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BenchLM
Alibaba logo
Model A
Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.0–58.5

Qwen3.6-27B vs Qwen3.6 Plus

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Alibaba logo
Model B
Qwen3.6 Plus

Alibaba

61.49/100

Supported · Public rank #64

90% interval 53.0–69.9

Decision reading

Qwen3.6 Plus has the higher public score estimate, 61.49 versus 52.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

23 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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 leads on the public coding lane, 47 to 42.6, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited

  • Agentic work

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

    Qwen3.6 Plus

    Qwen3.6 Plus leads on the public agentic lane, 36.3 to 33.9, with Supported evidence for both models, although the 90% intervals overlap.

    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

  • 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.

Shared results
23
Qwen3.6-27B only
15
Qwen3.6 Plus only
25
Like-for-like categories
3 / 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

Like-for-like
Qwen3.6-27B
33.9
Supported · #133/151
Qwen3.6 Plus
36.3
Supported · #128/151
Basis
BenchAlign lane · 6 vs 13 public rows
Reading
Qwen3.6 Plus leads · intervals overlap

Coding

Like-for-like
Qwen3.6-27B
42.6
Supported · #128/183
Qwen3.6 Plus
47.0
Supported · #92/183
Basis
BenchAlign lane · 6 vs 7 public rows
Reading
Qwen3.6 Plus leads · intervals overlap

Multimodal

Like-for-like
Qwen3.6-27B
51.5
#35/48
Qwen3.6 Plus
65.6
#22/48
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Qwen3.6 Plus leads

Knowledge

Directional only
Qwen3.6-27B
49.0
Estimated · #95/181
Qwen3.6 Plus
54.7
Supported · #63/181
Basis
BenchAlign lane · 6 vs 8 public rows
Reading
Directional only

Instruction following

Directional only
Qwen3.6-27B
82.2
#50/120
Qwen3.6 Plus
85.5
#41/120
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Directional only

Reasoning

Not comparable
Qwen3.6-27B
73.7
Unranked · 2 rankable rows
Qwen3.6 Plus
59.3
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Qwen3.6-27B
72.8
Unranked · 5 rankable rows
Qwen3.6 Plus
62.4
#4/7
Basis
Provisional lane · 2 vs 4 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.6-27B
Not ranked
Qwen3.6 Plus
69.7
#4/12
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
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.

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

Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.6 Plus
API rate not published
Fits in one request

Qwen3.6-27B 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

Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.6 Plus
API rate not published
Fits in one request

Qwen3.6-27B 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

Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Qwen3.6 Plus
API rate not published
Fits in one request
Cached-input rate unavailable

Qwen3.6-27B has no comparable published API token rate. Qwen3.6 Plus 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.

Context window

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

Qwen3.6-27B

262K

Qwen3.6 Plus

1M

API model ID

Qwen3.6-27B

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.

Qwen3.6-27B

No comparable hosted API rate

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Qwen3.6-27B

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Qwen3.6-27B

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Qwen3.6-27B

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Qwen3.6-27B

Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Qwen3.6-27B

Open Weight

Qwen3.6 Plus

Proprietary

License

Qwen3.6-27B

Open Weight

Qwen3.6 Plus

Proprietary

Release date

Qwen3.6-27B

2026-04-21

Qwen3.6 Plus

2026-04-02

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
Qwen3.6 Plus has the higher public score estimate, 61.49 versus 52.73, 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.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even—
Qwen3.6 Plus
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

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

    Qwen3.6-27B59.3%
    Source
    Qwen3.6 Plus61.6%
    Source

    Qwen3.6 Plus leads this result

  • Claw-Eval

    Qwen3.6-27B72.4%
    Source
    Qwen3.6 Plus58.8%
    Source

    Qwen3.6-27B leads this result

  • QwenClawBench

    Qwen3.6-27B53.4%
    Source
    Qwen3.6 Plus57.2%
    Source

    Qwen3.6 Plus leads this result

  • QwenWebBench

    Qwen3.6-27B1487
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • AndroidWorld

    Qwen3.6-27B70.3%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Gert Labs

    Shared source
    Qwen3.6-27B54.84%
    Qwen3.6 Plus50.60%

    Qwen3.6-27B leads this result

  • τ³-bench results

    Qwen3.6-27B—
    Qwen3.6 Plus70.7%
    Source

    Not directly comparable

  • VITA-Bench

    Qwen3.6-27B—
    Qwen3.6 Plus44.3%
    Source

    Not directly comparable

  • DeepPlanning

    Qwen3.6-27B—
    Qwen3.6 Plus41.5%
    Source

    Not directly comparable

  • Toolathlon

    Qwen3.6-27B—
    Qwen3.6 Plus39.8%
    Source

    Not directly comparable

  • MCP Atlas

    Qwen3.6-27B—
    Qwen3.6 Plus48.2%
    Source

    Not directly comparable

  • MCP-Tasks

    Qwen3.6-27B—
    Qwen3.6 Plus74.1%
    Source

    Not directly comparable

  • WideResearch

    Qwen3.6-27B—
    Qwen3.6 Plus74.3%
    Source

    Not directly comparable

  • ResearchClawBench

    Qwen3.6-27B—
    Qwen3.6 Plus18.0%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Qwen3.6-27B—
    Qwen3.6 Plus53.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.6-27B77.2%
    Source
    Qwen3.6 Plus78.8%
    Source

    Qwen3.6 Plus leads this result

  • SWE Multilingual

    Qwen3.6-27B71.3%
    Source
    Qwen3.6 Plus73.8%
    Source

    Qwen3.6 Plus leads this result

  • SWE-bench Pro

    Qwen3.6-27B53.5%
    Source
    Qwen3.6 Plus56.6%
    Source

    Qwen3.6 Plus leads this result

  • Terminal-Bench 2.0

    Qwen3.6-27B59.3%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • LiveCodeBench

    Qwen3.6-27B83.9%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • NL2Repo

    Qwen3.6-27B36.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • LiveCodeBench v6

    Qwen3.6-27B—
    Qwen3.6 Plus87.1%
    Source

    Not directly comparable

  • Vibe Code Bench

    Qwen3.6-27B—
    Qwen3.6 Plus25.56%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Qwen3.6-27B—
    Qwen3.6 Plus86.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Qwen3.6-27B—
    Qwen3.6 Plus73.4%
    Source

    Not directly comparable

Reasoning

  • AI-Needle

    Qwen3.6-27B—
    Qwen3.6 Plus68.3%
    Source

    Not directly comparable

  • LongBench v2

    Qwen3.6-27B—
    Qwen3.6 Plus62%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    Qwen3.6-27B86.2%
    Source
    Qwen3.6 Plus88.5%
    Source

    Qwen3.6 Plus leads this result

  • MMLU-Redux

    Qwen3.6-27B93.5%
    Source
    Qwen3.6 Plus94.5%
    Source

    Qwen3.6 Plus leads this result

  • SuperGPQA

    Qwen3.6-27B66%
    Source
    Qwen3.6 Plus71.6%
    Source

    Qwen3.6 Plus leads this result

  • C-Eval

    Qwen3.6-27B91.4%
    Source
    Qwen3.6 Plus93.3%
    Source

    Qwen3.6 Plus leads this result

  • GPQA

    Qwen3.6-27B87.8%
    Source
    Qwen3.6 Plus90.4%
    Source

    Qwen3.6 Plus leads this result

  • HLE

    Qwen3.6-27B24%
    Source
    Qwen3.6 Plus28.8%
    Source

    Qwen3.6 Plus leads this result

  • GPQA Diamond (Vals)

    Qwen3.6-27B—
    Qwen3.6 Plus87.4%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Qwen3.6-27B—
    Qwen3.6 Plus87.7%
    Source

    Not directly comparable

Math

  • HMMT Feb 2025

    Qwen3.6-27B93.8%
    Source
    Qwen3.6 Plus96.7%
    Source

    Qwen3.6 Plus leads this result

  • HMMT Nov 2025

    Qwen3.6-27B90.7%
    Source
    Qwen3.6 Plus94.6%
    Source

    Qwen3.6 Plus leads this result

  • HMMT Feb 2026

    Qwen3.6-27B84.3%
    Source
    Qwen3.6 Plus87.8%
    Source

    Qwen3.6 Plus leads this result

  • MMAnswerBench

    Qwen3.6-27B80.8%
    Source
    Qwen3.6 Plus83.8%
    Source

    Qwen3.6 Plus leads this result

  • AIME26

    Qwen3.6-27B94.1%
    Source
    Qwen3.6 Plus95.3%
    Source

    Qwen3.6 Plus leads this result

  • FrontierMath v2 (Tiers 1-3)

    Qwen3.6-27B—
    Qwen3.6 Plus26.207%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Qwen3.6-27B—
    Qwen3.6 Plus8.333%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    Qwen3.6-27B—
    Qwen3.6 Plus84.7%
    Source

    Not directly comparable

  • NOVA-63

    Qwen3.6-27B—
    Qwen3.6 Plus57.9%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Qwen3.6-27B82.9%
    Source
    Qwen3.6 Plus86.0%
    Source

    Qwen3.6 Plus leads this result

  • MMMU-Pro

    Qwen3.6-27B75.8%
    Source
    Qwen3.6 Plus78.8%
    Source

    Qwen3.6 Plus leads this result

  • RealWorldQA

    Qwen3.6-27B84.1%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • DynaMath

    Qwen3.6-27B85.6%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • MStar

    Qwen3.6-27B81.4%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • SimpleVQA

    Qwen3.6-27B56.1%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • CharXiv

    Qwen3.6-27B78.4%
    Source
    Qwen3.6 Plus81.5%
    Source

    Qwen3.6 Plus leads this result

  • CC-OCR

    Qwen3.6-27B81.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • CountBench

    Qwen3.6-27B97.8%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • RefCOCO (avg)

    Qwen3.6-27B92.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • ERQA

    Qwen3.6-27B62.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Video-MME (with subtitle)

    Qwen3.6-27B87.7%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • VideoMMMU

    Qwen3.6-27B84.4%
    Source
    Qwen3.6 Plus84.0%
    Source

    Qwen3.6-27B leads this result

  • MLVU (M-Avg)

    Qwen3.6-27B86.6%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • V*

    Qwen3.6-27B94.7%
    Source
    Qwen3.6 Plus96.9%
    Source

    Qwen3.6 Plus leads this result

  • MathVision

    Qwen3.6-27B—
    Qwen3.6 Plus88.0%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Qwen3.6-27B—
    Qwen3.6 Plus68.2%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Qwen3.6-27B—
    Qwen3.6 Plus94.3%
    Source

    Not directly comparable

  • IFBench

    Qwen3.6-27B—
    Qwen3.6 Plus75.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.6-27B or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public score estimate, 61.49 versus 52.73, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Qwen3.6-27B or Qwen3.6 Plus?

Qwen3.6 Plus leads the public coding lane, 47 to 42.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Qwen3.6-27B or Qwen3.6 Plus?

Qwen3.6 Plus leads the public agentic tasks lane, 36.3 to 33.9, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Qwen3.6-27B 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, Qwen3.6-27B or Qwen3.6 Plus?

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

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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