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Model comparison

Claude Opus 4.7 (Adaptive) vs Trinity-Large-Preview

Data verified

Head-to-head evidence from 15 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
10.3pts
← winning
55.94/100
0 category wins0 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); Trinity-Large-Preview #79 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and Trinity-Large-Preview share 15 comparable benchmark results. 0 of 8 categories are comparable. 23 results are unique to Claude Opus 4.7 (Adaptive); 3 to Trinity-Large-Preview.

Updated July 23, 2026
Shared results
15
Claude Opus 4.7 (Adaptive) only
23
Trinity-Large-Preview only
3
Comparable categories
0 / 8

Benchmark data for Claude Opus 4.7 (Adaptive) and Trinity-Large-Preview is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

Claude Opus 4.7 (Adaptive) is priced at $5.00 input / $25.00 output per 1M tokens, versus $0.25 input / $1.00 output per 1M tokens for Trinity-Large-Preview. Claude Opus 4.7 (Adaptive) has the larger context window at 1M, compared with 512K for Trinity-Large-Preview.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Claude Opus 4.7 (Adaptive) and Trinity-Large-Preview
CategoryClaude Opus 4.7 (Adaptive)ΔTrinity-Large-Preview
AgenticClaude Opus 4.7 (Adaptive)75.1MarginNo overlapTrinity-Large-PreviewNot measured
CodingClaude Opus 4.7 (Adaptive)78.6MarginNo overlapTrinity-Large-PreviewNot measured
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapTrinity-Large-PreviewNot measured
KnowledgeClaude Opus 4.7 (Adaptive)60.0MarginNo overlapTrinity-Large-PreviewNot measured
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapTrinity-Large-PreviewNot measured

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewComparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputTrinity-Large-Preview$0.25 input / $1 outputTrinity-Large-Preview has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableTrinity-Large-PreviewNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableTrinity-Large-PreviewNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MTrinity-Large-Preview512KClaude Opus 4.7 (Adaptive) lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewResult
Terminal-Bench 2.0Source 69.4%Not comparable
BrowseCompSource 79.3%Not comparable
MCP AtlasSource 77.3%Not comparable
OSWorld-VerifiedSource 78%Not comparable
CyberGymSource 73.1%Not comparable
AA Agentic IndexSource 44.4%Not comparable
τ²-bench resultsSource 88.6%90.1%Trinity-Large-Preview leads
GDPval-AASource 49.8%2.7%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 1495554Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%Not comparable
JobBenchSource 45.9%Not comparable
AA ITBenchSource 46.7%Not comparable
Coding
BenchmarkClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewResult
SWE-bench VerifiedSource 87.6%Not comparable
SWE-bench ProSource 64.3%Not comparable
Terminal-Bench 2.0Source 69.4%Not comparable
AA Coding IndexSource 73.6%Not comparable
AA-SciCodeSource 54.5%36.1%Claude Opus 4.7 (Adaptive) leads
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewResult
MRCR v2 128K-256KSource 59.2%Not comparable
ARC-AGI-2Source 75.8%Not comparable
AA-LCRSource 70.3%33.0%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%0.9%Claude Opus 4.7 (Adaptive) leads
Knowledge
BenchmarkClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewResult
GPQASource 94.2%Not comparable
GPQA-DSource 94.2%63.3%Claude Opus 4.7 (Adaptive) leads
HLESource 54.7%Not comparable
HLE w/o toolsSource 46.9%Not comparable
Artificial Analysis Intelligence IndexSource 53.5%24.5%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%75.2%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%14.7%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%-44.2%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%22.8%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience Hallucination RateSource 36.2%86.6%Claude Opus 4.7 (Adaptive) leads
MMLUSource 87.2%Not comparable
MMLU-Pro (Arcee)Source 75.2%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewResult
FrontierMath (legacy)Source 43.8%Not comparable
AIME25 (Arcee)Source 24.0%Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewResult
OfficeQA ProSource 43.6%Not comparable
CharXivSource 91%Not comparable
CharXiv w/o toolsSource 82.1%Not comparable
AA-MMMU-ProSource 78.8%Not comparable
Design Arena WebsiteSource 13251165Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)Trinity-Large-PreviewResult
AA-IFBenchSource 58.6%56.3%Claude Opus 4.7 (Adaptive) leads
Frequently Asked Questions (3)

Can I compare Claude Opus 4.7 (Adaptive) and Trinity-Large-Preview on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for Claude Opus 4.7 (Adaptive) and Trinity-Large-Preview today?

Claude Opus 4.7 (Adaptive): $5.00 input / $25.00 output per 1M tokens Trinity-Large-Preview: $0.25 input / $1.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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Last updated: July 23, 2026

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