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BenchLM

GLM-5 vs Qwen3.5 Plus

Updated September 28, 2026. Rank says GLM-5 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 has the higher public score estimate, 54.39 versus 48.92, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 2 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

Z.AI

54.39/100

Supported · Public rank #66

90% interval 44.7–64.1

Model B
Alibaba logo
Qwen3.5 Plus

Alibaba

48.92/100

Estimated · Public rank #86

90% interval 37.4–60.4

Shared results
2
GLM-5 only
34
Qwen3.5 Plus only
2
Like-for-like categories
0 / 8
Supported: GLM-5 · Estimated: Qwen3.5 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.

  • Long documents

    Prompts that approach the documented context limit

    Qwen3.5 Plus

    Qwen3.5 Plus has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Qwen3.5 Plus

    Qwen3.5 Plus has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Qwen3.5 Plus

    Qwen3.5 Plus has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

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

    Confidence: limited

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

    Confidence: rate-fallback

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.

38.9GLM-5—Qwen3.5 Plus

Not comparable · BenchAlign v5.7

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 leaderboardShortlist models for this job
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.

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 4.6
    GLM-5:16.434%
    Qwen3.5 Plus:21.034%
    Shared source

  • FrontierMath v2 (Tier 4)Math

    Normalized gap 0.0
    GLM-5:2.100%
    Qwen3.5 Plus:2.083%
    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 v5.7 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.

Agentic

Not comparable
GLM-5
37.4
Estimated · #58/117
Qwen3.5 Plus
Not ranked
Basis
BenchAlign v5.7 lane · 11 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
GLM-5
38.9
Estimated · #63/142
Qwen3.5 Plus
Not ranked
Basis
BenchAlign v5.7 lane · 6 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
GLM-5
53.3
Unranked · 4 rankable rows
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5
Not ranked
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GLM-5
47.2
Estimated · #73/168
Qwen3.5 Plus
Not ranked
Basis
BenchAlign v5.7 lane · 6 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5
48.7
#6/12
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5
87.2
#32/124
Qwen3.5 Plus
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5
56.5
#7/7
Qwen3.5 Plus
39.3
Unranked · 2 rankable rows
Basis
Provisional lane · 4 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.

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
$0.0026
Fits in one request
Qwen3.5 Plus
$0.0016
Fits in one request

Qwen3.5 Plus has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5
$0.0596
Fits in one request
Qwen3.5 Plus
$0.0272
Fits in one request

Qwen3.5 Plus has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GLM-5
$0.252
Does not fit in one request
Cached input priced at the published list-input rate
Qwen3.5 Plus
$0.112
Fits in one request
Cached input priced at the published list-input rate

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

200K

Qwen3.5 Plus

1M

API model ID

GLM-5

Not sourced

Qwen3.5 Plus

Not sourced

Cached-input rate

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

GLM-5

Not published

Qwen3.5 Plus

Not published

Documented inputs

GLM-5

Not sourced

Qwen3.5 Plus

Not sourced

Documented outputs

GLM-5

Not sourced

Qwen3.5 Plus

Not sourced

Provider availability

GLM-5

Not sourced

Qwen3.5 Plus

Not sourced

Reasoning profile

GLM-5

Non-Reasoning

Qwen3.5 Plus

Reasoning

Weight access

GLM-5

Open Weight

Qwen3.5 Plus

Proprietary

License

GLM-5

Open Weight

Qwen3.5 Plus

Proprietary

Release date

GLM-5

2026-03-01

Qwen3.5 Plus

2026-03-04

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 has the higher public score estimate, 54.39 versus 48.92, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0596 vs $0.0272. Cache-heavy agent loop: $0.252 vs $0.112.
Context tradeoff
Qwen3.5 Plus has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-5 or Qwen3.5 Plus?

GLM-5 has the higher public score estimate, 54.39 versus 48.92, 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 or Qwen3.5 Plus?

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

Which is better for agentic tasks, GLM-5 or Qwen3.5 Plus?

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

Which costs less, GLM-5 or Qwen3.5 Plus?

For the stated presets, chat costs $0.0026 on GLM-5 and $0.0016 on Qwen3.5 Plus; repository review costs $0.0596 and $0.0272; the cache-heavy agent loop costs $0.252 and $0.112. GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Plus has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5 or Qwen3.5 Plus?

Qwen3.5 Plus has the larger documented context window: 1M, compared with 200K.

Benchmark evidence

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

Browse raw public benchmark evidence38 rows

Agentic

  • Terminal-Bench 2.0

    GLM-556.2%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • Claw-Eval

    GLM-557.7%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • QwenClawBench

    GLM-554.1%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • τ³-bench results

    GLM-565.6%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • DeepPlanning

    GLM-514.6%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • Toolathlon

    GLM-538%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • MCP Atlas

    GLM-531.1%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • MCP-Tasks

    GLM-560.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • WideResearch

    GLM-569.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • CyberGym

    GLM-543.2%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • Gert Labs

    GLM-550.99%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • JobBench

    GLM-5—
    Qwen3.5 Plus18.5%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GLM-577.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • SWE-bench Verified*

    GLM-572.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • SWE-bench Pro

    GLM-555.1%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • SWE Multilingual

    GLM-573.3%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • SWE-Rebench

    GLM-562.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • React Native Evals

    GLM-574.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • Vibe Code Bench

    GLM-5—
    Qwen3.5 Plus15.74%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    GLM-560.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • AI-Needle

    GLM-563.3%
    Source
    Qwen3.5 Plus—

    Not directly comparable

Knowledge

  • GPQA

    GLM-586%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • GPQA-D

    GLM-586.0%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • SuperGPQA

    GLM-566.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • MMLU-Pro

    GLM-585.7%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • MMLU-Pro (Arcee)

    GLM-585.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • HLE

    GLM-550.4%
    Source
    Qwen3.5 Plus—

    Not directly comparable

Multilingual

  • MMLU-ProX

    GLM-583.1%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • NOVA-63

    GLM-555.1%
    Source
    Qwen3.5 Plus—

    Not directly comparable

Instruction following

  • IFEval

    GLM-592.6%
    Source
    Qwen3.5 Plus—

    Not directly comparable

Math

  • AIME26

    GLM-595.8%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • AIME25 (Arcee)

    GLM-593.3%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • HMMT Feb 2025

    GLM-597.5%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • HMMT Nov 2025

    GLM-596.9%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • HMMT Feb 2026

    GLM-586.4%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • MMAnswerBench

    GLM-582.5%
    Source
    Qwen3.5 Plus—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GLM-516.434%
    Qwen3.5 Plus21.034%

    Qwen3.5 Plus leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GLM-52.100%
    Qwen3.5 Plus2.083%

    GLM-5 leads this result

38 public results · 2 shared

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Last updated September 28, 2026