Qwen3.7 Plus vs Trinity-Large-Thinking

Updated September 29, 2026. Rank says Qwen3.7 Plus is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.7 Plus has the higher public score estimate, 55.84 versus 34.23, 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.

Model A

Qwen3.7 Plus

Alibaba

55.84/100

Supported · Public rank #54

90% interval 45.7–66.0

Model B

Trinity-Large-Thinking

Arcee AI

34.23/100

Estimated · Public rank #144

90% interval 13.7–54.8

Shared results: 1
Qwen3.7 Plus only: 51
Trinity-Large-Thinking only: 4
Like-for-like categories: 0 / 8

Supported: Qwen3.7 Plus · Estimated: Trinity-Large-ThinkingHow the comparison works

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.

Long documents
Prompts that approach the documented context limit
Qwen3.7 Plus
Qwen3.7 Plus has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.7 Plus is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

42.9Qwen3.7 Plus21.0Trinity-Large-Thinking

Directional only · BenchAlign v5.7

Qwen3.7 Plus scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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

Qwen3.7 Plus: 35.2; Supported · #66/117
Trinity-Large-Thinking: 17.7; Estimated · #104/117
Basis: BenchAlign v5.7 lane · 11 vs 1 public rows
Reading: Directional only

Coding

Directional only

Qwen3.7 Plus: 42.9; Estimated · #54/142
Trinity-Large-Thinking: 21.0; Supported · #119/142
Basis: BenchAlign v5.7 lane · 7 vs 1 public rows
Reading: Directional only

Knowledge

Directional only

Qwen3.7 Plus: 52.4; Estimated · #55/168
Trinity-Large-Thinking: 35.9; Estimated · #114/168
Basis: BenchAlign v5.7 lane · 7 vs 2 public rows
Reading: Directional only

Instruction following

Directional only

Qwen3.7 Plus: 89.2; #18/124
Trinity-Large-Thinking: 66.3; #68/124
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Qwen3.7 Plus: 75.2; Unranked · 3 rankable rows
Trinity-Large-Thinking: 48.3; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Qwen3.7 Plus: 73.5; #19/50
Trinity-Large-Thinking: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.7 Plus: 78.9; #3/12
Trinity-Large-Thinking: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Qwen3.7 Plus: 78.2; Unranked · 3 rankable rows
Trinity-Large-Thinking: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Qwen3.7 Plus and Trinity-Large-Thinking
Category	Qwen3.7 Plus	Trinity-Large-Thinking	Basis	Reading
Agentic	35.2Supported · #66/117	17.7Estimated · #104/117	Directional onlyBenchAlign v5.7 lane · 11 vs 1 public rows	Directional only
Coding	42.9Estimated · #54/142	21.0Supported · #119/142	Directional onlyBenchAlign v5.7 lane · 7 vs 1 public rows	Directional only
Knowledge	52.4Estimated · #55/168	35.9Estimated · #114/168	Directional onlyBenchAlign v5.7 lane · 7 vs 2 public rows	Directional only
Instruction following	89.2#18/124	66.3#68/124	Directional onlyProvisional lane · 1 vs 0 weighted rows	Directional only
Reasoning	75.2Unranked · 3 rankable rows	48.3Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	73.5#19/50	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multilingual	78.9#3/12	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	78.2Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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

Qwen3.7 Plus: API rate not published; Fits in one request
Trinity-Large-Thinking: $0.0007; Fits in one request

Qwen3.7 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.7 Plus: API rate not published; Fits in one request
Trinity-Large-Thinking: $0.0152; Fits in one request

Qwen3.7 Plus has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Qwen3.7 Plus: API rate not published; 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. Qwen3.7 Plus has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationQwen3.7 PlusTrinity-Large-Thinking

Context window

Maximum documented context; output-token limits may be lower.

Qwen3.7 Plus

Trinity-Large-Thinking

512K

API model ID

Qwen3.7 Plus

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.

Qwen3.7 Plus

No comparable hosted API rate

Trinity-Large-Thinking

Not published

Documented inputs

Qwen3.7 Plus

Not sourced

Trinity-Large-Thinking

Not sourced

Documented outputs

Qwen3.7 Plus

Not sourced

Trinity-Large-Thinking

Not sourced

Provider availability

Qwen3.7 Plus

Not sourced

Trinity-Large-Thinking

Not sourced

Reasoning profile

Qwen3.7 Plus

Reasoning

Trinity-Large-Thinking

Reasoning

Weight access

Qwen3.7 Plus

Proprietary

Trinity-Large-Thinking

Open Weight

License

Qwen3.7 Plus

Proprietary

Trinity-Large-Thinking

Open Weight

Release date

Qwen3.7 Plus

2026-06-03

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: Qwen3.7 Plus has the higher public score estimate, 55.84 versus 34.23, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.7 Plus has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Qwen3.7 Plus or Trinity-Large-Thinking?

Qwen3.7 Plus has the higher public score estimate, 55.84 versus 34.23, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Qwen3.7 Plus or Trinity-Large-Thinking?

Qwen3.7 Plus scores higher for coding on the public lane, 42.9 to 21. Qwen3.7 Plus is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Qwen3.7 Plus or Trinity-Large-Thinking?

Qwen3.7 Plus scores higher for agentic tasks on the public lane, 35.2 to 17.7. 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, Qwen3.7 Plus 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, Qwen3.7 Plus or Trinity-Large-Thinking?

Qwen3.7 Plus has the larger documented context window: 1M, compared with 512K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence56 rows

Agentic

Terminal-Bench 2.0
Qwen3.7 Plus70.3%
Source
Trinity-Large-Thinking—
Not directly comparable
QwenClawBench
Qwen3.7 Plus61.8%
Source
Trinity-Large-Thinking—
Not directly comparable
Claw-Eval
Qwen3.7 Plus62.7%
Source
Trinity-Large-Thinking—
Not directly comparable
BFCL v4
Qwen3.7 Plus72.9%
Source
Trinity-Large-Thinking—
Not directly comparable
MCP Atlas
Qwen3.7 Plus73.2%
Source
Trinity-Large-Thinking—
Not directly comparable
VITA-Bench
Qwen3.7 Plus45.6%
Source
Trinity-Large-Thinking—
Not directly comparable
DeepPlanning
Qwen3.7 Plus62.3%
Source
Trinity-Large-Thinking—
Not directly comparable
OSWorld-Verified
Qwen3.7 Plus73.3%
Source
Trinity-Large-Thinking—
Not directly comparable
AndroidWorld
Qwen3.7 Plus81.0%
Source
Trinity-Large-Thinking—
Not directly comparable
OSWorld 2.0
Qwen3.7 Plus2.8%
Source
Trinity-Large-Thinking—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.7 Plus52.8%
Source
Trinity-Large-Thinking—
Not directly comparable
Gert Labs
Qwen3.7 Plus—
Trinity-Large-Thinking32.55%
Source
Not directly comparable

Coding

Terminal-Bench 2.0
Qwen3.7 Plus70.3%
Source
Trinity-Large-Thinking—
Not directly comparable
SWE-bench Verified
Qwen3.7 Plus77.7%
Source
Trinity-Large-Thinking—
Not directly comparable
SWE-bench Pro
Qwen3.7 Plus57.6%
Source
Trinity-Large-Thinking—
Not directly comparable
SWE Multilingual
Qwen3.7 Plus75.8%
Source
Trinity-Large-Thinking—
Not directly comparable
NL2Repo
Qwen3.7 Plus41.1%
Source
Trinity-Large-Thinking—
Not directly comparable
SciCode
Qwen3.7 Plus51.3%
Source
Trinity-Large-Thinking—
Not directly comparable
LiveCodeBench
Qwen3.7 Plus89.6%
Source
Trinity-Large-Thinking—
Not directly comparable
SWE-bench Verified*
Qwen3.7 Plus—
Trinity-Large-Thinking63.2%
Source
Not directly comparable

Reasoning

CritPt
Qwen3.7 Plus9.1%
Source
Trinity-Large-Thinking—
Not directly comparable
MRCRv2
Qwen3.7 Plus91.7%
Source
Trinity-Large-Thinking—
Not directly comparable

Multimodal

MMMU-Pro
Qwen3.7 Plus79%
Source
Trinity-Large-Thinking—
Not directly comparable
MathVision
Qwen3.7 Plus90.3%
Source
Trinity-Large-Thinking—
Not directly comparable
CharXiv
Qwen3.7 Plus85.9%
Source
Trinity-Large-Thinking—
Not directly comparable
ERQA
Qwen3.7 Plus69.8%
Source
Trinity-Large-Thinking—
Not directly comparable
MedXpertQA (MM)
Qwen3.7 Plus71.0%
Source
Trinity-Large-Thinking—
Not directly comparable
ScreenSpot Pro
Qwen3.7 Plus79.0%
Source
Trinity-Large-Thinking—
Not directly comparable
SimpleVQA
Qwen3.7 Plus81.7%
Source
Trinity-Large-Thinking—
Not directly comparable
MMSearch-Plus
Qwen3.7 Plus41.4%
Source
Trinity-Large-Thinking—
Not directly comparable
RealWorldQA
Qwen3.7 Plus86.9%
Source
Trinity-Large-Thinking—
Not directly comparable
OmniDocBench 1.5
Qwen3.7 Plus91.4%
Source
Trinity-Large-Thinking—
Not directly comparable
OCRBench V2
Qwen3.7 Plus70.7%
Source
Trinity-Large-Thinking—
Not directly comparable
ODINW13
Qwen3.7 Plus51.1%
Source
Trinity-Large-Thinking—
Not directly comparable
Video-MME (with subtitle)
Qwen3.7 Plus88.0%
Source
Trinity-Large-Thinking—
Not directly comparable
VideoMMMU
Qwen3.7 Plus85.4%
Source
Trinity-Large-Thinking—
Not directly comparable
MLVU (M-Avg)
Qwen3.7 Plus87.4%
Source
Trinity-Large-Thinking—
Not directly comparable

Knowledge

GPQA
Qwen3.7 Plus90.3%
Source
Trinity-Large-Thinking—
Not directly comparable
GPQA-D
Qwen3.7 Plus90.3%
Source
Trinity-Large-Thinking76.3%
Source
Qwen3.7 Plus leads this result
HLE
Qwen3.7 Plus34.7%
Source
Trinity-Large-Thinking—
Not directly comparable
MMLU-Pro
Qwen3.7 Plus88.5%
Source
Trinity-Large-Thinking—
Not directly comparable
MMLU-Redux
Qwen3.7 Plus94.5%
Source
Trinity-Large-Thinking—
Not directly comparable
SuperGPQA
Qwen3.7 Plus71.4%
Source
Trinity-Large-Thinking—
Not directly comparable
MMMLU
Qwen3.7 Plus89.0%
Source
Trinity-Large-Thinking—
Not directly comparable
MMLU-Pro (Arcee)
Qwen3.7 Plus—
Trinity-Large-Thinking83.4%
Source
Not directly comparable

Multilingual

MMLU-ProX
Qwen3.7 Plus85.4%
Source
Trinity-Large-Thinking—
Not directly comparable
NOVA-63
Qwen3.7 Plus58.8%
Source
Trinity-Large-Thinking—
Not directly comparable
INCLUDE
Qwen3.7 Plus83.0%
Source
Trinity-Large-Thinking—
Not directly comparable
MAXIFE
Qwen3.7 Plus88.8%
Source
Trinity-Large-Thinking—
Not directly comparable
PolyMath
Qwen3.7 Plus84.0%
Source
Trinity-Large-Thinking—
Not directly comparable

Instruction following

IFEval
Qwen3.7 Plus94.6%
Source
Trinity-Large-Thinking—
Not directly comparable
IFBench
Qwen3.7 Plus79.1%
Source
Trinity-Large-Thinking—
Not directly comparable

Math

HMMT Feb 2026
Qwen3.7 Plus92.9%
Source
Trinity-Large-Thinking—
Not directly comparable
IMOAnswerBench
Qwen3.7 Plus86.0%
Source
Trinity-Large-Thinking—
Not directly comparable
Apex
Qwen3.7 Plus22.7%
Source
Trinity-Large-Thinking—
Not directly comparable
AIME25 (Arcee)
Qwen3.7 Plus—
Trinity-Large-Thinking96.3%
Source
Not directly comparable

56 public results · 1 shared

Related comparisons

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Last updated September 29, 2026

Qwen3.7 Plus vs Trinity-Large-Thinking

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Qwen3.7 Plus or Trinity-Large-Thinking?

Which is better for coding, Qwen3.7 Plus or Trinity-Large-Thinking?

Which is better for agentic tasks, Qwen3.7 Plus or Trinity-Large-Thinking?

Which costs less, Qwen3.7 Plus or Trinity-Large-Thinking?

Which has the larger context window, Qwen3.7 Plus or Trinity-Large-Thinking?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Qwen3.7 Plus vs Trinity-Large-Thinking