Qwen3.5 397B vs Qwen3.6 Plus

Updated October 6, 2026. Rank cannot separate these two. Price, access, and your workload decide. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.6 Plus has the higher public point estimate, 53.72 versus 53.65. Their conditional score ranges overlap. These ranges do not establish rank confidence. 37 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Qwen3.5 397B

Alibaba

53.65/100

Estimated · Public rank #71

Conditional range 39.3–68.0

Model B

Qwen3.6 Plus

Alibaba

53.72/100

Supported · Public rank #68

90% interval 44.7–62.8

Shared results: 37
Qwen3.5 397B only: 1
Qwen3.6 Plus only: 11
Like-for-like categories: 2 / 8

Estimated: Qwen3.5 397B · Supported: Qwen3.6 Plus. 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
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
Qwen3.5 397B 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
Qwen3.5 397B is 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6 Plus has no comparable published API token rate.
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.

Pick the job you would use them for

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

—Qwen3.5 397B41.7Qwen3.6 Plus

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 leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Terminal-Bench 2.0Agentic
Normalized gap 9.1
Qwen3.5 397B:52.5%
Qwen3.6 Plus:61.6%
Shared source
SWE-bench ProCoding
Normalized gap 5.7
Qwen3.5 397B:50.9%
Qwen3.6 Plus:56.6%
Shared source
SWE-bench VerifiedCoding
Normalized gap 2.6
Qwen3.5 397B:76.2%
Qwen3.6 Plus:78.8%
Shared source
GPQAKnowledge
Normalized gap 2.0
Qwen3.5 397B:88.4%
Qwen3.6 Plus:90.4%
Shared source
AIME26Math
Normalized gap 2.0
Qwen3.5 397B:93.3%
Qwen3.6 Plus:95.3%
Shared source

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.

Multimodal

Like-for-like

Qwen3.5 397B: 63.9; #27/49
Qwen3.6 Plus: 67.2; #23/49
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Qwen3.6 Plus leads

Multilingual

Like-for-like

Qwen3.5 397B: 93.8; #5/16
Qwen3.6 Plus: 93.8; #4/16
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Tie

Agentic

Directional only

Qwen3.5 397B: 38.8; Estimated · #62/120
Qwen3.6 Plus: 33.8; Supported · #75/120
Basis: BenchAlign v5.8 lane · 13 vs 13 public rows
Reading: Directional only

Knowledge

Directional only

Qwen3.5 397B: 49.1; Estimated · #71/173
Qwen3.6 Plus: 52.5; Supported · #62/173
Basis: BenchAlign v5.8 lane · 6 vs 8 public rows
Reading: Directional only

Instruction following

Directional only

Qwen3.5 397B: 0.0; #125/125
Qwen3.6 Plus: 82.5; #48/125
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Coding

Not comparable

Qwen3.5 397B: Not ranked
Qwen3.6 Plus: 41.7; Supported · #59/144
Basis: BenchAlign v5.8 lane · 3 vs 7 public rows
Reading: Not comparable

Reasoning

Not comparable

Qwen3.5 397B: 60.9; Unranked · 2 rankable rows
Qwen3.6 Plus: 61.3; Unranked · 4 rankable rows
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

Qwen3.5 397B: 73.5; Unranked · 5 rankable rows
Qwen3.6 Plus: 62.0; #5/7
Basis: Provisional lane · 2 vs 4 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Qwen3.5 397B and Qwen3.6 Plus
Category	Qwen3.5 397B	Qwen3.6 Plus	Basis	Reading
Multimodal	63.9#27/49	67.2#23/49	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Qwen3.6 Plus leads
Multilingual	93.8#5/16	93.8#4/16	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Tie
Agentic	38.8Estimated · #62/120	33.8Supported · #75/120	Directional onlyBenchAlign v5.8 lane · 13 vs 13 public rows	Directional only
Knowledge	49.1Estimated · #71/173	52.5Supported · #62/173	Directional onlyBenchAlign v5.8 lane · 6 vs 8 public rows	Directional only
Instruction following	0.0#125/125	82.5#48/125	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Coding	Not ranked	41.7Supported · #59/144	Not comparableBenchAlign v5.8 lane · 3 vs 7 public rows	Not comparable
Reasoning	60.9Unranked · 2 rankable rows	61.3Unranked · 4 rankable rows	Not comparableProvisional lane · 1 vs 1 weighted rows	Not comparable
Math	73.5Unranked · 5 rankable rows	62.0#5/7	Not comparableProvisional lane · 2 vs 4 weighted rows	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

Qwen3.5 397B: $0.0024; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.5 397B: $0.0408; Fits in one request
Qwen3.6 Plus: API rate not published; Fits in one request

Qwen3.6 Plus has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.5 397B: $0.168; Does not fit in one request; Cached input priced at the published list-input rate
Qwen3.6 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.5 397B does not fit this workload in one request. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6 Plus 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.

SpecificationQwen3.5 397BQwen3.6 Plus

Context window

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

Qwen3.5 397B

128K

Qwen3.6 Plus

API model ID

Qwen3.5 397B

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.

Qwen3.5 397B

Not published

Qwen3.6 Plus

No comparable hosted API rate

Documented inputs

Qwen3.5 397B

Not sourced

Qwen3.6 Plus

Not sourced

Documented outputs

Qwen3.5 397B

Not sourced

Qwen3.6 Plus

Not sourced

Provider availability

Qwen3.5 397B

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

Qwen3.5 397B

Non-Reasoning

Qwen3.6 Plus

Reasoning

Weight access

Qwen3.5 397B

Open Weight

Qwen3.6 Plus

Proprietary

License

Qwen3.5 397B

Open Weight

Qwen3.6 Plus

Proprietary

Release date

Qwen3.5 397B

2026-02-16

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 point estimate, 53.72 versus 53.65. 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: Qwen3.6 Plus has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Qwen3.5 397B or Qwen3.6 Plus?

Qwen3.6 Plus has the higher public point estimate, 53.72 versus 53.65. 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, Qwen3.5 397B or Qwen3.6 Plus?

Qwen3.5 397B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Qwen3.5 397B or Qwen3.6 Plus?

Qwen3.5 397B scores higher for agentic tasks on the public lane, 38.8 to 33.8. Qwen3.5 397B is 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, Qwen3.5 397B 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, Qwen3.5 397B or Qwen3.6 Plus?

Qwen3.6 Plus has the larger documented context window: 1M, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence49 rows

Agentic

Terminal-Bench 2.0
Shared source
Qwen3.5 397B52.5%
Qwen3.6 Plus61.6%
Qwen3.6 Plus leads this result
BrowseComp
Qwen3.5 397B62%
Source
Qwen3.6 Plus—
Not directly comparable
Claw-Eval
Shared source
Qwen3.5 397B56.8%
Qwen3.6 Plus58.8%
Qwen3.6 Plus leads this result
QwenClawBench
Shared source
Qwen3.5 397B51.8%
Qwen3.6 Plus57.2%
Qwen3.6 Plus leads this result
τ³-bench results
Shared source
Qwen3.5 397B68.4%
Qwen3.6 Plus70.7%
Qwen3.6 Plus leads this result
VITA-Bench
Shared source
Qwen3.5 397B43.7%
Qwen3.6 Plus44.3%
Qwen3.6 Plus leads this result
DeepPlanning
Shared source
Qwen3.5 397B37.6%
Qwen3.6 Plus41.5%
Qwen3.6 Plus leads this result
Toolathlon
Shared source
Qwen3.5 397B36.3%
Qwen3.6 Plus39.8%
Qwen3.6 Plus leads this result
MCP Atlas
Shared source
Qwen3.5 397B46.1%
Qwen3.6 Plus48.2%
Qwen3.6 Plus leads this result
MCP-Tasks
Shared source
Qwen3.5 397B74.2%
Qwen3.6 Plus74.1%
Qwen3.5 397B leads this result
WideResearch
Shared source
Qwen3.5 397B74.0%
Qwen3.6 Plus74.3%
Qwen3.6 Plus leads this result
Gert Labs
Shared source
Qwen3.5 397B46.76%
Qwen3.6 Plus50.60%
Qwen3.6 Plus leads this result
ResearchClawBench
Shared source
Qwen3.5 397B14.2%
Qwen3.6 Plus18.0%
Qwen3.6 Plus leads this result
Terminal-Bench 2.1 (Vals)
Qwen3.5 397B—
Qwen3.6 Plus53.2%
Source
Not directly comparable

Coding

SWE-bench Verified
Shared source
Qwen3.5 397B76.2%
Qwen3.6 Plus78.8%
Qwen3.6 Plus leads this result
LiveCodeBench v6
Shared source
Qwen3.5 397B83.6%
Qwen3.6 Plus87.1%
Qwen3.6 Plus leads this result
SWE-bench Pro
Shared source
Qwen3.5 397B50.9%
Qwen3.6 Plus56.6%
Qwen3.6 Plus leads this result
SWE Multilingual
Qwen3.5 397B—
Qwen3.6 Plus73.8%
Source
Not directly comparable
Vibe Code Bench
Qwen3.5 397B—
Qwen3.6 Plus25.56%
Source
Not directly comparable
LiveCodeBench (Vals)
Qwen3.5 397B—
Qwen3.6 Plus86.0%
Source
Not directly comparable
SWE-bench (Vals)
Qwen3.5 397B—
Qwen3.6 Plus73.4%
Source
Not directly comparable

Reasoning

LongBench v2
Shared source
Qwen3.5 397B63.2%
Qwen3.6 Plus62%
Qwen3.5 397B leads this result
AI-Needle
Shared source
Qwen3.5 397B68.7%
Qwen3.6 Plus68.3%
Qwen3.5 397B leads this result

Multimodal

MMMU-Pro
Shared source
Qwen3.5 397B79%
Qwen3.6 Plus78.8%
Qwen3.5 397B leads this result
MathVision
Shared source
Qwen3.5 397B88.6%
Qwen3.6 Plus88.0%
Qwen3.5 397B leads this result
CharXiv
Shared source
Qwen3.5 397B80.8%
Qwen3.6 Plus81.5%
Qwen3.6 Plus leads this result
VideoMMMU
Shared source
Qwen3.5 397B84.7%
Qwen3.6 Plus84.0%
Qwen3.5 397B leads this result
ScreenSpot Pro
Shared source
Qwen3.5 397B65.6%
Qwen3.6 Plus68.2%
Qwen3.6 Plus leads this result
V*
Shared source
Qwen3.5 397B95.8%
Qwen3.6 Plus96.9%
Qwen3.6 Plus leads this result
MMMU
Qwen3.5 397B—
Qwen3.6 Plus86.0%
Source
Not directly comparable

Knowledge

GPQA
Shared source
Qwen3.5 397B88.4%
Qwen3.6 Plus90.4%
Qwen3.6 Plus leads this result
SuperGPQA
Shared source
Qwen3.5 397B70.4%
Qwen3.6 Plus71.6%
Qwen3.6 Plus leads this result
MMLU-Pro
Shared source
Qwen3.5 397B87.8%
Qwen3.6 Plus88.5%
Qwen3.6 Plus leads this result
MMLU-Redux
Shared source
Qwen3.5 397B94.9%
Qwen3.6 Plus94.5%
Qwen3.5 397B leads this result
C-Eval
Shared source
Qwen3.5 397B93%
Qwen3.6 Plus93.3%
Qwen3.6 Plus leads this result
HLE
Shared source
Qwen3.5 397B28.7%
Qwen3.6 Plus28.8%
Qwen3.6 Plus leads this result
GPQA Diamond (Vals)
Qwen3.5 397B—
Qwen3.6 Plus87.4%
Source
Not directly comparable
MMLU-Pro (Vals)
Qwen3.5 397B—
Qwen3.6 Plus87.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
Shared source
Qwen3.5 397B84.7%
Qwen3.6 Plus84.7%
Tie
NOVA-63
Shared source
Qwen3.5 397B59.1%
Qwen3.6 Plus57.9%
Qwen3.5 397B leads this result

Instruction following

IFEval
Shared source
Qwen3.5 397B92.6%
Qwen3.6 Plus94.3%
Qwen3.6 Plus leads this result
IFBench
Qwen3.5 397B—
Qwen3.6 Plus75.8%
Source
Not directly comparable

Math

AIME26
Shared source
Qwen3.5 397B93.3%
Qwen3.6 Plus95.3%
Qwen3.6 Plus leads this result
HMMT Feb 2025
Shared source
Qwen3.5 397B94.8%
Qwen3.6 Plus96.7%
Qwen3.6 Plus leads this result
HMMT Nov 2025
Shared source
Qwen3.5 397B92.7%
Qwen3.6 Plus94.6%
Qwen3.6 Plus leads this result
HMMT Feb 2026
Shared source
Qwen3.5 397B87.9%
Qwen3.6 Plus87.8%
Qwen3.5 397B leads this result
MMAnswerBench
Shared source
Qwen3.5 397B80.9%
Qwen3.6 Plus83.8%
Qwen3.6 Plus leads this result
FrontierMath v2 (Tiers 1-3)
Qwen3.5 397B—
Qwen3.6 Plus26.207%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Qwen3.5 397B—
Qwen3.6 Plus8.333%
Source
Not directly comparable

49 public results · 37 shared

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Last updated October 6, 2026

Qwen3.5 397B vs Qwen3.6 Plus

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Multimodal

Multilingual

Agentic

Knowledge

Instruction following

Coding

Reasoning

Math

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

If you already use one of these models

Questions

Which is better, Qwen3.5 397B or Qwen3.6 Plus?

Which is better for coding, Qwen3.5 397B or Qwen3.6 Plus?

Which is better for agentic tasks, Qwen3.5 397B or Qwen3.6 Plus?

Which costs less, Qwen3.5 397B or Qwen3.6 Plus?

Which has the larger context window, Qwen3.5 397B or Qwen3.6 Plus?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Qwen3.5 397B vs Qwen3.6 Plus