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BenchLM

Qwen3.5 Flash vs Qwen 3.6 Max (preview)

Updated September 27, 2026. Rank says Qwen 3.6 Max (preview) is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Qwen 3.6 Max (preview) has the higher public score estimate, 54.97 versus 45.49, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Alibaba logo

Alibaba

45.49/100

Estimated · Public rank #93

90% interval 36.4–54.6

Model B
Alibaba logo

Alibaba

54.97/100

Estimated · Public rank #57

90% interval 45.7–64.2

Shared results
3
Qwen3.5 Flash only
3
Qwen 3.6 Max (preview) only
7
Like-for-like categories
0 / 8
Estimated: Qwen3.5 Flash and Qwen 3.6 Max (preview)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

    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 and Qwen 3.6 Max (preview) are 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

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.

26.5Qwen3.5 Flash46.9Qwen 3.6 Max (preview)

Directional only · BenchAlign v5.7

Qwen 3.6 Max (preview) scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

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.

  • FrontierMath v2 (Tiers 1-3)Math

    Normalized gap 16.9
    Qwen3.5 Flash:6.207%
    Qwen 3.6 Max (preview):23.103%
  • FrontierMath v2 (Tier 4)Math

    Normalized gap 4.2
    Qwen3.5 Flash:0.000%
    Qwen 3.6 Max (preview):4.167%
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 v5.7 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
26.5
Estimated · #97/135
Qwen 3.6 Max (preview)
46.9
Supported · #44/135
Basis
BenchAlign v5.7 lane · 2 vs 5 public rows
Reading
Directional only

Agentic

Not comparable
Qwen3.5 Flash
Not ranked
Qwen 3.6 Max (preview)
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
Qwen3.5 Flash
Not ranked
Qwen 3.6 Max (preview)
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Qwen3.5 Flash
Not ranked
Qwen 3.6 Max (preview)
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Qwen3.5 Flash
43.0
Estimated · #80/158
Qwen 3.6 Max (preview)
Not ranked
Basis
BenchAlign v5.7 lane · 2 vs 1 public rows
Reading
Not comparable

Multilingual

Not comparable
Qwen3.5 Flash
Not ranked
Qwen 3.6 Max (preview)
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Qwen3.5 Flash
Not ranked
Qwen 3.6 Max (preview)
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Qwen3.5 Flash
28.4
Unranked · 2 rankable rows
Qwen 3.6 Max (preview)
41.3
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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

Qwen3.5 Flash
$0.0003
Fits in one request
Qwen 3.6 Max (preview)
API rate not published
Fits in one request

Qwen 3.6 Max (preview) has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.5 Flash
$0.0062
Fits in one request
Qwen 3.6 Max (preview)
API rate not published
Fits in one request

Qwen 3.6 Max (preview) 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
Qwen 3.6 Max (preview)
API rate not published
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. Qwen 3.6 Max (preview) 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.

Context window

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

Qwen3.5 Flash

1M

Qwen 3.6 Max (preview)

256K

API model ID

Qwen3.5 Flash

Not sourced

Qwen 3.6 Max (preview)

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

Qwen 3.6 Max (preview)

No comparable hosted API rate

Documented inputs

Qwen3.5 Flash

Not sourced

Qwen 3.6 Max (preview)

Not sourced

Documented outputs

Qwen3.5 Flash

Not sourced

Qwen 3.6 Max (preview)

Not sourced

Provider availability

Qwen3.5 Flash

Not sourced

Qwen 3.6 Max (preview)

Not sourced

Reasoning profile

Qwen3.5 Flash

Reasoning

Qwen 3.6 Max (preview)

Reasoning

Weight access

Qwen3.5 Flash

Proprietary

Qwen 3.6 Max (preview)

Proprietary

License

Qwen3.5 Flash

Proprietary

Qwen 3.6 Max (preview)

Proprietary

Release date

Qwen3.5 Flash

2026-03-04

Qwen 3.6 Max (preview)

2026-04-20

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
Qwen 3.6 Max (preview) has the higher public score estimate, 54.97 versus 45.49, but the 90% score intervals overlap.
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.

Questions

Which is better, Qwen3.5 Flash or Qwen 3.6 Max (preview)?

Qwen 3.6 Max (preview) has the higher public score estimate, 54.97 versus 45.49, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Qwen3.5 Flash or Qwen 3.6 Max (preview)?

Qwen 3.6 Max (preview) scores higher for coding on the public lane, 46.9 to 26.5. 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 Qwen 3.6 Max (preview)?

Qwen3.5 Flash and Qwen 3.6 Max (preview) are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Qwen3.5 Flash or Qwen 3.6 Max (preview)?

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 Qwen 3.6 Max (preview)?

Qwen3.5 Flash has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

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

Browse raw public benchmark evidence13 rows

Agentic

  • Terminal-Bench 2.0

    Qwen3.5 Flash—
    Qwen 3.6 Max (preview)65.4%
    Source

    Not directly comparable

  • QwenClawBench

    Qwen3.5 Flash—
    Qwen 3.6 Max (preview)59.0%
    Source

    Not directly comparable

Coding

  • LiveCodeBench (Vals)

    Qwen3.5 Flash83.3%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • SWE-bench (Vals)

    Qwen3.5 Flash64.4%
    Source
    Qwen 3.6 Max (preview)72.8%
    Source

    Qwen 3.6 Max (preview) leads this result

  • SWE-bench Pro

    Qwen3.5 Flash—
    Qwen 3.6 Max (preview)57.3%
    Source

    Not directly comparable

  • SciCode

    Qwen3.5 Flash—
    Qwen 3.6 Max (preview)47%
    Source

    Not directly comparable

  • NL2Repo

    Qwen3.5 Flash—
    Qwen 3.6 Max (preview)42.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Qwen3.5 Flash—
    Qwen 3.6 Max (preview)65.4%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Qwen3.5 Flash82.8%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • MMLU-Pro (Vals)

    Qwen3.5 Flash84.1%
    Source
    Qwen 3.6 Max (preview)—

    Not directly comparable

  • SuperGPQA

    Qwen3.5 Flash—
    Qwen 3.6 Max (preview)73.9%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Qwen3.5 Flash6.207%
    Qwen 3.6 Max (preview)23.103%

    Qwen 3.6 Max (preview) leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Qwen3.5 Flash0.000%
    Qwen 3.6 Max (preview)4.167%

    Qwen 3.6 Max (preview) leads this result

13 public results · 3 shared

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Last updated September 27, 2026