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Model comparison

Claude Opus 4.7 (Adaptive) vs Qwen3.6-27B

Data verified

Head-to-head evidence from 23 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
12.4pts
← winning
53.82/100
3 category wins1 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and Qwen3.6-27B share 23 comparable benchmark results. 4 of 8 categories are comparable. 15 results are unique to Claude Opus 4.7 (Adaptive); 31 to Qwen3.6-27B.

Updated July 23, 2026
Shared results
23
Claude Opus 4.7 (Adaptive) only
15
Qwen3.6-27B only
31
Comparable categories
4 / 8

Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 23 shared benchmark results across 6 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Claude Opus 4.7 (Adaptive) is clearly ahead on the BenchAlign aggregate, 66.27 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Claude Opus 4.7 (Adaptive)'s sharpest advantage is in agentic, where it averages 75.1 against 59.3. The single biggest benchmark swing on the page is HLE, 54.7% to 24%. Qwen3.6-27B does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 262K for Qwen3.6-27B.

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 Qwen3.6-27B
CategoryClaude Opus 4.7 (Adaptive)ΔQwen3.6-27B
AgenticClaude Opus 4.7 (Adaptive)75.1Margin← 15.8Qwen3.6-27B59.3
MultimodalClaude Opus 4.7 (Adaptive)65.1Margin→ 11.6Qwen3.6-27B76.7
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin← 6.7Qwen3.6-27B53.3
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 1.1Qwen3.6-27B77.5
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapQwen3.6-27BNot measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapQwen3.6-27B89.2

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · Claude Opus 4.7 (Adaptive)B · Qwen3.6-27B

  1. HLE

    Knowledge
    Source ↗
    A 54.7%B 24%
    Winner: Claude Opus 4.7 (Adaptive)Δ 30.7
    HLE: Claude Opus 4.7 (Adaptive) scored 54.7%; Qwen3.6-27B scored 24%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. CharXiv

    Multimodal
    Source ↗
    A 91%B 78.4%
    Winner: Claude Opus 4.7 (Adaptive)Δ 12.6
    CharXiv: Claude Opus 4.7 (Adaptive) scored 91%; Qwen3.6-27B scored 78.4%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  3. SWE-bench Pro

    Coding
    Source ↗
    A 64.3%B 53.5%
    Winner: Claude Opus 4.7 (Adaptive)Δ 10.8
    SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; Qwen3.6-27B scored 53.5%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  4. SWE-bench Verified

    Coding
    Source ↗
    A 87.6%B 77.2%
    Winner: Claude Opus 4.7 (Adaptive)Δ 10.4
    SWE-bench Verified: Claude Opus 4.7 (Adaptive) scored 87.6%; Qwen3.6-27B scored 77.2%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  5. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 59.3%
    Winner: Claude Opus 4.7 (Adaptive)Δ 10.1
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; Qwen3.6-27B scored 59.3%. Claude Opus 4.7 (Adaptive) wins this benchmark.

Operational comparison

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

MetricClaude Opus 4.7 (Adaptive)Qwen3.6-27BComparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputQwen3.6-27B$0 input / $0 outputQwen3.6-27B has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableQwen3.6-27BNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableQwen3.6-27BNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MQwen3.6-27B262KClaude Opus 4.7 (Adaptive) lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)Qwen3.6-27BResult
Terminal-Bench 2.0Source 69.4%59.3%Claude Opus 4.7 (Adaptive) leads
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%27.0%Claude Opus 4.7 (Adaptive) leads
τ²-bench resultsSource 88.6%94.2%Qwen3.6-27B leads
GDPval-AASource 49.8%32.0%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 14951140Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
Claw-EvalSource —72.4%Not comparable
QwenClawBenchSource —53.4%Not comparable
QwenWebBenchSource —1487Not comparable
AndroidWorldSource —70.3%Not comparable
Gert LabsSource —54.84%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)Qwen3.6-27BResult
SWE-bench VerifiedSource 87.6%77.2%Claude Opus 4.7 (Adaptive) leads
SWE-bench ProSource 64.3%53.5%Claude Opus 4.7 (Adaptive) leads
Terminal-Bench 2.0Source 69.4%59.3%Claude Opus 4.7 (Adaptive) leads
AA Coding IndexSource 73.6%53.7%Claude Opus 4.7 (Adaptive) leads
AA-SciCodeSource 54.5%39.8%Claude Opus 4.7 (Adaptive) leads
SWE MultilingualSource —71.3%Not comparable
LiveCodeBenchSource —83.9%Not comparable
NL2RepoSource —36.2%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)Qwen3.6-27BResult
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%68.7%Claude Opus 4.7 (Adaptive) leads
CritPtSource 12.0%1.1%Claude Opus 4.7 (Adaptive) leads
KnowledgeClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)Qwen3.6-27BResult
GPQASource 94.2%87.8%Claude Opus 4.7 (Adaptive) leads
GPQA-DSource 94.2%—Not comparable
HLESource 54.7%24%Claude Opus 4.7 (Adaptive) leads
HLE w/o toolsSource 46.9%—Not comparable
Artificial Analysis Intelligence IndexSource 53.5%37.0%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%84.2%Claude Opus 4.7 (Adaptive) leads
AA-HLESource 39.6%21.6%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience IndexSource 26.2%-19.8%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%19.2%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience Hallucination RateSource 36.2%48.3%Claude Opus 4.7 (Adaptive) leads
MMLU-ProSource —86.2%Not comparable
MMLU-ReduxSource —93.5%Not comparable
SuperGPQASource —66%Not comparable
C-EvalSource —91.4%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)Qwen3.6-27BResult
FrontierMath (legacy)Source 43.8%—Not comparable
HMMT Feb 2025Source —93.8%Not comparable
HMMT Nov 2025Source —90.7%Not comparable
HMMT Feb 2026Source —84.3%Not comparable
MMAnswerBenchSource —80.8%Not comparable
AIME26Source —94.1%Not comparable
MultimodalQwen3.6-27B wins
BenchmarkClaude Opus 4.7 (Adaptive)Qwen3.6-27BResult
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%78.4%Claude Opus 4.7 (Adaptive) leads
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%74.6%Claude Opus 4.7 (Adaptive) leads
Design Arena WebsiteSource 1325—Not comparable
MMMUSource —82.9%Not comparable
MMMU-ProSource —75.8%Not comparable
RealWorldQASource —84.1%Not comparable
DynaMathSource —85.6%Not comparable
MStarSource —81.4%Not comparable
SimpleVQASource —56.1%Not comparable
CC-OCRSource —81.2%Not comparable
CountBenchSource —97.8%Not comparable
RefCOCO (avg)Source —92.5%Not comparable
ERQASource —62.5%Not comparable
Video-MME (with subtitle)Source —87.7%Not comparable
VideoMMMUSource —84.4%Not comparable
MLVU (M-Avg)Source —86.6%Not comparable
V*Source —94.7%Not comparable
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)Qwen3.6-27BResult
AA-IFBenchSource 58.6%67.6%Qwen3.6-27B leads
Frequently Asked Questions (5)

Which is better, Claude Opus 4.7 (Adaptive) or Qwen3.6-27B?

Claude Opus 4.7 (Adaptive) is ahead on BenchLM's BenchAlign leaderboard, 66.27 to 53.82. The biggest single separator in this matchup is HLE, where the scores are 54.7% and 24%.

Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or Qwen3.6-27B?

Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 60 versus 53.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.

Which is better for coding, Claude Opus 4.7 (Adaptive) or Qwen3.6-27B?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 77.5. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or Qwen3.6-27B?

Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 59.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

Which is better for multimodal and grounded tasks, Claude Opus 4.7 (Adaptive) or Qwen3.6-27B?

Qwen3.6-27B has the edge for multimodal and grounded tasks in this comparison, averaging 76.7 versus 65.1. Inside this category, CharXiv is the benchmark that creates the most daylight between them.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Opus 4.7 (Adaptive)
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even—
Model the full break-even

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