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Model comparison

Qwen3.6-27B vs Trinity-Large-Thinking

Data verified

Head-to-head evidence from 14 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

53.82/100
Margin
1.5pts
← winning
52.32/100
0 category wins0 category wins

Public leaderboard positions: Qwen3.6-27B #93 (Estimated); Trinity-Large-Thinking #100 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Qwen3.6-27B and Trinity-Large-Thinking share 14 comparable benchmark results. 0 of 8 categories are comparable. 40 results are unique to Qwen3.6-27B; 5 to Trinity-Large-Thinking.

Updated July 21, 2026
Shared results
14
Qwen3.6-27B only
40
Trinity-Large-Thinking only
5
Comparable categories
0 / 8

Benchmark data for Qwen3.6-27B and Trinity-Large-Thinking is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 5 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.

Trinity-Large-Thinking is priced at $0.25 input / $0.90 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. Trinity-Large-Thinking has the larger context window at 512K, 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 Qwen3.6-27B and Trinity-Large-Thinking
CategoryQwen3.6-27BΔTrinity-Large-Thinking
AgenticQwen3.6-27B59.3MarginNo overlapTrinity-Large-ThinkingNot measured
CodingQwen3.6-27B77.5MarginNo overlapTrinity-Large-ThinkingNot measured
KnowledgeQwen3.6-27B53.3MarginNo overlapTrinity-Large-ThinkingNot measured
MathQwen3.6-27B89.2MarginNo overlapTrinity-Large-ThinkingNot measured
MultimodalQwen3.6-27B76.7MarginNo overlapTrinity-Large-ThinkingNot measured

Operational comparison

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

MetricQwen3.6-27BTrinity-Large-ThinkingComparison
Input / output priceUSD per 1M tokensQwen3.6-27B$0 input / $0 outputTrinity-Large-Thinking$0.25 input / $0.9 outputQwen3.6-27B has the lower combined listed price.
Generation speedtokens per secondQwen3.6-27BNot availableTrinity-Large-ThinkingNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenQwen3.6-27BNot availableTrinity-Large-ThinkingNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensQwen3.6-27B262KTrinity-Large-Thinking512KTrinity-Large-Thinking lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkQwen3.6-27BTrinity-Large-ThinkingResult
Terminal-Bench 2.0Source 59.3%Not comparable
Claw-EvalSource 72.4%Not comparable
QwenClawBenchSource 53.4%Not comparable
QwenWebBenchSource 1487Not comparable
AndroidWorldSource 70.3%Not comparable
AA Agentic IndexSource 27.0%Not comparable
τ²-bench resultsSource 94.2%90.1%Qwen3.6-27B leads
GDPval-AASource 32.0%2.7%Qwen3.6-27B leads
GDPval-AASource 1140554Qwen3.6-27B leads
Gert LabsSource 54.84%32.55%Qwen3.6-27B leads
Coding
BenchmarkQwen3.6-27BTrinity-Large-ThinkingResult
SWE-bench VerifiedSource 77.2%Not comparable
SWE MultilingualSource 71.3%Not comparable
SWE-bench ProSource 53.5%Not comparable
Terminal-Bench 2.0Source 59.3%Not comparable
LiveCodeBenchSource 83.9%Not comparable
NL2RepoSource 36.2%Not comparable
AA Coding IndexSource 53.7%Not comparable
AA-SciCodeSource 39.8%36.1%Qwen3.6-27B leads
SWE-bench Verified*Source 63.2%Not comparable
Reasoning
BenchmarkQwen3.6-27BTrinity-Large-ThinkingResult
AA-LCRSource 68.7%33.0%Qwen3.6-27B leads
CritPtSource 1.1%0.9%Qwen3.6-27B leads
Knowledge
BenchmarkQwen3.6-27BTrinity-Large-ThinkingResult
MMLU-ProSource 86.2%Not comparable
MMLU-ReduxSource 93.5%Not comparable
SuperGPQASource 66%Not comparable
C-EvalSource 91.4%Not comparable
GPQASource 87.8%Not comparable
HLESource 24%Not comparable
Artificial Analysis Intelligence IndexSource 37.0%24.5%Qwen3.6-27B leads
AA-GPQA DiamondSource 84.2%75.2%Qwen3.6-27B leads
AA-HLESource 21.6%14.7%Qwen3.6-27B leads
AA-Omniscience IndexSource -19.8%-44.2%Qwen3.6-27B leads
AA-Omniscience AccuracySource 19.2%22.8%Trinity-Large-Thinking leads
AA-Omniscience Hallucination RateSource 48.3%86.6%Qwen3.6-27B leads
GPQA-DSource 76.3%Not comparable
MMLU-Pro (Arcee)Source 83.4%Not comparable
Math
BenchmarkQwen3.6-27BTrinity-Large-ThinkingResult
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
AIME25 (Arcee)Source 96.3%Not comparable
Multimodal
BenchmarkQwen3.6-27BTrinity-Large-ThinkingResult
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
CharXivSource 78.4%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
AA-MMMU-ProSource 74.6%Not comparable
Design Arena WebsiteSource 1165Not comparable
Inst. Following
BenchmarkQwen3.6-27BTrinity-Large-ThinkingResult
AA-IFBenchSource 67.6%56.3%Qwen3.6-27B leads
Frequently Asked Questions (3)

Can I compare Qwen3.6-27B and Trinity-Large-Thinking 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 Qwen3.6-27B and Trinity-Large-Thinking today?

Qwen3.6-27B: $0.00 input / $0.00 output per 1M tokens Trinity-Large-Thinking: $0.25 input / $0.90 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

Self-host vs API cost

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

Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even
Trinity-Large-Thinking
API / mo$863
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Model the full break-even

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Last updated: July 21, 2026

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