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Model A
Qwen3.5 Flash

Alibaba

56.03/100

Supported · Public rank #102

90% interval 46.166.0

Qwen3.5 Flash vs Qwen3.6-27B

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

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Model B
Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.058.5

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.5 Flash

    Qwen3.5 Flash 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

    Qwen3.5 Flash 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

    Qwen3.5 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.

    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: rate-fallback

  • 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
0
Qwen3.5 Flash only
2
Qwen3.6-27B only
38
Like-for-like categories
0 / 8

1 category rests 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.

Coding

Directional only
Qwen3.5 Flash
47.0
Estimated · #93/183
Qwen3.6-27B
42.6
Supported · #128/183
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Directional only

Agentic

Not comparable
Qwen3.5 Flash
Not ranked
Qwen3.6-27B
33.9
Supported · #133/151
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.5 Flash
Not ranked
Qwen3.6-27B
73.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.5 Flash
Not ranked
Qwen3.6-27B
49.0
Estimated · #95/181
Basis
BenchAlign lane · 0 vs 6 public rows
Reading
Not comparable

Math

Not comparable
Qwen3.5 Flash
28.6
Unranked · 2 rankable rows
Qwen3.6-27B
72.8
Unranked · 5 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.5 Flash
Not ranked
Qwen3.6-27B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.5 Flash
Not ranked
Qwen3.6-27B
51.5
#35/48
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.5 Flash
Not ranked
Qwen3.6-27B
82.2
#50/120
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) 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Qwen3.5 Flash
$0.0003
Fits in one request
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request

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

Repository review

50K fresh input + 3K output tokens

Qwen3.5 Flash
$0.0062
Fits in one request
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request

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

Cache-heavy agent loop

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

Qwen3.5 Flash
$0.026
Fits in one request
Cached input priced at the published list-input rate
Qwen3.6-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Qwen3.5 Flash has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6-27B 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.

Qwen3.5 Flash

1M

Qwen3.6-27B

262K

API model ID

Qwen3.5 Flash

Not sourced

Qwen3.6-27B

Not sourced

Cached-input rate

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

Qwen3.5 Flash

Not published

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

Qwen3.5 Flash

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

Qwen3.5 Flash

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

Qwen3.5 Flash

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

Qwen3.5 Flash

Reasoning

Qwen3.6-27B

Reasoning

Weight access

Qwen3.5 Flash

Proprietary

Qwen3.6-27B

Open Weight

License

Qwen3.5 Flash

Proprietary

Qwen3.6-27B

Open Weight

Release date

Qwen3.5 Flash

2026-03-04

Qwen3.6-27B

2026-04-21

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Qwen3.5 Flash 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.

Qwen3.5 Flash
API / mo$375
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even
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 evidence40 rows

Agentic

  • Terminal-Bench 2.0

    Qwen3.5 Flash
    Qwen3.6-27B59.3%
    Source

    Not directly comparable

  • Claw-Eval

    Qwen3.5 Flash
    Qwen3.6-27B72.4%
    Source

    Not directly comparable

  • QwenClawBench

    Qwen3.5 Flash
    Qwen3.6-27B53.4%
    Source

    Not directly comparable

  • QwenWebBench

    Qwen3.5 Flash
    Qwen3.6-27B1487
    Source

    Not directly comparable

  • AndroidWorld

    Qwen3.5 Flash
    Qwen3.6-27B70.3%
    Source

    Not directly comparable

  • Gert Labs

    Qwen3.5 Flash
    Qwen3.6-27B54.84%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Qwen3.5 Flash
    Qwen3.6-27B77.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Qwen3.5 Flash
    Qwen3.6-27B71.3%
    Source

    Not directly comparable

  • SWE-bench Pro

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

    Not directly comparable

  • Terminal-Bench 2.0

    Qwen3.5 Flash
    Qwen3.6-27B59.3%
    Source

    Not directly comparable

  • LiveCodeBench

    Qwen3.5 Flash
    Qwen3.6-27B83.9%
    Source

    Not directly comparable

  • NL2Repo

    Qwen3.5 Flash
    Qwen3.6-27B36.2%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

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

    Not directly comparable

  • MMLU-Redux

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

    Not directly comparable

  • SuperGPQA

    Qwen3.5 Flash
    Qwen3.6-27B66%
    Source

    Not directly comparable

  • C-Eval

    Qwen3.5 Flash
    Qwen3.6-27B91.4%
    Source

    Not directly comparable

  • GPQA

    Qwen3.5 Flash
    Qwen3.6-27B87.8%
    Source

    Not directly comparable

  • HLE

    Qwen3.5 Flash
    Qwen3.6-27B24%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Qwen3.5 Flash6.207%
    Source
    Qwen3.6-27B

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Qwen3.5 Flash0.000%
    Source
    Qwen3.6-27B

    Not directly comparable

  • HMMT Feb 2025

    Qwen3.5 Flash
    Qwen3.6-27B93.8%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Qwen3.5 Flash
    Qwen3.6-27B90.7%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Qwen3.5 Flash
    Qwen3.6-27B84.3%
    Source

    Not directly comparable

  • MMAnswerBench

    Qwen3.5 Flash
    Qwen3.6-27B80.8%
    Source

    Not directly comparable

  • AIME26

    Qwen3.5 Flash
    Qwen3.6-27B94.1%
    Source

    Not directly comparable

Multimodal

  • MMMU

    Qwen3.5 Flash
    Qwen3.6-27B82.9%
    Source

    Not directly comparable

  • MMMU-Pro

    Qwen3.5 Flash
    Qwen3.6-27B75.8%
    Source

    Not directly comparable

  • RealWorldQA

    Qwen3.5 Flash
    Qwen3.6-27B84.1%
    Source

    Not directly comparable

  • DynaMath

    Qwen3.5 Flash
    Qwen3.6-27B85.6%
    Source

    Not directly comparable

  • MStar

    Qwen3.5 Flash
    Qwen3.6-27B81.4%
    Source

    Not directly comparable

  • SimpleVQA

    Qwen3.5 Flash
    Qwen3.6-27B56.1%
    Source

    Not directly comparable

  • CharXiv

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

    Not directly comparable

  • CC-OCR

    Qwen3.5 Flash
    Qwen3.6-27B81.2%
    Source

    Not directly comparable

  • CountBench

    Qwen3.5 Flash
    Qwen3.6-27B97.8%
    Source

    Not directly comparable

  • RefCOCO (avg)

    Qwen3.5 Flash
    Qwen3.6-27B92.5%
    Source

    Not directly comparable

  • ERQA

    Qwen3.5 Flash
    Qwen3.6-27B62.5%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Qwen3.5 Flash
    Qwen3.6-27B87.7%
    Source

    Not directly comparable

  • VideoMMMU

    Qwen3.5 Flash
    Qwen3.6-27B84.4%
    Source

    Not directly comparable

  • MLVU (M-Avg)

    Qwen3.5 Flash
    Qwen3.6-27B86.6%
    Source

    Not directly comparable

  • V*

    Qwen3.5 Flash
    Qwen3.6-27B94.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Qwen3.5 Flash or Qwen3.6-27B?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Qwen3.5 Flash or Qwen3.6-27B?

Qwen3.5 Flash scores higher for coding on the public lane, 47 to 42.6. Qwen3.5 Flash 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, Qwen3.5 Flash or Qwen3.6-27B?

Qwen3.5 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Qwen3.5 Flash or Qwen3.6-27B?

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, Qwen3.5 Flash or Qwen3.6-27B?

Qwen3.5 Flash has the larger documented context window: 1M, compared with 262K.

Related comparisons

Last updated September 4, 2026

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