Qwen3.5-27B vs Qwen3.7 Max

Updated October 2, 2026. Rank says Qwen3.7 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.7 Max has the higher public point estimate, 63.46 versus 48.33. Their conditional score ranges overlap. These ranges do not establish rank confidence. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Qwen3.5-27B

Alibaba

48.33/100

Estimated · Public rank #98

Conditional range 38.6–58.0

Model B

Qwen3.7 Max

Alibaba

63.46/100

Supported · Public rank #43

90% interval 54.0–72.9

Shared results: 8
Qwen3.5-27B only: 8
Qwen3.7 Max only: 33
Like-for-like categories: 2 / 8

Estimated: Qwen3.5-27B · Supported: Qwen3.7 Max. 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
Qwen3.7 Max
Qwen3.7 Max 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
Qwen3.5-27B 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
Qwen3.5-27B 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
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.

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.

34.9Qwen3.5-27B45.4Qwen3.7 Max

Directional only · BenchAlign v5.8

Qwen3.7 Max has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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 28.1
Qwen3.5-27B:41.6%
Qwen3.7 Max:69.7%
Qwen3.5-27B source Qwen3.7 Max source
SWE-bench VerifiedCoding
Normalized gap 8.0
Qwen3.5-27B:72.4%
Qwen3.7 Max:80.4%
Qwen3.5-27B source Qwen3.7 Max source
SuperGPQAKnowledge
Normalized gap 8.0
Qwen3.5-27B:65.6%
Qwen3.7 Max:73.6%
Qwen3.5-27B source Qwen3.7 Max source
GPQAKnowledge
Normalized gap 6.9
Qwen3.5-27B:85.5%
Qwen3.7 Max:92.4%
Qwen3.5-27B source Qwen3.7 Max source
MMLU-ProXMultilingual
Normalized gap 4.8
Qwen3.5-27B:82.2%
Qwen3.7 Max:87%
Qwen3.5-27B source Qwen3.7 Max 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.

Knowledge

Like-for-like

Qwen3.5-27B: 44.4; Supported · #83/171
Qwen3.7 Max: 59.8; Supported · #41/171
Basis: BenchAlign v5.8 lane · 3 vs 9 public rows
Reading: Qwen3.7 Max leads · intervals overlap

Multilingual

Like-for-like

Qwen3.5-27B: 87.1; #10/16
Qwen3.7 Max: 100.0; #1/16
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Qwen3.7 Max leads

Agentic

Directional only

Qwen3.5-27B: 35.1; Estimated · #70/119
Qwen3.7 Max: 39.3; Supported · #60/119
Basis: BenchAlign v5.8 lane · 4 vs 10 public rows
Reading: Directional only

Coding

Directional only

Qwen3.5-27B: 34.9; Estimated · #83/144
Qwen3.7 Max: 45.4; Supported · #54/144
Basis: BenchAlign v5.8 lane · 2 vs 10 public rows
Reading: Directional only

Instruction following

Directional only

Qwen3.5-27B: 91.5; #12/125
Qwen3.7 Max: 89.2; #17/125
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

Qwen3.5-27B: 52.7; Unranked · 3 rankable rows
Qwen3.7 Max: 76.3; Unranked · 3 rankable rows
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Qwen3.5-27B: 71.6; Unranked · 3 rankable rows
Qwen3.7 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Qwen3.5-27B: Not ranked
Qwen3.7 Max: 81.9; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Qwen3.5-27B and Qwen3.7 Max
Category	Qwen3.5-27B	Qwen3.7 Max	Basis	Reading
Knowledge	44.4Supported · #83/171	59.8Supported · #41/171	Like-for-likeBenchAlign v5.8 lane · 3 vs 9 public rows	Qwen3.7 Max leads · intervals overlap
Multilingual	87.1#10/16	100.0#1/16	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Qwen3.7 Max leads
Agentic	35.1Estimated · #70/119	39.3Supported · #60/119	Directional onlyBenchAlign v5.8 lane · 4 vs 10 public rows	Directional only
Coding	34.9Estimated · #83/144	45.4Supported · #54/144	Directional onlyBenchAlign v5.8 lane · 2 vs 10 public rows	Directional only
Instruction following	91.5#12/125	89.2#17/125	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	52.7Unranked · 3 rankable rows	76.3Unranked · 3 rankable rows	Not comparableProvisional lane · 1 vs 1 weighted rows	Not comparable
Multimodal	71.6Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	81.9Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 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

Qwen3.5-27B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.5-27B has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Qwen3.5-27B: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.5-27B has no comparable published API token rate. Qwen3.7 Max has no comparable published API token rate.

Cache-heavy agent loop

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

Qwen3.5-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Qwen3.7 Max: API rate not published; Fits in one request; Cached-input rate unavailable

Qwen3.5-27B has no comparable published API token rate. Qwen3.7 Max 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.

SpecificationQwen3.5-27BQwen3.7 Max

Context window

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

Qwen3.5-27B

262K

Qwen3.7 Max

API model ID

Qwen3.5-27B

Not sourced

Qwen3.7 Max

Not sourced

Cached-input rate

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

Qwen3.5-27B

No comparable hosted API rate

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

Qwen3.5-27B

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

Qwen3.5-27B

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

Qwen3.5-27B

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

Qwen3.5-27B

Reasoning

Qwen3.7 Max

Reasoning

Weight access

Qwen3.5-27B

Open Weight

Qwen3.7 Max

Proprietary

License

Qwen3.5-27B

Open Weight

Qwen3.7 Max

Proprietary

Release date

Qwen3.5-27B

2026-03-04

Qwen3.7 Max

2026-05-16

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: Qwen3.7 Max has the higher public point estimate, 63.46 versus 48.33. 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: Qwen3.7 Max has the larger documented window (1M).

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

Questions

Which is better, Qwen3.5-27B or Qwen3.7 Max?

Qwen3.7 Max has the higher public point estimate, 63.46 versus 48.33. 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, Qwen3.5-27B or Qwen3.7 Max?

Qwen3.7 Max scores higher for coding on the public lane, 45.4 to 34.9. Qwen3.5-27B 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, Qwen3.5-27B or Qwen3.7 Max?

Qwen3.7 Max scores higher for agentic tasks on the public lane, 39.3 to 35.1. Qwen3.5-27B 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, Qwen3.5-27B or Qwen3.7 Max?

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, Qwen3.5-27B or Qwen3.7 Max?

Qwen3.7 Max has the larger documented context window: 1M, 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 evidence49 rows

Agentic

Terminal-Bench 2.0
Qwen3.5-27B41.6%
Source
Qwen3.7 Max69.7%
Source
Qwen3.7 Max leads this result
BrowseComp
Qwen3.5-27B61%
Source
Qwen3.7 Max—
Not directly comparable
OSWorld-Verified
Qwen3.5-27B56.2%
Source
Qwen3.7 Max—
Not directly comparable
Gert Labs
Shared source
Qwen3.5-27B39.41%
Qwen3.7 Max64.27%
Qwen3.7 Max leads this result
QwenClawBench
Qwen3.5-27B—
Qwen3.7 Max64.3%
Source
Not directly comparable
Claw-Eval
Qwen3.5-27B—
Qwen3.7 Max65.2%
Source
Not directly comparable
BFCL v4
Qwen3.5-27B—
Qwen3.7 Max75.0%
Source
Not directly comparable
MCP Atlas
Qwen3.5-27B—
Qwen3.7 Max76.4%
Source
Not directly comparable
VITA-Bench
Qwen3.5-27B—
Qwen3.7 Max47.9%
Source
Not directly comparable
HLE w/ tools
Qwen3.5-27B—
Qwen3.7 Max53.5%
Source
Not directly comparable
ResearchClawBench
Qwen3.5-27B—
Qwen3.7 Max18.7%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Qwen3.5-27B—
Qwen3.7 Max61.0%
Source
Not directly comparable

Coding

SWE-bench Verified
Qwen3.5-27B72.4%
Source
Qwen3.7 Max80.4%
Source
Qwen3.7 Max leads this result
SWE-Rebench
Qwen3.5-27B58.9%
Source
Qwen3.7 Max—
Not directly comparable
SWE-bench Pro
Qwen3.5-27B—
Qwen3.7 Max60.6%
Source
Not directly comparable
SWE Multilingual
Qwen3.5-27B—
Qwen3.7 Max78.3%
Source
Not directly comparable
NL2Repo
Qwen3.5-27B—
Qwen3.7 Max47.2%
Source
Not directly comparable
SciCode
Qwen3.5-27B—
Qwen3.7 Max53.5%
Source
Not directly comparable
LiveCodeBench
Qwen3.5-27B—
Qwen3.7 Max91.6%
Source
Not directly comparable
Terminal-Bench 2.0
Qwen3.5-27B—
Qwen3.7 Max69.7%
Source
Not directly comparable
OpenHarmony Bench
Qwen3.5-27B—
Qwen3.7 Max53.4%
Source
Not directly comparable
LiveCodeBench (Vals)
Qwen3.5-27B—
Qwen3.7 Max87.1%
Source
Not directly comparable
SWE-bench (Vals)
Qwen3.5-27B—
Qwen3.7 Max68.8%
Source
Not directly comparable

Reasoning

LongBench v2
Qwen3.5-27B60.6%
Source
Qwen3.7 Max—
Not directly comparable
MRCRv2
Qwen3.5-27B—
Qwen3.7 Max90.4%
Source
Not directly comparable
CritPt
Qwen3.5-27B—
Qwen3.7 Max13.4%
Source
Not directly comparable

Multimodal

MMMU
Qwen3.5-27B82.3%
Source
Qwen3.7 Max—
Not directly comparable
MMVU
Qwen3.5-27B73.3%
Source
Qwen3.7 Max—
Not directly comparable
MathVision
Qwen3.5-27B86.0%
Source
Qwen3.7 Max—
Not directly comparable
V*
Qwen3.5-27B93.7%
Source
Qwen3.7 Max—
Not directly comparable

Knowledge

MMLU-Pro
Qwen3.5-27B86.1%
Source
Qwen3.7 Max89.6%
Source
Qwen3.7 Max leads this result
SuperGPQA
Qwen3.5-27B65.6%
Source
Qwen3.7 Max73.6%
Source
Qwen3.7 Max leads this result
GPQA
Qwen3.5-27B85.5%
Source
Qwen3.7 Max92.4%
Source
Qwen3.7 Max leads this result
GPQA-D
Qwen3.5-27B—
Qwen3.7 Max92.4%
Source
Not directly comparable
HLE
Qwen3.5-27B—
Qwen3.7 Max41.4%
Source
Not directly comparable
MMLU-Redux
Qwen3.5-27B—
Qwen3.7 Max95%
Source
Not directly comparable
MMMLU
Qwen3.5-27B—
Qwen3.7 Max90.3%
Source
Not directly comparable
GPQA Diamond (Vals)
Qwen3.5-27B—
Qwen3.7 Max90.2%
Source
Not directly comparable
MMLU-Pro (Vals)
Qwen3.5-27B—
Qwen3.7 Max89.3%
Source
Not directly comparable

Multilingual

MMLU-ProX
Qwen3.5-27B82.2%
Source
Qwen3.7 Max87%
Source
Qwen3.7 Max leads this result
NOVA-63
Qwen3.5-27B—
Qwen3.7 Max59.0%
Source
Not directly comparable
INCLUDE
Qwen3.5-27B—
Qwen3.7 Max86.2%
Source
Not directly comparable
MAXIFE
Qwen3.5-27B—
Qwen3.7 Max89.2%
Source
Not directly comparable
PolyMath
Qwen3.5-27B—
Qwen3.7 Max86.5%
Source
Not directly comparable

Instruction following

IFEval
Qwen3.5-27B95%
Source
Qwen3.7 Max94.3%
Source
Qwen3.5-27B leads this result
IFBench
Qwen3.5-27B—
Qwen3.7 Max79.1%
Source
Not directly comparable

Math

HMMT Feb 2026
Qwen3.5-27B—
Qwen3.7 Max97.1%
Source
Not directly comparable
IMOAnswerBench
Qwen3.5-27B—
Qwen3.7 Max90.0%
Source
Not directly comparable
Apex
Qwen3.5-27B—
Qwen3.7 Max44.5%
Source
Not directly comparable

49 public results · 8 shared

Related comparisons

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Last updated October 2, 2026

Qwen3.5-27B vs Qwen3.7 Max

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

Knowledge

Multilingual

Agentic

Coding

Instruction following

Reasoning

Multimodal

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, Qwen3.5-27B or Qwen3.7 Max?

Which is better for coding, Qwen3.5-27B or Qwen3.7 Max?

Which is better for agentic tasks, Qwen3.5-27B or Qwen3.7 Max?

Which costs less, Qwen3.5-27B or Qwen3.7 Max?

Which has the larger context window, Qwen3.5-27B or Qwen3.7 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Qwen3.5-27B vs Qwen3.7 Max