Model A

Alibaba

67.5/100

Estimated · Public rank #25

90% interval 57.7–77.4

Qwen3.8-Flash-Next vs Qwen3.8 Max

Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.8 Max

Alibaba

79.2/100

Supported · Public rank #6

90% interval 76.9–81.4

Decision reading

Qwen3.8 Max has the higher public score estimate, 79.18 versus 67.54, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

22 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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
Qwen3.8 Max
Qwen3.8 Max leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.8 Max
Qwen3.8 Max 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
No shared weighted benchmark basis supports a winner.
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

What is actually comparable

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

2 Qwen3.8-Flash-Next only

22 Shared

32 Qwen3.8 Max only

Shared results: 22
Qwen3.8-Flash-Next only: 2
Qwen3.8 Max only: 32
Like-for-like categories: 3 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Like-for-like

Qwen3.8-Flash-Next: 62.5
Qwen3.8 Max: 67.7
Weighted basis: 1 vs 1 rows
Reading: Qwen3.8 Max leads

Knowledge

Like-for-like

Qwen3.8-Flash-Next: 43.4
Qwen3.8 Max: 50.2
Weighted basis: 2 vs 2 rows
Reading: Qwen3.8 Max leads

Instruction following

Like-for-like

Qwen3.8-Flash-Next: 81.3
Qwen3.8 Max: 82.8
Weighted basis: 1 vs 1 rows
Reading: Qwen3.8 Max leads

Multimodal

Directional only

Qwen3.8-Flash-Next: 90.6
Qwen3.8 Max: 86.3
Weighted basis: 1 vs 2 rows
Reading: Directional only

Agentic

Not comparable

Qwen3.8-Flash-Next: Not measured
Qwen3.8 Max: 86.1
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.8-Flash-Next: Not measured
Qwen3.8 Max: 78.3
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Math

Not comparable

Qwen3.8-Flash-Next: Not measured
Qwen3.8 Max: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.8-Flash-Next: Not measured
Qwen3.8 Max: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Qwen3.8-Flash-Next and Qwen3.8 Max
Category	Qwen3.8-Flash-Next	Qwen3.8 Max	Weighted basis	Reading
Coding	62.5	67.7	Like-for-like1 vs 1 rows	Qwen3.8 Max leads
Knowledge	43.4	50.2	Like-for-like2 vs 2 rows	Qwen3.8 Max leads
Instruction following	81.3	82.8	Like-for-like1 vs 1 rows	Qwen3.8 Max leads
Multimodal	90.6	86.3	Directional only1 vs 2 rows	Directional only
Agentic	Not measured	86.1	Not comparable0 vs 1 rows	Not comparable
Reasoning	Not measured	78.3	Not comparable0 vs 2 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

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.

HLE
Knowledge
Qwen3.8-Flash-Next: 35.9%Qwen3.8 Max: 43.6%Normalized gap 7.7Qwen3.8-Flash-Next source Qwen3.8 Max source
SWE-bench Pro
Coding
Qwen3.8-Flash-Next: 62.5%Qwen3.8 Max: 67.7%Normalized gap 5.2Qwen3.8-Flash-Next source Qwen3.8 Max source
CharXiv
Multimodal
Qwen3.8-Flash-Next: 90.6%Qwen3.8 Max: 93.5%Normalized gap 2.9Qwen3.8-Flash-Next source Qwen3.8 Max source
IFBench
Instruction following
Qwen3.8-Flash-Next: 81.3%Qwen3.8 Max: 82.8%Normalized gap 1.5Qwen3.8-Flash-Next source Qwen3.8 Max source
GPQA
Knowledge
Qwen3.8-Flash-Next: 91.7%Qwen3.8 Max: 92.6%Normalized gap 0.9Qwen3.8-Flash-Next 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

Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8-Flash-Next has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.8 Max: API rate not published; Fits in one request

Qwen3.8-Flash-Next has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Qwen3.8 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.8-Flash-Next has no comparable published API token rate. 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.

SpecificationQwen3.8-Flash-NextQwen3.8 Max

Context window

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

Qwen3.8-Flash-Next

262K

Qwen3.8-Flash-Next model card

Qwen3.8 Max

Alibaba Cloud Model Studio pricing

API model ID

Qwen3.8-Flash-Next

Qwen/Qwen3.8-Flash-Next

Qwen3.8-Flash-Next model card

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.

Qwen3.8-Flash-Next

No comparable hosted API rate

Qwen3.8-Flash-Next model card

Qwen3.8 Max

No comparable hosted API rate

Alibaba Cloud Model Studio pricing

Documented inputs

Qwen3.8-Flash-Next

Not sourced

Qwen3.8 Max

Not sourced

Documented outputs

Qwen3.8-Flash-Next

Not sourced

Qwen3.8 Max

Not sourced

Provider availability

Qwen3.8-Flash-Next

Not sourced

Qwen3.8 Max

Not sourced

Reasoning profile

Qwen3.8-Flash-Next

Reasoning

Qwen3.8 Max

Reasoning

Weight access

Qwen3.8-Flash-Next

Open Weight

Qwen3.8 Max

Open Weight

License

Qwen3.8-Flash-Next

Open Weight

Qwen3.8 Max

Open Weight

Release date

Qwen3.8-Flash-Next

2026-08-26

Qwen3.8 Max

2026-08-03

If you already use one of these models

Deployment change: Both entries list Alibaba as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: Qwen3.8 Max has the higher public score estimate, 79.18 versus 67.54, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.8 Max has the larger documented window (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 evidence56 rows

Agentic

CoWorkBench
Qwen3.8-Flash-Next73.9%
Source
Qwen3.8 Max74.8%
Source
Qwen3.8 Max leads this result
JobBench
Qwen3.8-Flash-Next55.7%
Source
Qwen3.8 Max53.4%
Source
Qwen3.8-Flash-Next leads this result
Agents' Last Exam
Qwen3.8-Flash-Next51.2%
Source
Qwen3.8 Max52.4%
Source
Qwen3.8 Max leads this result
Toolathlon-Verified
Qwen3.8-Flash-Next73.5%
Source
Qwen3.8 Max72.5%
Source
Qwen3.8-Flash-Next leads this result
AndroidWorld
Qwen3.8-Flash-Next84.5%
Source
Qwen3.8 Max85.3%
Source
Qwen3.8 Max leads this result
OSWorld 2.0
Qwen3.8-Flash-Next19.4%
Source
Qwen3.8 Max19.4%
Source
Tie
Terminal-Bench 2.1
Qwen3.8-Flash-Next—
Qwen3.8 Max86.6%
Source
Not directly comparable
skillsBench
Qwen3.8-Flash-Next—
Qwen3.8 Max70.2%
Source
Not directly comparable
AutomationBench
Qwen3.8-Flash-Next—
Qwen3.8 Max27.3%
Source
Not directly comparable
WideResearch
Qwen3.8-Flash-Next—
Qwen3.8 Max81.9%
Source
Not directly comparable
HLE w/ tools
Qwen3.8-Flash-Next—
Qwen3.8 Max56.2%
Source
Not directly comparable
OSWorld-Verified
Qwen3.8-Flash-Next—
Qwen3.8 Max86.1%
Source
Not directly comparable
WebArena-Verified
Qwen3.8-Flash-Next—
Qwen3.8 Max66.8%
Source
Not directly comparable
MobileWorld
Qwen3.8-Flash-Next—
Qwen3.8 Max77.8%
Source
Not directly comparable

Coding

SWE-bench Pro
Qwen3.8-Flash-Next62.5%
Source
Qwen3.8 Max67.7%
Source
Qwen3.8 Max leads this result
SWE Multilingual
Qwen3.8-Flash-Next81%
Source
Qwen3.8 Max—
Not directly comparable
NL2Repo
Qwen3.8-Flash-Next48.1%
Source
Qwen3.8 Max55.9%
Source
Qwen3.8 Max leads this result
deepSwe
Qwen3.8-Flash-Next58.7%
Source
Qwen3.8 Max56.6%
Source
Qwen3.8-Flash-Next leads this result
LiveCodeBench v6
Qwen3.8-Flash-Next91.9%
Source
Qwen3.8 Max—
Not directly comparable
Terminal-Bench 2.1
Qwen3.8-Flash-Next—
Qwen3.8 Max86.6%
Source
Not directly comparable
FrontierSWE
Qwen3.8-Flash-Next—
Qwen3.8 Max73.5%
Source
Not directly comparable
MLS-Bench Lite
Qwen3.8-Flash-Next—
Qwen3.8 Max41.0%
Source
Not directly comparable
PaperBench
Qwen3.8-Flash-Next—
Qwen3.8 Max93.0%
Source
Not directly comparable
VulcanBench v3
Qwen3.8-Flash-Next—
Qwen3.8 Max81.2%
Source
Not directly comparable
OpenHarmony Bench
Qwen3.8-Flash-Next—
Qwen3.8 Max60.8%
Source
Not directly comparable

Reasoning

MRCRv2
Qwen3.8-Flash-Next—
Qwen3.8 Max92.9%
Source
Not directly comparable
LongBench v2
Qwen3.8-Flash-Next—
Qwen3.8 Max66.3%
Source
Not directly comparable

Knowledge

GPQA
Qwen3.8-Flash-Next91.7%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
GPQA-D
Qwen3.8-Flash-Next91.7%
Source
Qwen3.8 Max92.6%
Source
Qwen3.8 Max leads this result
HLE
Qwen3.8-Flash-Next35.9%
Source
Qwen3.8 Max43.6%
Source
Qwen3.8 Max leads this result
HLE w/o tools
Qwen3.8-Flash-Next35.9%
Source
Qwen3.8 Max43.6%
Source
Qwen3.8 Max leads this result

Multimodal

Vision2Web
Qwen3.8-Flash-Next64.0%
Source
Qwen3.8 Max69.0%
Source
Qwen3.8 Max leads this result
ERQA
Qwen3.8-Flash-Next72.3%
Source
Qwen3.8 Max77.8%
Source
Qwen3.8 Max leads this result
LVBench
Qwen3.8-Flash-Next76.6%
Source
Qwen3.8 Max81.8%
Source
Qwen3.8 Max leads this result
RealWorldQA
Qwen3.8-Flash-Next88.5%
Source
Qwen3.8 Max88.0%
Source
Qwen3.8-Flash-Next leads this result
MathVision
Qwen3.8-Flash-Next90.6%
Source
Qwen3.8 Max95.2%
Source
Qwen3.8 Max leads this result
MathVision w/ Python
Qwen3.8-Flash-Next95.7%
Source
Qwen3.8 Max97.7%
Source
Qwen3.8 Max leads this result
CharXiv w/o tools
Qwen3.8-Flash-Next84.6%
Source
Qwen3.8 Max88.4%
Source
Qwen3.8 Max leads this result
CharXiv
Qwen3.8-Flash-Next90.6%
Source
Qwen3.8 Max93.5%
Source
Qwen3.8 Max leads this result
MMMU-Pro
Qwen3.8-Flash-Next—
Qwen3.8 Max82.3%
Source
Not directly comparable
BabyVision
Qwen3.8-Flash-Next—
Qwen3.8 Max82.0%
Source
Not directly comparable
BabyVision w/ Python
Qwen3.8-Flash-Next—
Qwen3.8 Max91.3%
Source
Not directly comparable
ZeroBench
Qwen3.8-Flash-Next—
Qwen3.8 Max24.0%
Source
Not directly comparable
ZeroBench w/ Python
Qwen3.8-Flash-Next—
Qwen3.8 Max49.0%
Source
Not directly comparable
MedXpertQA (MM)
Qwen3.8-Flash-Next—
Qwen3.8 Max80.4%
Source
Not directly comparable
ScreenSpot Pro
Qwen3.8-Flash-Next—
Qwen3.8 Max84.5%
Source
Not directly comparable
OmniDocBench 1.5
Qwen3.8-Flash-Next—
Qwen3.8 Max92.1%
Source
Not directly comparable
OCRBench V2
Qwen3.8-Flash-Next—
Qwen3.8 Max74.2%
Source
Not directly comparable
CC-OCR
Qwen3.8-Flash-Next—
Qwen3.8 Max79.6%
Source
Not directly comparable
SimpleVQA
Qwen3.8-Flash-Next—
Qwen3.8 Max75.0%
Source
Not directly comparable
PerceptionBench
Qwen3.8-Flash-Next—
Qwen3.8 Max63.5%
Source
Not directly comparable
Video-MME (with subtitle)
Qwen3.8-Flash-Next—
Qwen3.8 Max90.4%
Source
Not directly comparable
VideoMMMU
Qwen3.8-Flash-Next—
Qwen3.8 Max88.7%
Source
Not directly comparable
MMVU
Qwen3.8-Flash-Next—
Qwen3.8 Max82.4%
Source
Not directly comparable
MLVU (M-Avg)
Qwen3.8-Flash-Next—
Qwen3.8 Max90.8%
Source
Not directly comparable

Instruction following

IFBench
Qwen3.8-Flash-Next81.3%
Source
Qwen3.8 Max82.8%
Source
Qwen3.8 Max leads this result

Frequently asked questions

Which is better, Qwen3.8-Flash-Next or Qwen3.8 Max?

Qwen3.8 Max has the higher public score estimate, 79.18 versus 67.54, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Qwen3.8-Flash-Next or Qwen3.8 Max?

Qwen3.8 Max leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, Qwen3.8-Flash-Next or Qwen3.8 Max?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Qwen3.8-Flash-Next 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, Qwen3.8-Flash-Next or Qwen3.8 Max?

Qwen3.8 Max has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 26, 2026

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Qwen3.8-Flash-Next vs Qwen3.8 Max

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Instruction following

Multimodal

Agentic

Reasoning

Math

Multilingual

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

Frequently asked questions

Which is better, Qwen3.8-Flash-Next or Qwen3.8 Max?

Which is better for coding, Qwen3.8-Flash-Next or Qwen3.8 Max?

Which is better for agentic tasks, Qwen3.8-Flash-Next or Qwen3.8 Max?

Which costs less, Qwen3.8-Flash-Next or Qwen3.8 Max?

Which has the larger context window, Qwen3.8-Flash-Next or Qwen3.8 Max?

Related comparisons

Watch Qwen3.8-Flash-Next vs Qwen3.8 Max