Coding
Like-for-like- Qwen3.6-27B
- 42.6
- Supported · #128/183
- Trinity-Large-Thinking
- 28.1
- Supported · #172/183
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Qwen3.6-27B leads · intervals overlap
Qwen3.6-27B vs Trinity-Large-Thinking
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.6-27B has the higher public score estimate, 52.73 versus 47.12, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Coding work
Code generation, repair, and software-engineering tasks
Qwen3.6-27B
Qwen3.6-27B leads on the public coding lane, 42.6 to 28.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Trinity-Large-Thinking
Trinity-Large-Thinking has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Trinity-Large-Thinking is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
37 Qwen3.6-27B only
1 Shared
4 Trinity-Large-Thinking only
- Shared results
- 1
- Qwen3.6-27B only
- 37
- Trinity-Large-Thinking only
- 4
- Like-for-like categories
- 1 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Directional only- Qwen3.6-27B
- 33.9
- Supported · #133/151
- Trinity-Large-Thinking
- 41.3
- Estimated · #117/151
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- Qwen3.6-27B
- 49.0
- Estimated · #95/181
- Trinity-Large-Thinking
- 44.1
- Estimated · #121/181
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- Qwen3.6-27B
- 82.2
- #50/120
- Trinity-Large-Thinking
- 67.5
- #64/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Qwen3.6-27B
- 73.7
- Unranked · 2 rankable rows
- Trinity-Large-Thinking
- 48.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Qwen3.6-27B
- 72.8
- Unranked · 5 rankable rows
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Qwen3.6-27B
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Qwen3.6-27B
- 51.5
- #35/48
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Qwen3.6-27B | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 42.6Supported · #128/183 | 28.1Supported · #172/183 | Like-for-likeBenchAlign lane · 6 vs 1 public rows | Qwen3.6-27B leads · intervals overlap |
| Agentic | 33.9Supported · #133/151 | 41.3Estimated · #117/151 | Directional onlyBenchAlign lane · 6 vs 1 public rows | Directional only |
| Knowledge | 49.0Estimated · #95/181 | 44.1Estimated · #121/181 | Directional onlyBenchAlign lane · 6 vs 2 public rows | Directional only |
| Instruction following | 82.2#50/120 | 67.5#64/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 73.7Unranked · 2 rankable rows | 48.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 72.8Unranked · 5 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 51.5#35/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
Qwen3.6-27B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
Qwen3.6-27B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Qwen3.6-27B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6-27B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationQwen3.6-27BTrinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
Qwen3.6-27B
262K
Trinity-Large-Thinking
512K
API model ID
Qwen3.6-27B
Not sourced
Trinity-Large-Thinking
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Qwen3.6-27B
No comparable hosted API rate
Trinity-Large-Thinking
Not published
Documented inputs
Qwen3.6-27B
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
Qwen3.6-27B
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
Qwen3.6-27B
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
Qwen3.6-27B
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
Qwen3.6-27B
Open Weight
Trinity-Large-Thinking
Open Weight
License
Qwen3.6-27B
Open Weight
Trinity-Large-Thinking
Open Weight
Release date
Qwen3.6-27B
2026-04-21
Trinity-Large-Thinking
2026-03-10
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Qwen3.6-27B has the higher public score estimate, 52.73 versus 47.12, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Trinity-Large-Thinking has the larger documented window (512K).
Run the same representative tasks against both endpoints before changing production traffic.
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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence42 rows
Agentic
Terminal-Bench 2.0
Qwen3.6-27B59.3%SourceTrinity-Large-Thinking—Not directly comparable
Claw-Eval
Qwen3.6-27B72.4%SourceTrinity-Large-Thinking—Not directly comparable
QwenClawBench
Qwen3.6-27B53.4%SourceTrinity-Large-Thinking—Not directly comparable
QwenWebBench
Qwen3.6-27B1487SourceTrinity-Large-Thinking—Not directly comparable
AndroidWorld
Qwen3.6-27B70.3%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
Shared sourceQwen3.6-27B54.84%Trinity-Large-Thinking32.55%Qwen3.6-27B leads this result
Coding
SWE-bench Verified
Qwen3.6-27B77.2%SourceTrinity-Large-Thinking—Not directly comparable
SWE Multilingual
Qwen3.6-27B71.3%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Pro
Qwen3.6-27B53.5%SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 2.0
Qwen3.6-27B59.3%SourceTrinity-Large-Thinking—Not directly comparable
LiveCodeBench
Qwen3.6-27B83.9%SourceTrinity-Large-Thinking—Not directly comparable
NL2Repo
Qwen3.6-27B36.2%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
Qwen3.6-27B—Trinity-Large-Thinking63.2%SourceNot directly comparable
Knowledge
MMLU-Pro
Qwen3.6-27B86.2%SourceTrinity-Large-Thinking—Not directly comparable
MMLU-Redux
Qwen3.6-27B93.5%SourceTrinity-Large-Thinking—Not directly comparable
SuperGPQA
Qwen3.6-27B66%SourceTrinity-Large-Thinking—Not directly comparable
C-Eval
Qwen3.6-27B91.4%SourceTrinity-Large-Thinking—Not directly comparable
GPQA
Qwen3.6-27B87.8%SourceTrinity-Large-Thinking—Not directly comparable
HLE
Qwen3.6-27B24%SourceTrinity-Large-Thinking—Not directly comparable
GPQA-D
Qwen3.6-27B—Trinity-Large-Thinking76.3%SourceNot directly comparable
MMLU-Pro (Arcee)
Qwen3.6-27B—Trinity-Large-Thinking83.4%SourceNot directly comparable
Math
HMMT Feb 2025
Qwen3.6-27B93.8%SourceTrinity-Large-Thinking—Not directly comparable
HMMT Nov 2025
Qwen3.6-27B90.7%SourceTrinity-Large-Thinking—Not directly comparable
HMMT Feb 2026
Qwen3.6-27B84.3%SourceTrinity-Large-Thinking—Not directly comparable
MMAnswerBench
Qwen3.6-27B80.8%SourceTrinity-Large-Thinking—Not directly comparable
AIME26
Qwen3.6-27B94.1%SourceTrinity-Large-Thinking—Not directly comparable
AIME25 (Arcee)
Qwen3.6-27B—Trinity-Large-Thinking96.3%SourceNot directly comparable
Multimodal
MMMU
Qwen3.6-27B82.9%SourceTrinity-Large-Thinking—Not directly comparable
MMMU-Pro
Qwen3.6-27B75.8%SourceTrinity-Large-Thinking—Not directly comparable
RealWorldQA
Qwen3.6-27B84.1%SourceTrinity-Large-Thinking—Not directly comparable
DynaMath
Qwen3.6-27B85.6%SourceTrinity-Large-Thinking—Not directly comparable
MStar
Qwen3.6-27B81.4%SourceTrinity-Large-Thinking—Not directly comparable
SimpleVQA
Qwen3.6-27B56.1%SourceTrinity-Large-Thinking—Not directly comparable
CharXiv
Qwen3.6-27B78.4%SourceTrinity-Large-Thinking—Not directly comparable
CC-OCR
Qwen3.6-27B81.2%SourceTrinity-Large-Thinking—Not directly comparable
CountBench
Qwen3.6-27B97.8%SourceTrinity-Large-Thinking—Not directly comparable
RefCOCO (avg)
Qwen3.6-27B92.5%SourceTrinity-Large-Thinking—Not directly comparable
ERQA
Qwen3.6-27B62.5%SourceTrinity-Large-Thinking—Not directly comparable
Video-MME (with subtitle)
Qwen3.6-27B87.7%SourceTrinity-Large-Thinking—Not directly comparable
VideoMMMU
Qwen3.6-27B84.4%SourceTrinity-Large-Thinking—Not directly comparable
MLVU (M-Avg)
Qwen3.6-27B86.6%SourceTrinity-Large-Thinking—Not directly comparable
V*
Qwen3.6-27B94.7%SourceTrinity-Large-Thinking—Not directly comparable
Frequently asked questions
Which is better, Qwen3.6-27B or Trinity-Large-Thinking?
Qwen3.6-27B has the higher public score estimate, 52.73 versus 47.12, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Qwen3.6-27B or Trinity-Large-Thinking?
Qwen3.6-27B leads the public coding lane, 42.6 to 28.1, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Qwen3.6-27B or Trinity-Large-Thinking?
Trinity-Large-Thinking scores higher for agentic tasks on the public lane, 41.3 to 33.9. Trinity-Large-Thinking is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Qwen3.6-27B or Trinity-Large-Thinking?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Qwen3.6-27B or Trinity-Large-Thinking?
Trinity-Large-Thinking has the larger documented context window: 512K, compared with 262K.
Related comparisons
Last updated September 4, 2026
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