Model A

Alibaba

64.8/100

Supported · Public rank #40

90% interval 55.5–74.2

Qwen3.6 Plus vs Qwen3.8-Flash-Next

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

Model B

Qwen3.8-Flash-Next

Alibaba

67.5/100

Estimated · Public rank #25

90% interval 57.7–77.4

Decision reading

Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 64.82, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

8 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.

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

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
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.

35 Qwen3.6 Plus only

8 Shared

16 Qwen3.8-Flash-Next only

Shared results: 8
Qwen3.6 Plus only: 35
Qwen3.8-Flash-Next only: 16
Like-for-like categories: 0 / 8

4 categories use 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

Directional only

Qwen3.6 Plus: 70.3
Qwen3.8-Flash-Next: 62.5
Weighted basis: 2 vs 1 rows
Reading: Directional only

Knowledge

Directional only

Qwen3.6 Plus: 57.1
Qwen3.8-Flash-Next: 43.4
Weighted basis: 4 vs 2 rows
Reading: Directional only

Multimodal

Directional only

Qwen3.6 Plus: 79.8
Qwen3.8-Flash-Next: 90.6
Weighted basis: 2 vs 1 rows
Reading: Directional only

Instruction following

Directional only

Qwen3.6 Plus: 82.3
Qwen3.8-Flash-Next: 81.3
Weighted basis: 2 vs 1 rows
Reading: Directional only

Agentic

Not comparable

Qwen3.6 Plus: 61.6
Qwen3.8-Flash-Next: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.6 Plus: 62.0
Qwen3.8-Flash-Next: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Math

Not comparable

Qwen3.6 Plus: 60.5
Qwen3.8-Flash-Next: Not measured
Weighted basis: 4 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Qwen3.6 Plus: 84.7
Qwen3.8-Flash-Next: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Qwen3.6 Plus and Qwen3.8-Flash-Next
Category	Qwen3.6 Plus	Qwen3.8-Flash-Next	Weighted basis	Reading
Coding	70.3	62.5	Directional only2 vs 1 rows	Directional only
Knowledge	57.1	43.4	Directional only4 vs 2 rows	Directional only
Multimodal	79.8	90.6	Directional only2 vs 1 rows	Directional only
Instruction following	82.3	81.3	Directional only2 vs 1 rows	Directional only
Agentic	61.6	Not measured	Not comparable1 vs 0 rows	Not comparable
Reasoning	62.0	Not measured	Not comparable1 vs 0 rows	Not comparable
Math	60.5	Not measured	Not comparable4 vs 0 rows	Not comparable
Multilingual	84.7	Not measured	Not comparable1 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.

CharXiv
Multimodal
Qwen3.6 Plus: 81.5%Qwen3.8-Flash-Next: 90.6%Normalized gap 9.1Qwen3.6 Plus source Qwen3.8-Flash-Next source
HLE
Knowledge
Qwen3.6 Plus: 28.8%Qwen3.8-Flash-Next: 35.9%Normalized gap 7.1Qwen3.6 Plus source Qwen3.8-Flash-Next source
SWE-bench Pro
Coding
Qwen3.6 Plus: 56.6%Qwen3.8-Flash-Next: 62.5%Normalized gap 5.9Qwen3.6 Plus source Qwen3.8-Flash-Next source
IFBench
Instruction following
Qwen3.6 Plus: 75.8%Qwen3.8-Flash-Next: 81.3%Normalized gap 5.5Qwen3.6 Plus source Qwen3.8-Flash-Next source
GPQA
Knowledge
Qwen3.6 Plus: 90.4%Qwen3.8-Flash-Next: 91.7%Normalized gap 1.3Qwen3.6 Plus source Qwen3.8-Flash-Next 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

Qwen3.6 Plus: API rate not published; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.6 Plus has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.6 Plus: API rate not published; Fits in one request
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request

Qwen3.6 Plus has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.6 Plus: API rate not published; Fits in one request; Cached-input rate unavailable
Qwen3.8-Flash-Next: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Qwen3.6 Plus has no comparable published API token rate. Qwen3.8-Flash-Next 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 PlusQwen3.8-Flash-Next

Context window

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

Qwen3.6 Plus

Qwen3.8-Flash-Next

262K

Qwen3.8-Flash-Next model card

API model ID

Qwen3.6 Plus

Not sourced

Qwen3.8-Flash-Next

Qwen/Qwen3.8-Flash-Next

Qwen3.8-Flash-Next model card

Cached-input rate

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

Qwen3.6 Plus

No comparable hosted API rate

Qwen3.8-Flash-Next

No comparable hosted API rate

Qwen3.8-Flash-Next model card

Documented inputs

Qwen3.6 Plus

Not sourced

Qwen3.8-Flash-Next

Not sourced

Documented outputs

Qwen3.6 Plus

Not sourced

Qwen3.8-Flash-Next

Not sourced

Provider availability

Qwen3.6 Plus

Not sourced

Qwen3.8-Flash-Next

Not sourced

Reasoning profile

Qwen3.6 Plus

Reasoning

Qwen3.8-Flash-Next

Reasoning

Weight access

Qwen3.6 Plus

Proprietary

Qwen3.8-Flash-Next

Open Weight

License

Qwen3.6 Plus

Proprietary

Qwen3.8-Flash-Next

Open Weight

Release date

Qwen3.6 Plus

2026-04-02

Qwen3.8-Flash-Next

2026-08-26

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-Flash-Next has the higher public score estimate, 67.54 versus 64.82, but the 90% score intervals overlap.
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.

Benchmark evidence

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

Browse raw public benchmark evidence59 rows

Agentic

Terminal-Bench 2.0
Qwen3.6 Plus61.6%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Claw-Eval
Qwen3.6 Plus58.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
QwenClawBench
Qwen3.6 Plus57.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable
τ³-bench results
Qwen3.6 Plus70.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
VITA-Bench
Qwen3.6 Plus44.3%
Source
Qwen3.8-Flash-Next—
Not directly comparable
DeepPlanning
Qwen3.6 Plus41.5%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Toolathlon
Qwen3.6 Plus39.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MCP Atlas
Qwen3.6 Plus48.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MCP-Tasks
Qwen3.6 Plus74.1%
Source
Qwen3.8-Flash-Next—
Not directly comparable
WideResearch
Qwen3.6 Plus74.3%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Gert Labs
Qwen3.6 Plus50.60%
Source
Qwen3.8-Flash-Next—
Not directly comparable
ResearchClawBench
Qwen3.6 Plus18.0%
Source
Qwen3.8-Flash-Next—
Not directly comparable
CoWorkBench
Qwen3.6 Plus—
Qwen3.8-Flash-Next73.9%
Source
Not directly comparable
JobBench
Qwen3.6 Plus—
Qwen3.8-Flash-Next55.7%
Source
Not directly comparable
Agents' Last Exam
Qwen3.6 Plus—
Qwen3.8-Flash-Next51.2%
Source
Not directly comparable
Toolathlon-Verified
Qwen3.6 Plus—
Qwen3.8-Flash-Next73.5%
Source
Not directly comparable
AndroidWorld
Qwen3.6 Plus—
Qwen3.8-Flash-Next84.5%
Source
Not directly comparable
OSWorld 2.0
Qwen3.6 Plus—
Qwen3.8-Flash-Next19.4%
Source
Not directly comparable

Coding

SWE-bench Verified
Qwen3.6 Plus78.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
SWE-bench Pro
Qwen3.6 Plus56.6%
Source
Qwen3.8-Flash-Next62.5%
Source
Qwen3.8-Flash-Next leads this result
SWE Multilingual
Qwen3.6 Plus73.8%
Source
Qwen3.8-Flash-Next81%
Source
Qwen3.8-Flash-Next leads this result
LiveCodeBench v6
Qwen3.6 Plus87.1%
Source
Qwen3.8-Flash-Next91.9%
Source
Qwen3.8-Flash-Next leads this result
Vibe Code Bench
Qwen3.6 Plus25.56%
Source
Qwen3.8-Flash-Next—
Not directly comparable
NL2Repo
Qwen3.6 Plus—
Qwen3.8-Flash-Next48.1%
Source
Not directly comparable
deepSwe
Qwen3.6 Plus—
Qwen3.8-Flash-Next58.7%
Source
Not directly comparable

Reasoning

AI-Needle
Qwen3.6 Plus68.3%
Source
Qwen3.8-Flash-Next—
Not directly comparable
LongBench v2
Qwen3.6 Plus62%
Source
Qwen3.8-Flash-Next—
Not directly comparable

Knowledge

GPQA
Qwen3.6 Plus90.4%
Source
Qwen3.8-Flash-Next91.7%
Source
Qwen3.8-Flash-Next leads this result
SuperGPQA
Qwen3.6 Plus71.6%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMLU-Pro
Qwen3.6 Plus88.5%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMLU-Redux
Qwen3.6 Plus94.5%
Source
Qwen3.8-Flash-Next—
Not directly comparable
C-Eval
Qwen3.6 Plus93.3%
Source
Qwen3.8-Flash-Next—
Not directly comparable
HLE
Qwen3.6 Plus28.8%
Source
Qwen3.8-Flash-Next35.9%
Source
Qwen3.8-Flash-Next leads this result
GPQA-D
Qwen3.6 Plus—
Qwen3.8-Flash-Next91.7%
Source
Not directly comparable
HLE w/o tools
Qwen3.6 Plus—
Qwen3.8-Flash-Next35.9%
Source
Not directly comparable

Math

AIME26
Qwen3.6 Plus95.3%
Source
Qwen3.8-Flash-Next—
Not directly comparable
HMMT Feb 2025
Qwen3.6 Plus96.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
HMMT Nov 2025
Qwen3.6 Plus94.6%
Source
Qwen3.8-Flash-Next—
Not directly comparable
HMMT Feb 2026
Qwen3.6 Plus87.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMAnswerBench
Qwen3.6 Plus83.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Qwen3.6 Plus26.207%
Source
Qwen3.8-Flash-Next—
Not directly comparable
FrontierMath v2 (Tier 4)
Qwen3.6 Plus8.333%
Source
Qwen3.8-Flash-Next—
Not directly comparable

Multilingual

MMLU-ProX
Qwen3.6 Plus84.7%
Source
Qwen3.8-Flash-Next—
Not directly comparable
NOVA-63
Qwen3.6 Plus57.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable

Multimodal

MMMU
Qwen3.6 Plus86.0%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MMMU-Pro
Qwen3.6 Plus78.8%
Source
Qwen3.8-Flash-Next—
Not directly comparable
MathVision
Qwen3.6 Plus88.0%
Source
Qwen3.8-Flash-Next90.6%
Source
Qwen3.8-Flash-Next leads this result
VideoMMMU
Qwen3.6 Plus84.0%
Source
Qwen3.8-Flash-Next—
Not directly comparable
ScreenSpot Pro
Qwen3.6 Plus68.2%
Source
Qwen3.8-Flash-Next—
Not directly comparable
CharXiv
Qwen3.6 Plus81.5%
Source
Qwen3.8-Flash-Next90.6%
Source
Qwen3.8-Flash-Next leads this result
V*
Qwen3.6 Plus96.9%
Source
Qwen3.8-Flash-Next—
Not directly comparable
Vision2Web
Qwen3.6 Plus—
Qwen3.8-Flash-Next64.0%
Source
Not directly comparable
ERQA
Qwen3.6 Plus—
Qwen3.8-Flash-Next72.3%
Source
Not directly comparable
LVBench
Qwen3.6 Plus—
Qwen3.8-Flash-Next76.6%
Source
Not directly comparable
RealWorldQA
Qwen3.6 Plus—
Qwen3.8-Flash-Next88.5%
Source
Not directly comparable
MathVision w/ Python
Qwen3.6 Plus—
Qwen3.8-Flash-Next95.7%
Source
Not directly comparable
CharXiv w/o tools
Qwen3.6 Plus—
Qwen3.8-Flash-Next84.6%
Source
Not directly comparable

Instruction following

IFEval
Qwen3.6 Plus94.3%
Source
Qwen3.8-Flash-Next—
Not directly comparable
IFBench
Qwen3.6 Plus75.8%
Source
Qwen3.8-Flash-Next81.3%
Source
Qwen3.8-Flash-Next leads this result

Frequently asked questions

Which is better, Qwen3.6 Plus or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 64.82, 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 Plus or Qwen3.8-Flash-Next?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Qwen3.6 Plus or Qwen3.8-Flash-Next?

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, Qwen3.6 Plus or Qwen3.8-Flash-Next?

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 Plus or Qwen3.8-Flash-Next?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 262K.

Related comparisons

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Last updated August 26, 2026

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Qwen3.6 Plus vs Qwen3.8-Flash-Next

Which one for your work

Long documents

Coding work

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

Multimodal

Instruction following

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

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, Qwen3.6 Plus or Qwen3.8-Flash-Next?

Which is better for coding, Qwen3.6 Plus or Qwen3.8-Flash-Next?

Which is better for agentic tasks, Qwen3.6 Plus or Qwen3.8-Flash-Next?

Which costs less, Qwen3.6 Plus or Qwen3.8-Flash-Next?

Which has the larger context window, Qwen3.6 Plus or Qwen3.8-Flash-Next?

Related comparisons

Watch Qwen3.6 Plus vs Qwen3.8-Flash-Next