Laguna S 2.1 vs Qwen3.8 Max

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.

3 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 S 2.1

Poolside

—

Evidence status unavailable

90% interval unavailable

Model B

Qwen3.8 Max

Alibaba

71.76/100

Supported · Public rank #11

90% interval 68.1–75.4

Updated September 15, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Share or export

Share on X LinkedInSocial card CSV JSON

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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Laguna S 2.1 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
Laguna S 2.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

3 Laguna S 2.1 only

3 Shared

57 Qwen3.8 Max only

Shared results: 3
Laguna S 2.1 only: 3
Qwen3.8 Max only: 57
Like-for-like categories: 0 / 8

2 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

Laguna S 2.1: 46.4; Estimated · #79/153
Qwen3.8 Max: 67.3; Supported · #9/153
Basis: BenchAlign lane · 2 vs 15 public rows
Reading: Directional only

Coding

Directional only

Laguna S 2.1: 46.0; Estimated · #87/152
Qwen3.8 Max: 60.8; Supported · #20/152
Basis: BenchAlign lane · 4 vs 12 public rows
Reading: Directional only

Reasoning

Not comparable

Laguna S 2.1: Not ranked
Qwen3.8 Max: 86.5; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Laguna S 2.1: Not ranked
Qwen3.8 Max: 68.8; Supported · #17/183
Basis: BenchAlign lane · 0 vs 6 public rows
Reading: Not comparable

Math

Not comparable

Laguna S 2.1: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Laguna S 2.1: Not ranked
Qwen3.8 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Laguna S 2.1: Not ranked
Qwen3.8 Max: 87.4; #5/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Laguna S 2.1: Not ranked
Qwen3.8 Max: 90.7; #18/123
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Laguna S 2.1 and Qwen3.8 Max
Category	Laguna S 2.1	Qwen3.8 Max	Basis	Reading
Agentic	46.4Estimated · #79/153	67.3Supported · #9/153	Directional onlyBenchAlign lane · 2 vs 15 public rows	Directional only
Coding	46.0Estimated · #87/152	60.8Supported · #20/152	Directional onlyBenchAlign lane · 4 vs 12 public rows	Directional only
Reasoning	Not ranked	86.5Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Knowledge	Not ranked	68.8Supported · #17/183	Not comparableBenchAlign lane · 0 vs 6 public rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	87.4#5/48	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Instruction following	Not ranked	90.7#18/123	Not comparableProvisional lane · 0 vs 1 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.

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.

SWE-bench Pro
Coding
Laguna S 2.1: 59.4%Qwen3.8 Max: 67.7%Normalized gap 8.3Laguna S 2.1 source Qwen3.8 Max source

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 S 2.1: $0.0002; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Laguna S 2.1: $0.0056; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Laguna S 2.1: $0.006; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.8 Max 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.

SpecificationLaguna S 2.1Qwen3.8 Max

Context window

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

Laguna S 2.1

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

Laguna S 2.1

Not sourced

Qwen3.8 Max

qwen3.8-max

Alibaba Cloud Model Studio pricing

Cached-input rate

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

Laguna S 2.1

$0.01 per 1M cached input tokens

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Laguna S 2.1

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Laguna S 2.1

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Laguna S 2.1

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Laguna S 2.1

Reasoning

Qwen3.8 Max

Reasoning

Weight access

Laguna S 2.1

Open Weight

Qwen3.8 Max

Open Weight

License

Laguna S 2.1

Open Weight

Qwen3.8 Max

Open Weight

Release date

Laguna S 2.1

2026-07-21

Qwen3.8 Max

2026-08-03

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: Both models list 1M.

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

Benchmark evidence

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

Browse raw public benchmark evidence63 rows

Agentic

Terminal-Bench 2.0
Laguna S 2.170.2%
Source
Qwen3.8 Max—
Not directly comparable
Toolathlon-Verified
Laguna S 2.149.7%
Source
Qwen3.8 Max72.5%
Source
Qwen3.8 Max leads this result
Terminal-Bench 2.1
Laguna S 2.1—
Qwen3.8 Max86.6%
Source
Not directly comparable
CoWorkBench
Laguna S 2.1—
Qwen3.8 Max74.8%
Source
Not directly comparable
JobBench
Laguna S 2.1—
Qwen3.8 Max53.4%
Source
Not directly comparable
skillsBench
Laguna S 2.1—
Qwen3.8 Max70.2%
Source
Not directly comparable
Agents' Last Exam
Laguna S 2.1—
Qwen3.8 Max52.4%
Source
Not directly comparable
AutomationBench
Laguna S 2.1—
Qwen3.8 Max27.3%
Source
Not directly comparable
WideResearch
Laguna S 2.1—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
Laguna S 2.1—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
Laguna S 2.1—
Qwen3.8 Max86.1%
Source
Not directly comparable
OSWorld 2.0
Laguna S 2.1—
Qwen3.8 Max19.4%
Source
Not directly comparable
WebArena-Verified
Laguna S 2.1—
Qwen3.8 Max66.8%
Source
Not directly comparable
AndroidWorld
Laguna S 2.1—
Qwen3.8 Max85.3%
Source
Not directly comparable
MobileWorld
Laguna S 2.1—
Qwen3.8 Max77.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Laguna S 2.1—
Qwen3.8 Max67.4%
Source
Not directly comparable

Coding

Terminal-Bench 2.0
Laguna S 2.170.2%
Source
Qwen3.8 Max—
Not directly comparable
SWE Multilingual
Laguna S 2.178.5%
Source
Qwen3.8 Max—
Not directly comparable
SWE-bench Pro
Laguna S 2.159.4%
Source
Qwen3.8 Max67.7%
Source
Qwen3.8 Max leads this result
DeepSWE
Laguna S 2.140.4%
Source
Qwen3.8 Max56.6%
Source
Qwen3.8 Max leads this result
Terminal-Bench 2.1
Laguna S 2.1—
Qwen3.8 Max86.6%
Source
Not directly comparable
NL2Repo
Laguna S 2.1—
Qwen3.8 Max55.9%
Source
Not directly comparable
FrontierSWE
Laguna S 2.1—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
Laguna S 2.1—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
Laguna S 2.1—
Qwen3.8 Max93.0%
Source
Not directly comparable
VulcanBench v3
Laguna S 2.1—
Qwen3.8 Max81.2%
Source
Not directly comparable
OpenHarmony Bench
Laguna S 2.1—
Qwen3.8 Max60.8%
Source
Not directly comparable
FrontierSWE v2
Laguna S 2.1—
Qwen3.8 Max15.8%
Source
Not directly comparable
LiveCodeBench (Vals)
Laguna S 2.1—
Qwen3.8 Max87.9%
Source
Not directly comparable
SWE-bench (Vals)
Laguna S 2.1—
Qwen3.8 Max85.6%
Source
Not directly comparable

Reasoning

MRCRv2
Laguna S 2.1—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
Laguna S 2.1—
Qwen3.8 Max66.3%
Source
Not directly comparable

Knowledge

GPQA
Laguna S 2.1—
Qwen3.8 Max92.6%
Source
Not directly comparable
GPQA-D
Laguna S 2.1—
Qwen3.8 Max92.6%
Source
Not directly comparable
HLE
Laguna S 2.1—
Qwen3.8 Max43.6%
Source
Not directly comparable
HLE w/o tools
Laguna S 2.1—
Qwen3.8 Max43.6%
Source
Not directly comparable
GPQA Diamond (Vals)
Laguna S 2.1—
Qwen3.8 Max93.7%
Source
Not directly comparable
MMLU-Pro (Vals)
Laguna S 2.1—
Qwen3.8 Max88.6%
Source
Not directly comparable

Multimodal

MMMU-Pro
Laguna S 2.1—
Qwen3.8 Max82.3%
Source
Not directly comparable
MathVision
Laguna S 2.1—
Qwen3.8 Max95.2%
Source
Not directly comparable
MathVision w/ Python
Laguna S 2.1—
Qwen3.8 Max97.7%
Source
Not directly comparable
BabyVision
Laguna S 2.1—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
Laguna S 2.1—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
Laguna S 2.1—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
Laguna S 2.1—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
Laguna S 2.1—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
Laguna S 2.1—
Qwen3.8 Max84.5%
Source
Not directly comparable
Vision2Web
Laguna S 2.1—
Qwen3.8 Max69.0%
Source
Not directly comparable
CharXiv w/o tools
Laguna S 2.1—
Qwen3.8 Max88.4%
Source
Not directly comparable
CharXiv
Laguna S 2.1—
Qwen3.8 Max93.5%
Source
Not directly comparable
OmniDocBench 1.5
Laguna S 2.1—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
Laguna S 2.1—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
Laguna S 2.1—
Qwen3.8 Max79.6%
Source
Not directly comparable
RealWorldQA
Laguna S 2.1—
Qwen3.8 Max88.0%
Source
Not directly comparable
ERQA
Laguna S 2.1—
Qwen3.8 Max77.8%
Source
Not directly comparable
SimpleVQA
Laguna S 2.1—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
Laguna S 2.1—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
Laguna S 2.1—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
Laguna S 2.1—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
Laguna S 2.1—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
Laguna S 2.1—
Qwen3.8 Max90.8%
Source
Not directly comparable
LVBench
Laguna S 2.1—
Qwen3.8 Max81.8%
Source
Not directly comparable

Instruction following

IFBench
Laguna S 2.1—
Qwen3.8 Max82.8%
Source
Not directly comparable

Questions

Which is better, Laguna S 2.1 or Qwen3.8 Max?

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 S 2.1 or Qwen3.8 Max?

Qwen3.8 Max scores higher for coding on the public lane, 60.8 to 46. Laguna S 2.1 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, Laguna S 2.1 or Qwen3.8 Max?

Qwen3.8 Max scores higher for agentic tasks on the public lane, 67.3 to 46.4. Laguna S 2.1 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, Laguna S 2.1 or Qwen3.8 Max?

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 S 2.1 or Qwen3.8 Max?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 15, 2026

Watch Laguna S 2.1 vs Qwen3.8 Max

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

Laguna S 2.1 vs Qwen3.8 Max

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multimodal

Instruction following

Questions

Which is better, Laguna S 2.1 or Qwen3.8 Max?

Which is better for coding, Laguna S 2.1 or Qwen3.8 Max?

Which is better for agentic tasks, Laguna S 2.1 or Qwen3.8 Max?

Which costs less, Laguna S 2.1 or Qwen3.8 Max?

Which has the larger context window, Laguna S 2.1 or Qwen3.8 Max?

Related comparisons

Watch Laguna S 2.1 vs Qwen3.8 Max