BenchLeader
AnthropicReasoning model

Claude Opus 4.7

Reasoning effort

Claude Opus 4.7 is an Anthropic proprietary reasoning model, released 16 Apr 2026. Its best configuration ranks #59 of 756 on the BenchLeader Index at 63.8 ±2.3, in the upper half. The ± is the point: 126 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in human preference (69) and lowest in coding (62). At $10.00 per million tokens blended it is among the most expensive ranked models. Output speed of 48 tokens per second puts it in the slowest quarter, with a first token in 1.6 s. It has been measured at 7 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 2 Oct 2026.

Blended price
$10.00/M
$5.00 in · $25.00 out
Output speed
48 tok/s
OpenRouter traffic, 7-day median; not yet measured by Artificial Analysis
First answer
1.61 s
Context
1M
Full answer
–
Cost per run
–
Released
16 Apr 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index64
  2. Reasoning65
  3. Coding62
  4. Agents & tools67
  5. Maths67
  6. Knowledge62
  7. Human preference69
  8. Multimodal67

Versions

Anthropic has shipped 9 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
Claude Opus 5.5high22 Sept 202670.1#2
Claude Opus 5high24 Jul 202668.4#14
Claude Opus 4.8max28 May 202662.4#82
Claude Opus 4.7this page16 Apr 202663.8#59
Claude Opus 4.617 Dec 202563.1#68
Claude Opus 4.5thinking24 Nov 202560.2#106
Claude Opus 4.1thinking5 Aug 202556.7#181
Claude Opus 4thinking22 May 202555.3#214
Claude 3 Opus4 Mar 202439.3#664

Reasoning-effort configurations

The same model behaves differently depending on how much it is allowed to think. Each row is one setting, scored only on the benchmarks that were run at that setting. “Not stated” collects results from publishers that did not say which setting they used; for a reasoning model that is usually its thinking mode, but we do not assume it. Pick a setting here or at the top of the page to see its price, speed and category scores.

EffortIndexRankSpeedFirst answerChat reply costCategories
no reasoning58.3#15043 tok/s1.09 s$0.0095Agents & tools 81 · Composite 65 · Instruction following 46 · Knowledge 69 · Long context 63 · Multimodal 60 · Reasoning 50
low––48 tok/s1.61 s$0.0095
high61.3#9448 tok/s1.61 s$0.0095Coding 64 · Human preference 70 · Maths 69 · Multimodal 67 · Reasoning 56
xhigh56.6#18648 tok/s1.61 s$0.0095Composite 54 · Knowledge 58 · Maths 60 · Reasoning 65
max62.7#7647 tok/s25 s$0.0095Agents & tools 60 · Coding 66 · Composite 76 · Instruction following 59 · Knowledge 74 · Long context 65 · Maths 59 · Multimodal 62 · Reasoning 62
thinking––48 tok/s1.61 s$0.0095Reasoning 73
not statedbest63.8#5948 tok/s1.61 s$0.0095Agents & tools 67 · Coding 62 · Human preference 69 · Knowledge 62 · Maths 67 · Multimodal 67 · Reasoning 65

How its index has moved

8 Sept 2026 to 5 Oct 2026
566268
  • not stated
  • max
  • high
  • no reasoning

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
GPQA Diamond–––90.2%#4386.4%#72––Epoch AI Benchmarking Hub
Humanity's Last Exam––––––36.2%#9Scale AI / CAIS
SimpleBench––––––61.7%#23SimpleBench
LMArena Hard Prompts––1526#6–––1520#8LMArena
LiveBench Reasoningnot in index–––87.2%#27–––LiveBench
GPQA Diamond (AA)not in index88.5%#86–––91.4%#47––Artificial Analysis
Humanity's Last Exam (AA)not in index33.3%#121–––42.3%#62––Artificial Analysis
GPQA Diamond (Vals)not in index––––––90.2%#25Vals AI
Kagi LLM Benchmark–––––80.7%#773.3%#18Kagi LLM Benchmark
ARC-AGI-3––0.2%#42––––ARC Prize
CritPt5.1%#136–––12.0%#95––Artificial Analysis
Chess Puzzlesnot in index–20.0%#85–30.0%#517.0%#143––Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index––––28.0%#34–13.0%#96Epoch AI Benchmarking Hub
LMCAnot in index––––52.2%#42––Epoch AI Benchmarking Hub
DTBenchnot in index––––94.7%#43––Epoch AI Benchmarking Hub
ForecastBenchnot in index––––––60.3%#28ForecastBench

Coding

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
SWE-bench Verified (Epoch)––––83.5%#1––Epoch AI Benchmarking Hub
SciCode––––54.5%#54––SciCode
WeirdML––76.4%#25–75.5%#28–76.4%#26WeirdML
FrontierCode––––––38.5%#25Cognition
GSO-Bench––44.1%#6–––44.1%#6GSO-Bench
LMArena Coding––1551#4–––1546#6LMArena
LMArena WebDev––1557#33–––1555#35LMArena
LiveBench Codingnot in index–––82.1%#11–––LiveBench
LiveCodeBench––––––85.1%#34Vals AI
SWE-bench (Vals)not in index––––––82.0%#21Vals AI
Code Migrationnot in index––––––43.9%#21Vals AI
ProgramBenchnot in index––––––0.0%#23Vals AI
Vibe Code Bench v1.1not in index––––––71.0%#31Vals AI
SWE Atlas: Codebase QnAnot in index––––––40.3%#11Scale AI SEAL
SWE Atlas: Refactoringnot in index––––––48.6%#4Scale AI SEAL
SWE Atlas: Test Writingnot in index––––––38.5%#13Scale AI SEAL
MirrorCodenot in index––31.1%#5––––Epoch AI Benchmarking Hub
ALE-Benchnot in index––––––1323.0#25Epoch AI Benchmarking Hub
Terminal-Bench 2.1 (AA)not in index––––83.2%#36––Artificial Analysis

Agents & tools

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
Terminal-Bench––––––80.2%#3Terminal-Bench
OSWorld-Verified 2.0––––18.2%#8––OSWorld
APEX-Agents––––49.2%#28––Mercor
LiveBench Agentic Codingnot in index–––50.7%#38–––LiveBench
Terminal-Bench Hard54.5%#15–––51.5%#21––Artificial Analysis
τ²-Bench Telecom (AA)not in index74.0%#143–––88.6%#73––Artificial Analysis
Terminal-Bench 2.1 (Vals)––––––68.5%#31Vals AI
MCP Atlas––––79.1%#13––Scale AI SEAL
HiL-Bench––––––41.7%#5Scale AI SEAL
GDPval (AA)not in index––––41.9%#80––Artificial Analysis
τ²-Bench Banking (AA)––––34.6%#49––Artificial Analysis
ITBench SRE (AA)not in index––––46.7%#11––Artificial Analysis
Analyst Agent (AA)not in index––––43.8%#15––Artificial Analysis
Finance Agent v2not in index––––––51.5%#33Vals AI
Harvey's Legal Agent Benchmarknot in index––––––6.7%#23Vals AI
Legal Research Benchnot in index––––––38.5%#27Vals AI
Tax Agent Benchnot in index––––––63.9%#22Vals AI
Terminal-Bench 2.0 (Vals)not in index––––––68.5%#3Vals AI
LMArena Searchnot in index––––––1212#5LMArena
PostTrainBenchnot in index–––28.6%#8–––Epoch AI Benchmarking Hub
ExploitBenchnot in index––––––26.5%#3Epoch AI Benchmarking Hub
GBAEvalnot in index––––––43.8%#12Epoch AI Benchmarking Hub
Vending-Bench 2not in index––––––10936.8#5Andon Labs

Maths

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
FrontierMath Tiers 1–3––––70.2%#24––Epoch AI Benchmarking Hub
FrontierMath Tier 4––––31.7%#32––Epoch AI Benchmarking Hub
OTIS Mock AIME–––97.8%#2386.7%#78––Epoch AI Benchmarking Hub
ProofBench––––54.0%#25––Vals AI
LiveBench Mathematicsnot in index–––92.8%#24–––LiveBench
AIME (Vals)––––––96.3%#7Vals AI
AIME 2026–––95.8%#13–––MathArena
HMMT February 2026–––93.9%#10–––MathArena
MathArena Apex–––40.6%#7–––MathArena
LMArena Maths––1504#11–––1492#26LMArena

Knowledge

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
SimpleQA Verified–––51.7%#23–––Epoch AI Benchmarking Hub
LiveBench Data Analysisnot in index–––78.3%#24–––LiveBench
AA-Omniscience14.8#78–––27.3#40––Artificial Analysis
MMLU-Pro––––––89.9%#8Vals AI
LegalBench––––––85.3%#22Vals AI
CorpFin––––––66.1%#22Vals AI
TaxEval––––––75.3%#20Vals AI
CaseLaw v2not in index––––––68.4%#5Vals AI
Excel Modeling Benchmarknot in index––––––63.7%#24Vals AI
MedCodenot in index––––––54.9%#7Vals AI
MedScribenot in index––––––83.0%#45Vals AI
EBR-benchnot in index––––19.1%#14––Epoch AI Benchmarking Hub
GDP.pdfnot in index––––21.0%#26––Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index44.7%#90–––48.9%#66––Artificial Analysis
AA-Omniscience: non-hallucinationnot in index45.9%#126–––57.7%#77––Artificial Analysis

Instruction following

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
LiveBench Languagenot in index–––77.9%#39–––LiveBench
LiveBench Instruction Followingnot in index–––66.7%#38–––LiveBench
IFBench43.6%#219–––58.6%#128––Artificial Analysis
LMArena Instruction Followingnot in index––1502#5–––1493#9LMArena

Human preference

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
LMArena Text––1501#5–––1494#10LMArena
EQ-Bench 4––––––1311#5EQ-Bench
LMArena Creative Writingnot in index––1489#6–––1483#9LMArena
LMArena Multi-turnnot in index––1516#5–––1514#6LMArena
LMArena Longer Queriesnot in index––1515#6–––1508#9LMArena

Multimodal

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
LMArena Vision––1313#10–––1316#4LMArena
MMMU-Pro76.4%#84–––78.8%#64––Artificial Analysis
MMMU-Pro (Vals)not in index––––––85.5%#24Vals AI
MortgageTaxnot in index––––––70.3%#3Vals AI
SAGEnot in index––––––56.1%#1Vals AI
Blueprint-Bench 2not in index––––––24.5%#22Epoch AI Benchmarking Hub

Long context

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
AA-LCR75.7%#140–––78.7%#101––Artificial Analysis
LMArena Documentnot in index––1493#5–––1494#3LMArena

Composite

Benchmarkno reasoninglowhighxhighmaxthinkingnot statedSourceTrend
Epoch Capabilities Indexnot in index––––––156.3#22Epoch AI Benchmarking Hub
LiveBench–––76.5%#28–––LiveBench
AA Intelligence Index30.9#103–––40.7#54––Artificial Analysis
Vals Multimodal Indexnot in index––––––67.4%#10Vals AI

Where it wins

Benchmarks where this configuration ranks in the top five of every configuration measured.

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
Google Vertex91 tok/s0.56 s$5.50$27.501M–
Google Vertex76 tok/s1.19 s$5.00$25.001M–
Amazon Bedrock61 tok/s1.32 s$5.00$25.001M–
Anthropic56 tok/s1.50 s$5.00$25.001M–
Claude Platform on AWS48 tok/s1.54 s$5.00$25.001M–

Price history

Listed price per 1M tokens over time, as recorded by OpenRouter for the provider with the longest history.

$0.00$7.56$15.13$22.69$30.25Apr 26May 26Jun 26Jul 26Aug 26Sept 26
input outputnow $5.50 in · $27.50 out

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $0.500 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0095$0.00817.9 s
Summarise a 30-page report12,000 / 600$0.075$0.03514.1 s
Code edit6,000 / 1,500$0.068$0.04732.9 s
Agentic coding session60,000 / 4,000$0.400$0.1981.4 min
Structured extraction2,000 / 200$0.015$0.00835.8 s

See also

Data as of 5 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “Claude Opus 4.7: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/claude-opus-4-7, data as of 5 Oct 2026.