Laguna M.1 vs Qwen3.6 Plus

Updated October 25, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Laguna M.1

Poolside

—

Evidence status unavailable

90% interval unavailable

Model B

Qwen3.6 Plus

Alibaba

53.57/100

Supported · Public rank #78

90% interval 44.0–63.2

Shared results: 10
Laguna M.1 only: 0
Qwen3.6 Plus only: 39
Like-for-like categories: 1 / 8

Supported: Qwen3.6 PlusHow 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.

Coding work
Code generation, repair, and software-engineering tasks
Qwen3.6 Plus
Qwen3.6 Plus has the higher public coding point estimate, 40.2 to 25.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.6 Plus
Qwen3.6 Plus 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
Laguna M.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
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: listed-rates
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.

25.9Laguna M.140.2Qwen3.6 Plus

Like-for-like · BenchAlign v5.8

Qwen3.6 Plus has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

MMLU-Pro (Vals)Knowledge
Normalized gap 18.9
Laguna M.1:68.8%
Qwen3.6 Plus:87.7%
Laguna M.1 source Qwen3.6 Plus source
LiveCodeBench (Vals)Coding
Normalized gap 17.9
Laguna M.1:68.1%
Qwen3.6 Plus:86.0%
Laguna M.1 source Qwen3.6 Plus source
Terminal-Bench 2.0Agentic
Normalized gap 15.8
Laguna M.1:45.8%
Qwen3.6 Plus:61.6%
Laguna M.1 source Qwen3.6 Plus source
SWE MultilingualCoding
Normalized gap 10.7
Laguna M.1:63.1%
Qwen3.6 Plus:73.8%
Laguna M.1 source Qwen3.6 Plus source
SWE-bench ProCoding
Normalized gap 7.4
Laguna M.1:49.2%
Qwen3.6 Plus:56.6%
Laguna M.1 source Qwen3.6 Plus source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Coding

Like-for-like

Laguna M.1: 25.9; Supported · #109/153
Qwen3.6 Plus: 40.2; Supported · #68/153
Basis: BenchAlign v5.8 lane · 6 vs 9 public rows
Reading: Qwen3.6 Plus leads · intervals overlap

Agentic

Not comparable

Laguna M.1: Not ranked
Qwen3.6 Plus: 34.5; Supported · #86/132
Basis: BenchAlign v5.8 lane · 2 vs 13 public rows
Reading: Not comparable

Reasoning

Not comparable

Laguna M.1: Not ranked
Qwen3.6 Plus: 73.9; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Laguna M.1: Not ranked
Qwen3.6 Plus: 75.9; #22/56
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Laguna M.1: Not ranked
Qwen3.6 Plus: 52.9; Supported · #68/187
Basis: BenchAlign v5.8 lane · 2 vs 8 public rows
Reading: Not comparable

Multilingual

Not comparable

Laguna M.1: Not ranked
Qwen3.6 Plus: 93.8; #4/17
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Laguna M.1: Not ranked
Qwen3.6 Plus: 85.8; #49/153
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Laguna M.1: Not ranked
Qwen3.6 Plus: 69.5; #6/12
Basis: Provisional lane · 0 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Laguna M.1 and Qwen3.6 Plus
Category	Laguna M.1	Qwen3.6 Plus	Basis	Reading
Coding	25.9Supported · #109/153	40.2Supported · #68/153	Like-for-likeBenchAlign v5.8 lane · 6 vs 9 public rows	Qwen3.6 Plus leads · intervals overlap
Agentic	Not ranked	34.5Supported · #86/132	Not comparableBenchAlign v5.8 lane · 2 vs 13 public rows	Not comparable
Reasoning	Not ranked	73.9Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	75.9#22/56	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Knowledge	Not ranked	52.9Supported · #68/187	Not comparableBenchAlign v5.8 lane · 2 vs 8 public rows	Not comparable
Multilingual	Not ranked	93.8#4/17	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	85.8#49/153	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	69.5#6/12	Not comparableProvisional lane · 0 vs 3 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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

Laguna M.1: API rate not published; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Laguna M.1 has no comparable published API token rate. Qwen3.6 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Laguna M.1: API rate not published; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Laguna M.1 has no comparable published API token rate. Qwen3.6 Plus has no comparable published API token rate.

Cache-heavy agent loop

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

Laguna M.1: API rate not published; Fits in one request; Cached-input rate unavailable
Qwen3.6 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

Laguna M.1 has no comparable published API token rate. Qwen3.6 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.

SpecificationLaguna M.1Qwen3.6 Plus

Context window

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

Laguna M.1

256K

Qwen3.6 Plus

API model ID

Laguna M.1

Not sourced

Qwen3.6 Plus

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Laguna M.1

No comparable hosted API rate

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Laguna M.1

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Laguna M.1

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Laguna M.1

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Laguna M.1

Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Laguna M.1

Proprietary

Qwen3.6 Plus

Proprietary

License

Laguna M.1

Proprietary

Qwen3.6 Plus

Proprietary

Release date

Laguna M.1

2026-04-28

Qwen3.6 Plus

2026-04-02

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: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.6 Plus has the larger documented window (1M).

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

Questions

Which is better, Laguna M.1 or Qwen3.6 Plus?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Laguna M.1 or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public coding point estimate, 40.2 to 25.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Laguna M.1 or Qwen3.6 Plus?

Laguna M.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Laguna M.1 or Qwen3.6 Plus?

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, Laguna M.1 or Qwen3.6 Plus?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence49 rows

Agentic

Terminal-Bench 2.0
Laguna M.145.8%
Source
Qwen3.6 Plus61.6%
Source
Qwen3.6 Plus leads this result
Terminal-Bench 2.1 (Vals)
Laguna M.134.1%
Source
Qwen3.6 Plus53.2%
Source
Qwen3.6 Plus leads this result
Claw-Eval
Laguna M.1—
Qwen3.6 Plus58.8%
Source
Not directly comparable
QwenClawBench
Laguna M.1—
Qwen3.6 Plus57.2%
Source
Not directly comparable
τ³-bench results
Laguna M.1—
Qwen3.6 Plus70.7%
Source
Not directly comparable
VITA-Bench
Laguna M.1—
Qwen3.6 Plus44.3%
Source
Not directly comparable
DeepPlanning
Laguna M.1—
Qwen3.6 Plus41.5%
Source
Not directly comparable
Toolathlon
Laguna M.1—
Qwen3.6 Plus39.8%
Source
Not directly comparable
MCP Atlas
Laguna M.1—
Qwen3.6 Plus48.2%
Source
Not directly comparable
MCP-Tasks
Laguna M.1—
Qwen3.6 Plus74.1%
Source
Not directly comparable
WideResearch
Laguna M.1—
Qwen3.6 Plus74.3%
Source
Not directly comparable
Gert Labs
Laguna M.1—
Qwen3.6 Plus50.60%
Source
Not directly comparable
ResearchClawBench
Laguna M.1—
Qwen3.6 Plus18.0%
Source
Not directly comparable

Coding

SWE-bench Verified
Laguna M.174.6%
Source
Qwen3.6 Plus78.8%
Source
Qwen3.6 Plus leads this result
SWE Multilingual
Laguna M.163.1%
Source
Qwen3.6 Plus73.8%
Source
Qwen3.6 Plus leads this result
SWE-bench Pro
Laguna M.149.2%
Source
Qwen3.6 Plus56.6%
Source
Qwen3.6 Plus leads this result
Terminal-Bench 2.0
Laguna M.145.8%
Source
Qwen3.6 Plus61.6%
Source
Qwen3.6 Plus leads this result
LiveCodeBench (Vals)
Laguna M.168.1%
Source
Qwen3.6 Plus86.0%
Source
Qwen3.6 Plus leads this result
SWE-bench (Vals)
Laguna M.157.6%
Source
Qwen3.6 Plus73.4%
Source
Qwen3.6 Plus leads this result
LiveCodeBench v6
Laguna M.1—
Qwen3.6 Plus87.1%
Source
Not directly comparable
NL2Repo
Laguna M.1—
Qwen3.6 Plus37.9%
Source
Not directly comparable
Vibe Code Bench
Laguna M.1—
Qwen3.6 Plus25.56%
Source
Not directly comparable

Reasoning

AI-Needle
Laguna M.1—
Qwen3.6 Plus68.3%
Source
Not directly comparable
LongBench v2
Laguna M.1—
Qwen3.6 Plus62%
Source
Not directly comparable

Multimodal

MMMU
Laguna M.1—
Qwen3.6 Plus86.0%
Source
Not directly comparable
MMMU-Pro
Laguna M.1—
Qwen3.6 Plus78.8%
Source
Not directly comparable
MathVision
Laguna M.1—
Qwen3.6 Plus88.0%
Source
Not directly comparable
VideoMMMU
Laguna M.1—
Qwen3.6 Plus84.0%
Source
Not directly comparable
ScreenSpot Pro
Laguna M.1—
Qwen3.6 Plus68.2%
Source
Not directly comparable
CharXiv
Laguna M.1—
Qwen3.6 Plus81.5%
Source
Not directly comparable
V*
Laguna M.1—
Qwen3.6 Plus96.9%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Laguna M.127.0%
Source
Qwen3.6 Plus87.4%
Source
Qwen3.6 Plus leads this result
MMLU-Pro (Vals)
Laguna M.168.8%
Source
Qwen3.6 Plus87.7%
Source
Qwen3.6 Plus leads this result
GPQA
Laguna M.1—
Qwen3.6 Plus90.4%
Source
Not directly comparable
SuperGPQA
Laguna M.1—
Qwen3.6 Plus71.6%
Source
Not directly comparable
MMLU-Pro
Laguna M.1—
Qwen3.6 Plus88.5%
Source
Not directly comparable
MMLU-Redux
Laguna M.1—
Qwen3.6 Plus94.5%
Source
Not directly comparable
C-Eval
Laguna M.1—
Qwen3.6 Plus93.3%
Source
Not directly comparable
HLE
Laguna M.1—
Qwen3.6 Plus28.8%
Source
Not directly comparable

Multilingual

MMLU-ProX
Laguna M.1—
Qwen3.6 Plus84.7%
Source
Not directly comparable
NOVA-63
Laguna M.1—
Qwen3.6 Plus57.9%
Source
Not directly comparable

Instruction following

IFEval
Laguna M.1—
Qwen3.6 Plus94.3%
Source
Not directly comparable
IFBench
Laguna M.1—
Qwen3.6 Plus75.8%
Source
Not directly comparable

Math

AIME26
Laguna M.1—
Qwen3.6 Plus95.3%
Source
Not directly comparable
HMMT Feb 2025
Laguna M.1—
Qwen3.6 Plus96.7%
Source
Not directly comparable
HMMT Nov 2025
Laguna M.1—
Qwen3.6 Plus94.6%
Source
Not directly comparable
HMMT Feb 2026
Laguna M.1—
Qwen3.6 Plus87.8%
Source
Not directly comparable
MMAnswerBench
Laguna M.1—
Qwen3.6 Plus83.8%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Laguna M.1—
Qwen3.6 Plus38.246%
Source
Not directly comparable

49 public results · 10 shared

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Last updated October 25, 2026

Laguna M.1 vs Qwen3.6 Plus

Decision reading

Which one for your work

Coding work

Long documents

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

Coding

Agentic

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

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, Laguna M.1 or Qwen3.6 Plus?

Which is better for coding, Laguna M.1 or Qwen3.6 Plus?

Which is better for agentic tasks, Laguna M.1 or Qwen3.6 Plus?

Which costs less, Laguna M.1 or Qwen3.6 Plus?

Which has the larger context window, Laguna M.1 or Qwen3.6 Plus?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Laguna M.1 vs Qwen3.6 Plus