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BenchLM
Anthropic logo
Model A
Claude Opus 5

Anthropic

80.66/100

Supported · Public rank #4

90% interval 78.3–83.0

Claude Opus 5 vs GLM-5V-Turbo

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Z.AI logo
Model B
GLM-5V-Turbo

Z.AI

60.56/100

Supported · Public rank #71

90% interval 49.6–71.5

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    Claude Opus 5

    Claude Opus 5 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GLM-5V-Turbo

    GLM-5V-Turbo 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

    GLM-5V-Turbo

    GLM-5V-Turbo 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

    GLM-5V-Turbo 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

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

    Confidence: rate-fallback

What is actually comparable

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

Shared results
0
Claude Opus 5 only
73
GLM-5V-Turbo only
2
Like-for-like categories
0 / 8

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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
Claude Opus 5
75.6
Supported · #3/183
GLM-5V-Turbo
55.1
Estimated · #43/183
Basis
BenchAlign lane · 16 vs 0 public rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 5
82.1
Supported · #3/181
GLM-5V-Turbo
53.3
Estimated · #67/181
Basis
BenchAlign lane · 19 vs 0 public rows
Reading
Directional only

Agentic

Not comparable
Claude Opus 5
77.4
Supported · #2/151
GLM-5V-Turbo
Not ranked
Basis
BenchAlign lane · 19 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 5
75.6
#13/22
GLM-5V-Turbo
68.3
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 5
Not ranked
GLM-5V-Turbo
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 5
Not ranked
GLM-5V-Turbo
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 5
88.7
#2/48
GLM-5V-Turbo
67.2
Unranked · 1 rankable row
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 5
Not ranked
GLM-5V-Turbo
73.7
#60/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Claude Opus 5
$0.0175
Fits in one request
GLM-5V-Turbo
$0.0032
Fits in one request

GLM-5V-Turbo has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5
$0.325
Fits in one request
GLM-5V-Turbo
$0.072
Fits in one request

GLM-5V-Turbo has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Claude Opus 5
$0.45
Fits in one request
GLM-5V-Turbo
$0.304
Does not fit in one request
Cached input priced at the published list-input rate

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

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-5V-Turbo

200K

Cached-input rate

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

Claude Opus 5

$0.5 per 1M cached input tokens

Claude API pricing

GLM-5V-Turbo

Not published

Reasoning profile

Claude Opus 5

Reasoning

GLM-5V-Turbo

Non-Reasoning

Weight access

Claude Opus 5

Proprietary

GLM-5V-Turbo

Proprietary

License

Claude Opus 5

Proprietary

GLM-5V-Turbo

Proprietary

Release date

Claude Opus 5

2026-07-24

GLM-5V-Turbo

2026-03-01

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.325 vs $0.072. Cache-heavy agent loop: $0.45 vs $0.304.
Context tradeoff
Claude Opus 5 has the larger documented window (1M).

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

Benchmark evidence

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

Browse raw public benchmark evidence75 rows

Agentic

  • Terminal-Bench 3.0

    Claude Opus 542.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • BrowseComp

    Claude Opus 590.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HLE w/ tools

    Claude Opus 564.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • DeepSearchQA

    Claude Opus 595.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • DRACO

    Claude Opus 588.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • BrowseComp (10-agent, prerelease)

    Claude Opus 593.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 570.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • MCP Atlas

    Claude Opus 585.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • MCP-Atlas claim coverage

    Claude Opus 589.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • LAB all-pass (Anthropic harness)

    Claude Opus 523.58%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • LAB criterion-pass (Anthropic harness)

    Claude Opus 593.74%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • LAB all-pass (Harvey held-out)

    Claude Opus 511.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • LAB criterion-pass (Harvey held-out)

    Claude Opus 594.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Toolathlon-Verified

    Claude Opus 580.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Opus 587.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Opus 573.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Opus 523.5 turns
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • AutomationBench

    Claude Opus 526.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Opus 584.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Claw-Eval

    Claude Opus 5—
    GLM-5V-Turbo53.8%
    Source

    Not directly comparable

  • Gert Labs

    Claude Opus 5—
    GLM-5V-Turbo30.76%
    Source

    Not directly comparable

Coding

  • Bug Hunt Bench

    Claude Opus 527 fixes
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE-bench Verified

    Claude Opus 596%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 579.2%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE Multilingual

    Claude Opus 589.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE Multimodal

    Claude Opus 559.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • deepSwe

    Claude Opus 568.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 553.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • FrontierCode 1.1 Extended

    Claude Opus 563.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • FrontierSWE v2

    Claude Opus 552.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ProgramBench (episode 1)

    Claude Opus 583.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ProgramBench

    Claude Opus 593.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • cursorBench32

    Claude Opus 570.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • VulcanBench v3

    Claude Opus 587.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • VulcanBench CII v1

    Claude Opus 596.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Opus 589.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Opus 597.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Claude Opus 597.50%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 590.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ARC-AGI-3

    Claude Opus 530.2%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Knowledge

  • HLE

    Claude Opus 564.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HLE w/o tools

    Claude Opus 556.3%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HLE-Verified

    Claude Opus 554.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • LABBench2

    Claude Opus 584.2%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HealthBench (raw)

    Claude Opus 567.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HealthBench (length-adjusted)

    Claude Opus 557.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HealthBench Professional

    Claude Opus 559.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • HealthBench Professional (raw)

    Claude Opus 573.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • BioMysteryBench (human-solvable)

    Claude Opus 590.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • BioMysteryBench (human-difficult)

    Claude Opus 549.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SpatialBench Verified

    Claude Opus 572.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • SingleCellBench

    Claude Opus 560.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ProteinGym Hard

    Claude Opus 547.7%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Protein Design

    Claude Opus 542.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Organic chemistry V2

    Claude Opus 561.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Opus 561.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Protocols (understanding)

    Claude Opus 578.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Opus 593.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Opus 591.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Math

  • IMO 2026

    Claude Opus 542/42
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • RiemannBench (no tools)

    Claude Opus 560.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • RiemannBench (tools)

    Claude Opus 579.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ArXivMath Jun. 2026 (no tools)

    Claude Opus 590.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • ArXivMath Jun. 2026 (tools)

    Claude Opus 591.3%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Multilingual

  • GMMLU

    Claude Opus 592.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • MILU

    Claude Opus 592.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • INCLUDE

    Claude Opus 589.8%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Multimodal

  • Chartography (no tools)

    Claude Opus 529.6%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • Chartography (tools)

    Claude Opus 583.0%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • BenchCAD Vision2Code (no tools)

    Claude Opus 50.366
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    Claude Opus 50.821
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • GDP.pdf (no tools)

    Claude Opus 583.4%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • GDP.pdf (tools)

    Claude Opus 585.5%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • OfficeQA

    Claude Opus 578.1%
    Source
    GLM-5V-Turbo—

    Not directly comparable

  • OfficeQA Pro

    Claude Opus 566.9%
    Source
    GLM-5V-Turbo—

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 5 or GLM-5V-Turbo?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Opus 5 or GLM-5V-Turbo?

Claude Opus 5 scores higher for coding on the public lane, 75.6 to 55.1. GLM-5V-Turbo 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, Claude Opus 5 or GLM-5V-Turbo?

GLM-5V-Turbo is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Opus 5 or GLM-5V-Turbo?

For the stated presets, chat costs $0.0175 on Claude Opus 5 and $0.0032 on GLM-5V-Turbo; repository review costs $0.325 and $0.072; the cache-heavy agent loop costs $0.45 and $0.304. GLM-5V-Turbo does not fit this workload in one request. GLM-5V-Turbo has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 5 or GLM-5V-Turbo?

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

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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