defensible reading is that Claude Opus 4.7 has a slight overall edge in current third-party benchmark summaries, while GPT-5.5 looks stronger in long-running tool-use and agent-style workflows. The public data does not support a blanket claim that either model wins across the board.
If you need a starting point, test Claude Opus 4.7 first for reasoning, finance, SWE-Bench Pro-style code work and MCP-style tasks. Test GPT-5.5 first for terminal, browsing, OS operations and tool-heavy agent flows. The production decision should come from your data, your cost model, your latency requirements and your own controlled evaluation.
