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Data

dots3-note Preview vs Qwen3.8-Flash-Next

Updated October 10, 2026. Rank cannot separate these two. Price, access, and your workload decide. Public scores include evidence status and uncertainty.

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Decision reading

Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 64.05. Their conditional score ranges overlap. These ranges do not establish rank confidence. 9 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
dots3-note Preview

Dots Studio

64.05/100

Estimated · Public rank #39

Conditional range 49.7–78.4

Model B
Alibaba logo
Qwen3.8-Flash-Next

Alibaba

64.23/100

Estimated · Public rank #37

Conditional range 49.9–78.6

Shared results
9
dots3-note Preview only
22
Qwen3.8-Flash-Next only
15
Like-for-like categories
1 / 8
Estimated: dots3-note Preview and Qwen3.8-Flash-Next. Conditional ranges do not establish rank confidence.How 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

    dots3-note Preview

    dots3-note Preview 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

    Dots3-note Preview is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Dots3-note Preview and Qwen3.8-Flash-Next are 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: 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.

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

—dots3-note Preview52.4Qwen3.8-Flash-Next

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

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.

Instruction following

Like-for-like
dots3-note Preview
89.1
#31/127
Qwen3.8-Flash-Next
91.2
#22/127
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Qwen3.8-Flash-Next leads

Agentic

Directional only
dots3-note Preview
58.1
Estimated · #28/123
Qwen3.8-Flash-Next
59.5
Estimated · #24/123
Basis
BenchAlign v5.8 lane · 9 vs 6 public rows
Reading
Directional only

Coding

Not comparable
dots3-note Preview
Not ranked
Qwen3.8-Flash-Next
52.4
Supported · #37/146
Basis
BenchAlign v5.8 lane · 7 vs 5 public rows
Reading
Not comparable

Reasoning

Not comparable
dots3-note Preview
76.6
Unranked · 1 rankable row
Qwen3.8-Flash-Next
80.9
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
dots3-note Preview
79.6
Unranked · 10 rankable rows
Qwen3.8-Flash-Next
87.7
#8/54
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Knowledge

Not comparable
dots3-note Preview
Not ranked
Qwen3.8-Flash-Next
59.4
Supported · #47/177
Basis
BenchAlign v5.8 lane · 1 vs 4 public rows
Reading
Not comparable

Multilingual

Not comparable
dots3-note Preview
Not ranked
Qwen3.8-Flash-Next
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
dots3-note Preview
Not ranked
Qwen3.8-Flash-Next
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
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

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request

dots3-note Preview 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

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.8-Flash-Next
Self-hosted; infrastructure cost varies
Fits in one request

dots3-note Preview 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

dots3-note Preview
Self-hosted; infrastructure cost varies
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

dots3-note Preview has no comparable published API token rate. Qwen3.8-Flash-Next 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.

Documented inputs

dots3-note Preview

Not sourced

Qwen3.8-Flash-Next

Not sourced

Documented outputs

dots3-note Preview

Not sourced

Qwen3.8-Flash-Next

Not sourced

Provider availability

dots3-note Preview

Not sourced

Qwen3.8-Flash-Next

Not sourced

Reasoning profile

dots3-note Preview

Reasoning

Qwen3.8-Flash-Next

Reasoning

Weight access

dots3-note Preview

Open Weight

Qwen3.8-Flash-Next

Open Weight

License

dots3-note Preview

Open Weight

Qwen3.8-Flash-Next

Open Weight

Release date

dots3-note Preview

2026-08-14

Qwen3.8-Flash-Next

2026-08-26

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
Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 64.05. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
dots3-note Preview has the larger documented window (512K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, dots3-note Preview or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 64.05. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, dots3-note Preview or Qwen3.8-Flash-Next?

Dots3-note Preview is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, dots3-note Preview or Qwen3.8-Flash-Next?

Qwen3.8-Flash-Next scores higher for agentic tasks on the public lane, 59.5 to 58.1. Dots3-note Preview and Qwen3.8-Flash-Next are 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, dots3-note Preview 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, dots3-note Preview or Qwen3.8-Flash-Next?

dots3-note Preview has the larger documented context window: 512K, compared with 262K.

Benchmark evidence

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

Browse raw public benchmark evidence46 rows

Agentic

  • Claw-Eval

    dots3-note Preview73.4%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • Terminal-Bench 2.1

    dots3-note Preview75.1%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • Toolathlon-Verified

    dots3-note Preview55.6%
    Source
    Qwen3.8-Flash-Next73.5%
    Source

    Qwen3.8-Flash-Next leads this result

  • skillsBench

    dots3-note Preview52.8%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • APEX-Agents

    dots3-note Preview30.8%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • BrowseComp

    dots3-note Preview83.3%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • HLE w/ tools

    dots3-note Preview52.6%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • DeepSearchQA

    dots3-note Preview92.1%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • WideResearch

    dots3-note Preview78.9%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • CoWorkBench

    dots3-note Preview—
    Qwen3.8-Flash-Next73.9%
    Source

    Not directly comparable

  • JobBench

    dots3-note Preview—
    Qwen3.8-Flash-Next55.7%
    Source

    Not directly comparable

  • Agents' Last Exam

    dots3-note Preview—
    Qwen3.8-Flash-Next51.2%
    Source

    Not directly comparable

  • AndroidWorld

    dots3-note Preview—
    Qwen3.8-Flash-Next84.5%
    Source

    Not directly comparable

  • OSWorld 2.0

    dots3-note Preview—
    Qwen3.8-Flash-Next19.4%
    Source

    Not directly comparable

Coding

  • Codeforces

    dots3-note Preview3056.0
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • LiveCodeBench v6

    dots3-note Preview91.5%
    Source
    Qwen3.8-Flash-Next91.9%
    Source

    Qwen3.8-Flash-Next leads this result

  • Terminal-Bench 2.1

    dots3-note Preview75.1%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • SWE-bench Verified

    dots3-note Preview78.4%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • SWE Multilingual

    dots3-note Preview75.7%
    Source
    Qwen3.8-Flash-Next81%
    Source

    Qwen3.8-Flash-Next leads this result

  • SWE-bench Pro

    dots3-note Preview61%
    Source
    Qwen3.8-Flash-Next62.5%
    Source

    Qwen3.8-Flash-Next leads this result

  • NL2Repo

    dots3-note Preview49.8%
    Source
    Qwen3.8-Flash-Next48.1%
    Source

    dots3-note Preview leads this result

  • DeepSWE

    dots3-note Preview—
    Qwen3.8-Flash-Next58.7%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    dots3-note Preview81.4%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

Multimodal

  • SimpleVQA

    dots3-note Preview72.5%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • MMMU-Pro

    dots3-note Preview79.1%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • MathVision

    dots3-note Preview87.7%
    Source
    Qwen3.8-Flash-Next90.6%
    Source

    Qwen3.8-Flash-Next leads this result

  • ZeroBench

    dots3-note Preview19.0%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • CharXiv w/o tools

    dots3-note Preview83.1%
    Source
    Qwen3.8-Flash-Next84.6%
    Source

    Qwen3.8-Flash-Next leads this result

  • GDP.pdf (no tools)

    dots3-note Preview60.7%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • PerceptionBench

    dots3-note Preview53.4%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • BabyVision

    dots3-note Preview50.0%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • MMVU

    dots3-note Preview79.9%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • VideoMMMU

    dots3-note Preview86.8%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

  • Vision2Web

    dots3-note Preview—
    Qwen3.8-Flash-Next64.0%
    Source

    Not directly comparable

  • ERQA

    dots3-note Preview—
    Qwen3.8-Flash-Next72.3%
    Source

    Not directly comparable

  • LVBench

    dots3-note Preview—
    Qwen3.8-Flash-Next76.6%
    Source

    Not directly comparable

  • RealWorldQA

    dots3-note Preview—
    Qwen3.8-Flash-Next88.5%
    Source

    Not directly comparable

  • MathVision w/ Python

    dots3-note Preview—
    Qwen3.8-Flash-Next95.7%
    Source

    Not directly comparable

  • CharXiv

    dots3-note Preview—
    Qwen3.8-Flash-Next90.6%
    Source

    Not directly comparable

Knowledge

  • HLE

    dots3-note Preview52.6%
    Source
    Qwen3.8-Flash-Next35.9%
    Source

    dots3-note Preview leads this result

  • GPQA

    dots3-note Preview—
    Qwen3.8-Flash-Next91.7%
    Source

    Not directly comparable

  • GPQA-D

    dots3-note Preview—
    Qwen3.8-Flash-Next91.7%
    Source

    Not directly comparable

  • HLE w/o tools

    dots3-note Preview—
    Qwen3.8-Flash-Next35.9%
    Source

    Not directly comparable

Instruction following

  • IFBench

    dots3-note Preview80.4%
    Source
    Qwen3.8-Flash-Next81.3%
    Source

    Qwen3.8-Flash-Next leads this result

  • IFEval

    dots3-note Preview93.9%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

Math

  • IMOAnswerBench

    dots3-note Preview90.9%
    Source
    Qwen3.8-Flash-Next—

    Not directly comparable

46 public results · 9 shared

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Last updated October 10, 2026