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BenchLM
Data

GLM-5.3 vs Qwen3.6 Plus

Updated November 13, 2026. Rank says GLM-5.3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

GLM-5.3 has the higher public score estimate, 60.96 versus 53.41, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 7 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Z.AI logo
GLM-5.3

Z.AI

60.96/100

Supported · Public rank #48

90% interval 51.2–70.7

Model B
Alibaba logo
Qwen3.6 Plus

Alibaba

53.41/100

Supported · Public rank #76

90% interval 43.5–63.3

API price per 1M tokens

GLM-5.3: No comparable API price

Qwen3.6 Plus: No comparable API price

Context window

GLM-5.3: 1M

Qwen3.6 Plus: 1M

Released

GLM-5.3: August 14, 2026

Qwen3.6 Plus: April 2, 2026

Shared results
7
GLM-5.3 only
32
Qwen3.6 Plus only
42
Like-for-like categories
4 / 8
Supported: GLM-5.3 and Qwen3.6 PlusHow 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GLM-5.3

    GLM-5.3 has the higher public coding point estimate, 53.6 to 39.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Agentic work

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

    GLM-5.3

    GLM-5.3 has the higher public agentic point estimate, 66.7 to 34.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited
Show secondary and unsupported calls

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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.

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

53.6GLM-5.339.8Qwen3.6 Plus

Like-for-like · BenchAlign v5.8

GLM-5.3 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 leaderboardShortlist models for this job
Same basis rules as the category table below

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

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.

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, reasoning, multimodal, 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
GLM-5.3
66.7
Supported · #14/135
Qwen3.6 Plus
34.3
Supported · #87/135
Basis
BenchAlign v5.8 lane · 17 vs 13 public rows
Reading
GLM-5.3 leads

Coding

Like-for-like
GLM-5.3
53.6
Supported · #33/162
Qwen3.6 Plus
39.8
Supported · #72/162
Basis
BenchAlign v5.8 lane · 17 vs 9 public rows
Reading
GLM-5.3 leads · intervals overlap

Reasoning

Like-for-like
GLM-5.3
57.3
Supported · #36/160
Qwen3.6 Plus
35.6
Supported · #98/160
Basis
BenchAlign v5.8 lane · 0 vs 2 public rows
Reading
GLM-5.3 leads

Knowledge

Like-for-like
GLM-5.3
59.9
Supported · #48/180
Qwen3.6 Plus
53.1
Supported · #66/180
Basis
BenchAlign v5.8 lane · 2 vs 8 public rows
Reading
GLM-5.3 leads · intervals overlap

Multimodal

Not comparable
GLM-5.3
Not ranked
Qwen3.6 Plus
55.2
Estimated · #46/67
Basis
BenchAlign v5.8 lane · 0 vs 7 public rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.3
Not ranked
Qwen3.6 Plus
93.8
#4/17
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.3
Not ranked
Qwen3.6 Plus
85.8
#50/157
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5.3
67.0
Unranked · 2 rankable rows
Qwen3.6 Plus
62.3
#6/12
Basis
Provisional lane · 2 vs 3 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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

GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.6 Plus
API rate not published
Fits in one request

GLM-5.3 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

GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.6 Plus
API rate not published
Fits in one request

GLM-5.3 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

GLM-5.3
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

GLM-5.3 has no comparable published API token 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.

Context window

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

GLM-5.3

1M

Z.AI GLM-5.3 model card

Qwen3.6 Plus

1M

Cached-input rate

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

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Qwen3.6 Plus

No comparable hosted API rate

Provider availability

GLM-5.3

Not sourced

Qwen3.6 Plus

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Qwen3.6 Plus

Reasoning

Weight access

GLM-5.3

Open Weight

Qwen3.6 Plus

Proprietary

License

GLM-5.3

Open Weight

Qwen3.6 Plus

Proprietary

Release date

GLM-5.3

2026-08-14

Qwen3.6 Plus

2026-04-02

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
GLM-5.3 has the higher public score estimate, 60.96 versus 53.41, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-5.3 or Qwen3.6 Plus?

GLM-5.3 has the higher public score estimate, 60.96 versus 53.41, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-5.3 or Qwen3.6 Plus?

GLM-5.3 has the higher public coding point estimate, 53.6 to 39.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GLM-5.3 or Qwen3.6 Plus?

GLM-5.3 has the higher public agentic tasks point estimate, 66.7 to 34.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, GLM-5.3 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, GLM-5.3 or Qwen3.6 Plus?

Both models list the same context window, 1M.

Self-host vs API cost

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

GLM-5.3
API / mo (DeepInfra)$2,297
Self-host / mo$27,962
Break-evenNot reached at 70% load
Qwen3.6 Plus
API / moNo API price on file
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 evidence81 rows

Agentic

  • Terminal-Bench 4.1

    GLM-5.341.20%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Finch

    GLM-5.365.1%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • b3 Agent Security

    GLM-5.393.0%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Cyber Misuse Refusal

    GLM-5.387.0%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Terminal-Bench 2.1

    GLM-5.388.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • terminalBench3

    GLM-5.328.3%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • CyberGym

    GLM-5.384.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • ExploitGym

    GLM-5.315.0%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Toolathlon-Verified

    GLM-5.373.0%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • AutomationBench

    GLM-5.348.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Agents' Last Exam

    GLM-5.328.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • HLE w/ tools

    GLM-5.362.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5.371.5%
    Source
    Qwen3.6 Plus53.2%
    Source

    GLM-5.3 leads this result

  • Hermes Index

    GLM-5.339.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Hermes Bench

    GLM-5.371.8%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • MazeBench

    GLM-5.30%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • CWE-bench

    GLM-5.342.0%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.3—
    Qwen3.6 Plus61.6%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-5.3—
    Qwen3.6 Plus58.8%
    Source

    Not directly comparable

  • QwenClawBench

    GLM-5.3—
    Qwen3.6 Plus57.2%
    Source

    Not directly comparable

  • τ³-bench results

    GLM-5.3—
    Qwen3.6 Plus70.7%
    Source

    Not directly comparable

  • VITA-Bench

    GLM-5.3—
    Qwen3.6 Plus44.3%
    Source

    Not directly comparable

  • DeepPlanning

    GLM-5.3—
    Qwen3.6 Plus41.5%
    Source

    Not directly comparable

  • Toolathlon

    GLM-5.3—
    Qwen3.6 Plus39.8%
    Source

    Not directly comparable

  • MCP Atlas

    GLM-5.3—
    Qwen3.6 Plus48.2%
    Source

    Not directly comparable

  • MCP-Tasks

    GLM-5.3—
    Qwen3.6 Plus74.1%
    Source

    Not directly comparable

  • WideResearch

    GLM-5.3—
    Qwen3.6 Plus74.3%
    Source

    Not directly comparable

  • Gert Labs

    GLM-5.3—
    Qwen3.6 Plus50.60%
    Source

    Not directly comparable

  • ResearchClawBench

    GLM-5.3—
    Qwen3.6 Plus18.0%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    GLM-5.388.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • terminalBench3

    GLM-5.328.3%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • DeepSWE

    GLM-5.366.9%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • NL2Repo

    GLM-5.358%
    Source
    Qwen3.6 Plus37.9%
    Source

    GLM-5.3 leads this result

  • ProgramBench

    GLM-5.319.0%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • FrontierSWE

    GLM-5.378.1%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • sweMarathon

    GLM-5.342.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • PostTrainBench (lab runs)

    GLM-5.339.8%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • VulcanBench v3

    GLM-5.378.3%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • OpenHarmony Bench v1.0

    GLM-5.360.8%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • FrontierSWE v2

    GLM-5.330.2%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5.380.5%
    Source
    Qwen3.6 Plus86.0%
    Source

    Qwen3.6 Plus leads this result

  • SWE-bench (Vals)

    GLM-5.395.4%
    Source
    Qwen3.6 Plus73.4%
    Source

    GLM-5.3 leads this result

  • Bug Hunt Bench

    GLM-5.319.0 fixes
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • CursorBench 4.0

    GLM-5.342.6%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • PostTrainBench

    GLM-5.334.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • OpenHarmony Bench v1.2

    GLM-5.360.65%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • SWE-bench Verified

    GLM-5.3—
    Qwen3.6 Plus78.8%
    Source

    Not directly comparable

  • SWE-bench Pro

    GLM-5.3—
    Qwen3.6 Plus56.6%
    Source

    Not directly comparable

  • SWE Multilingual

    GLM-5.3—
    Qwen3.6 Plus73.8%
    Source

    Not directly comparable

  • LiveCodeBench v6

    GLM-5.3—
    Qwen3.6 Plus87.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.3—
    Qwen3.6 Plus61.6%
    Source

    Not directly comparable

  • Vibe Code Bench

    GLM-5.3—
    Qwen3.6 Plus25.56%
    Source

    Not directly comparable

Reasoning

  • AI-Needle

    GLM-5.3—
    Qwen3.6 Plus68.3%
    Source

    Not directly comparable

  • LongBench v2

    GLM-5.3—
    Qwen3.6 Plus62%
    Source

    Not directly comparable

Multimodal

  • MMMU

    GLM-5.3—
    Qwen3.6 Plus86.0%
    Source

    Not directly comparable

  • MMMU-Pro

    GLM-5.3—
    Qwen3.6 Plus78.8%
    Source

    Not directly comparable

  • MathVision

    GLM-5.3—
    Qwen3.6 Plus88.0%
    Source

    Not directly comparable

  • VideoMMMU

    GLM-5.3—
    Qwen3.6 Plus84.0%
    Source

    Not directly comparable

  • ScreenSpot Pro

    GLM-5.3—
    Qwen3.6 Plus68.2%
    Source

    Not directly comparable

  • CharXiv

    GLM-5.3—
    Qwen3.6 Plus81.5%
    Source

    Not directly comparable

  • V*

    GLM-5.3—
    Qwen3.6 Plus96.9%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    GLM-5.388.1%
    Source
    Qwen3.6 Plus87.4%
    Source

    GLM-5.3 leads this result

  • MMLU-Pro (Vals)

    GLM-5.386.8%
    Source
    Qwen3.6 Plus87.7%
    Source

    Qwen3.6 Plus leads this result

  • GPQA

    GLM-5.3—
    Qwen3.6 Plus90.4%
    Source

    Not directly comparable

  • SuperGPQA

    GLM-5.3—
    Qwen3.6 Plus71.6%
    Source

    Not directly comparable

  • MMLU-Pro

    GLM-5.3—
    Qwen3.6 Plus88.5%
    Source

    Not directly comparable

  • MMLU-Redux

    GLM-5.3—
    Qwen3.6 Plus94.5%
    Source

    Not directly comparable

  • C-Eval

    GLM-5.3—
    Qwen3.6 Plus93.3%
    Source

    Not directly comparable

  • HLE

    GLM-5.3—
    Qwen3.6 Plus28.8%
    Source

    Not directly comparable

Multilingual

  • MMLU-ProX

    GLM-5.3—
    Qwen3.6 Plus84.7%
    Source

    Not directly comparable

  • NOVA-63

    GLM-5.3—
    Qwen3.6 Plus57.9%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    GLM-5.331.5%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • IFEval

    GLM-5.3—
    Qwen3.6 Plus94.3%
    Source

    Not directly comparable

  • IFBench

    GLM-5.3—
    Qwen3.6 Plus75.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GLM-5.368.772%
    Qwen3.6 Plus38.246%

    GLM-5.3 leads this result

  • FrontierMath v2 (Tier 4)

    GLM-5.329.268%
    Source
    Qwen3.6 Plus—

    Not directly comparable

  • AIME26

    GLM-5.3—
    Qwen3.6 Plus95.3%
    Source

    Not directly comparable

  • HMMT Feb 2025

    GLM-5.3—
    Qwen3.6 Plus96.7%
    Source

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.3—
    Qwen3.6 Plus94.6%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.3—
    Qwen3.6 Plus87.8%
    Source

    Not directly comparable

  • MMAnswerBench

    GLM-5.3—
    Qwen3.6 Plus83.8%
    Source

    Not directly comparable

81 public results · 7 shared

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Last updated November 13, 2026