Model A

Anthropic

80.66/100

Supported · Public rank #4

90% interval 78.3–83.0

Claude Opus 5 vs GLM-5.1

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

Model B

GLM-5.1

Z.AI

64.41/100

Supported · Public rank #47

90% interval 55.6–73.3

Decision reading

Claude Opus 5 has the higher public score, 80.66 versus 64.41, and the 90% score intervals do not overlap.

9 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.

Coding work
Code generation, repair, and software-engineering tasks
Claude Opus 5
Claude Opus 5 leads on the public coding lane, 75.6 to 56.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
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-5.1
GLM-5.1 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-5.1
GLM-5.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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-5.1 does not fit this workload in one request. GLM-5.1 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.

64 Claude Opus 5 only

9 Shared

17 GLM-5.1 only

Shared results: 9
Claude Opus 5 only: 64
GLM-5.1 only: 17
Like-for-like categories: 2 / 8

1 category rests 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

Like-for-like

Claude Opus 5: 75.6; Supported · #3/183
GLM-5.1: 56.8; Supported · #39/183
Basis: BenchAlign lane · 16 vs 7 public rows
Reading: Claude Opus 5 leads

Knowledge

Like-for-like

Claude Opus 5: 82.1; Supported · #3/181
GLM-5.1: 55.4; Supported · #59/181
Basis: BenchAlign lane · 19 vs 4 public rows
Reading: Claude Opus 5 leads

Agentic

Directional only

Claude Opus 5: 77.4; Supported · #2/151
GLM-5.1: 50.1; Estimated · #63/151
Basis: BenchAlign lane · 19 vs 9 public rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 5: 75.6; #13/22
GLM-5.1: 69.9; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 5: Not ranked
GLM-5.1: 64.1; #3/7
Basis: Provisional lane · 0 vs 4 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 5: Not ranked
GLM-5.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 5: 88.7; #2/48
GLM-5.1: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 5: Not ranked
GLM-5.1: 93.5; #5/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 5 and GLM-5.1
Category	Claude Opus 5	GLM-5.1	Basis	Reading
Coding	75.6Supported · #3/183	56.8Supported · #39/183	Like-for-likeBenchAlign lane · 16 vs 7 public rows	Claude Opus 5 leads
Knowledge	82.1Supported · #3/181	55.4Supported · #59/181	Like-for-likeBenchAlign lane · 19 vs 4 public rows	Claude Opus 5 leads
Agentic	77.4Supported · #2/151	50.1Estimated · #63/151	Directional onlyBenchAlign lane · 19 vs 9 public rows	Directional only
Reasoning	75.6#13/22	69.9Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Math	Not ranked	64.1#3/7	Not comparableProvisional lane · 0 vs 4 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	88.7#2/48	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	93.5#5/120	Not comparableProvisional lane · 0 vs 0 weighted rows	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.

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.

BrowseComp
Agentic
Claude Opus 5: 90.8%GLM-5.1: 68%Normalized gap 22.8Claude Opus 5 source GLM-5.1 source
SWE-bench Pro
Coding
Claude Opus 5: 79.2%GLM-5.1: 58.4%Normalized gap 20.8Claude Opus 5 source GLM-5.1 source
HLE
Knowledge
Claude Opus 5: 64.7%GLM-5.1: 52.3%Normalized gap 12.4Claude Opus 5 source GLM-5.1 source
LiveCodeBench (Vals)
Coding
Claude Opus 5: 89.0%GLM-5.1: 81.4%Normalized gap 7.6Claude Opus 5 source GLM-5.1 source
MMLU-Pro (Vals)
Knowledge
Claude Opus 5: 91.6%GLM-5.1: 86.9%Normalized gap 4.7Claude Opus 5 source GLM-5.1 source

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-5.1: $0.0036; Fits in one request

GLM-5.1 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-5.1: $0.0832; Fits in one request

GLM-5.1 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-5.1: $0.352; Does not fit in one request; Cached input priced at the published list-input rate

GLM-5.1 does not fit this workload in one request. GLM-5.1 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.

SpecificationClaude Opus 5GLM-5.1

Context window

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

Claude Opus 5

Anthropic model overview

GLM-5.1

203K

API model ID

Claude Opus 5

claude-opus-5

Anthropic model overview

GLM-5.1

Not sourced

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-5.1

Not published

Documented inputs

Claude Opus 5

text, image

Anthropic model overview

GLM-5.1

Not sourced

Documented outputs

Claude Opus 5

text

Anthropic model overview

GLM-5.1

Not sourced

Provider availability

Claude Opus 5

Generally Available · Claude API

Anthropic model overview

GLM-5.1

Not sourced

Reasoning profile

Claude Opus 5

Reasoning

GLM-5.1

Reasoning

Weight access

Claude Opus 5

Proprietary

GLM-5.1

Open Weight

License

Claude Opus 5

Proprietary

GLM-5.1

Open Weight

Release date

Claude Opus 5

2026-07-24

GLM-5.1

2026-04-07

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: Claude Opus 5 has the higher public score, 80.66 versus 64.41, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.325 vs $0.0832. Cache-heavy agent loop: $0.45 vs $0.352.
Context tradeoff: Claude Opus 5 has the larger documented window (1M).

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

Self-host vs API cost

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

Claude Opus 5

API / mo$22,500

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

GLM-5.1

API / mo$4,350

Self-host / mo$18,221

Break-even264M/day

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 evidence90 rows

Agentic

Terminal-Bench 3.0
Claude Opus 542.7%
Source
GLM-5.1—
Not directly comparable
BrowseComp
Claude Opus 590.8%
Source
GLM-5.168%
Source
Claude Opus 5 leads this result
HLE w/ tools
Claude Opus 564.7%
Source
GLM-5.1—
Not directly comparable
DeepSearchQA
Claude Opus 595.0%
Source
GLM-5.1—
Not directly comparable
DRACO
Claude Opus 588.6%
Source
GLM-5.1—
Not directly comparable
BrowseComp (10-agent, prerelease)
Claude Opus 593.6%
Source
GLM-5.1—
Not directly comparable
OSWorld 2.0
Claude Opus 570.6%
Source
GLM-5.1—
Not directly comparable
MCP Atlas
Claude Opus 585.8%
Source
GLM-5.171.8%
Source
Claude Opus 5 leads this result
MCP-Atlas claim coverage
Claude Opus 589.1%
Source
GLM-5.1—
Not directly comparable
LAB all-pass (Anthropic harness)
Claude Opus 523.58%
Source
GLM-5.1—
Not directly comparable
LAB criterion-pass (Anthropic harness)
Claude Opus 593.74%
Source
GLM-5.1—
Not directly comparable
LAB all-pass (Harvey held-out)
Claude Opus 511.7%
Source
GLM-5.1—
Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Opus 594.1%
Source
GLM-5.1—
Not directly comparable
Toolathlon-Verified
Claude Opus 580.6%
Source
GLM-5.1—
Not directly comparable
Toolathlon Verified Pass@3
Claude Opus 587.0%
Source
GLM-5.1—
Not directly comparable
Toolathlon Verified Pass³
Claude Opus 573.1%
Source
GLM-5.1—
Not directly comparable
Toolathlon Verified avg. turns
Claude Opus 523.5 turns
Source
GLM-5.1—
Not directly comparable
AutomationBench
Claude Opus 526.0%
Source
GLM-5.1—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 584.6%
Source
GLM-5.156.9%
Source
Claude Opus 5 leads this result
Terminal-Bench 2.0
Claude Opus 5—
GLM-5.163.5%
Source
Not directly comparable
τ³-bench results
Claude Opus 5—
GLM-5.170.6%
Source
Not directly comparable
CyberGym
Claude Opus 5—
GLM-5.168.7%
Source
Not directly comparable
Claw-Eval
Claude Opus 5—
GLM-5.162.3%
Source
Not directly comparable
Gert Labs
Claude Opus 5—
GLM-5.160.11%
Source
Not directly comparable
ResearchClawBench
Claude Opus 5—
GLM-5.118.2%
Source
Not directly comparable

Coding

Bug Hunt Bench
Claude Opus 527 fixes
Source
GLM-5.1—
Not directly comparable
SWE-bench Verified
Claude Opus 596%
Source
GLM-5.1—
Not directly comparable
SWE-bench Pro
Claude Opus 579.2%
Source
GLM-5.158.4%
Source
Claude Opus 5 leads this result
SWE Multilingual
Claude Opus 589.5%
Source
GLM-5.1—
Not directly comparable
SWE Multimodal
Claude Opus 559.4%
Source
GLM-5.1—
Not directly comparable
deepSwe
Claude Opus 568.8%
Source
GLM-5.1—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 553.4%
Source
GLM-5.1—
Not directly comparable
FrontierCode 1.1 Extended
Claude Opus 563.6%
Source
GLM-5.1—
Not directly comparable
FrontierSWE v2
Claude Opus 552.0%
Source
GLM-5.1—
Not directly comparable
ProgramBench (episode 1)
Claude Opus 583.0%
Source
GLM-5.1—
Not directly comparable
ProgramBench
Claude Opus 593.0%
Source
GLM-5.1—
Not directly comparable
cursorBench32
Claude Opus 570.0%
Source
GLM-5.1—
Not directly comparable
VulcanBench v3
Claude Opus 587.0%
Source
GLM-5.1—
Not directly comparable
VulcanBench CII v1
Claude Opus 596.4%
Source
GLM-5.1—
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 589.0%
Source
GLM-5.181.4%
Source
Claude Opus 5 leads this result
SWE-bench (Vals)
Claude Opus 597.0%
Source
GLM-5.176.4%
Source
Claude Opus 5 leads this result
NL2Repo
Claude Opus 5—
GLM-5.142.7%
Source
Not directly comparable
SWE-Rebench
Claude Opus 5—
GLM-5.162.7%
Source
Not directly comparable
Vibe Code Bench
Claude Opus 5—
GLM-5.131.46%
Source
Not directly comparable
OpenHarmony Bench
Claude Opus 5—
GLM-5.152.3%
Source
Not directly comparable

Reasoning

ARC-AGI-1
Claude Opus 597.50%
Source
GLM-5.1—
Not directly comparable
ARC-AGI-2
Claude Opus 590.4%
Source
GLM-5.1—
Not directly comparable
ARC-AGI-3
Claude Opus 530.2%
Source
GLM-5.1—
Not directly comparable

Knowledge

HLE
Claude Opus 564.7%
Source
GLM-5.152.3%
Source
Claude Opus 5 leads this result
HLE w/o tools
Claude Opus 556.3%
Source
GLM-5.1—
Not directly comparable
HLE-Verified
Claude Opus 554.4%
Source
GLM-5.1—
Not directly comparable
LABBench2
Claude Opus 584.2%
Source
GLM-5.1—
Not directly comparable
HealthBench (raw)
Claude Opus 567.1%
Source
GLM-5.1—
Not directly comparable
HealthBench (length-adjusted)
Claude Opus 557.8%
Source
GLM-5.1—
Not directly comparable
HealthBench Professional
Claude Opus 559.8%
Source
GLM-5.1—
Not directly comparable
HealthBench Professional (raw)
Claude Opus 573.4%
Source
GLM-5.1—
Not directly comparable
BioMysteryBench (human-solvable)
Claude Opus 590.1%
Source
GLM-5.1—
Not directly comparable
BioMysteryBench (human-difficult)
Claude Opus 549.4%
Source
GLM-5.1—
Not directly comparable
SpatialBench Verified
Claude Opus 572.5%
Source
GLM-5.1—
Not directly comparable
SingleCellBench
Claude Opus 560.6%
Source
GLM-5.1—
Not directly comparable
ProteinGym Hard
Claude Opus 547.7%
Source
GLM-5.1—
Not directly comparable
Protein Design
Claude Opus 542.5%
Source
GLM-5.1—
Not directly comparable
Organic chemistry V2
Claude Opus 561.6%
Source
GLM-5.1—
Not directly comparable
Protocols (troubleshooting)
Claude Opus 561.1%
Source
GLM-5.1—
Not directly comparable
Protocols (understanding)
Claude Opus 578.4%
Source
GLM-5.1—
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 593.4%
Source
GLM-5.184.5%
Source
Claude Opus 5 leads this result
MMLU-Pro (Vals)
Claude Opus 591.6%
Source
GLM-5.186.9%
Source
Claude Opus 5 leads this result
GPQA-D
Claude Opus 5—
GLM-5.186.2%
Source
Not directly comparable

Math

IMO 2026
Claude Opus 542/42
Source
GLM-5.1—
Not directly comparable
RiemannBench (no tools)
Claude Opus 560.0%
Source
GLM-5.1—
Not directly comparable
RiemannBench (tools)
Claude Opus 579.0%
Source
GLM-5.1—
Not directly comparable
ArXivMath Jun. 2026 (no tools)
Claude Opus 590.8%
Source
GLM-5.1—
Not directly comparable
ArXivMath Jun. 2026 (tools)
Claude Opus 591.3%
Source
GLM-5.1—
Not directly comparable
AIME26
Claude Opus 5—
GLM-5.195.3%
Source
Not directly comparable
HMMT Nov 2025
Claude Opus 5—
GLM-5.194.0%
Source
Not directly comparable
HMMT Feb 2026
Claude Opus 5—
GLM-5.182.6%
Source
Not directly comparable
MMAnswerBench
Claude Opus 5—
GLM-5.183.8%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 5—
GLM-5.133.448%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 5—
GLM-5.112.500%
Source
Not directly comparable

Multilingual

GMMLU
Claude Opus 592.5%
Source
GLM-5.1—
Not directly comparable
MILU
Claude Opus 592.1%
Source
GLM-5.1—
Not directly comparable
INCLUDE
Claude Opus 589.8%
Source
GLM-5.1—
Not directly comparable

Multimodal

Chartography (no tools)
Claude Opus 529.6%
Source
GLM-5.1—
Not directly comparable
Chartography (tools)
Claude Opus 583.0%
Source
GLM-5.1—
Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Opus 50.366
Source
GLM-5.1—
Not directly comparable
BenchCAD Vision2Code (tools)
Claude Opus 50.821
Source
GLM-5.1—
Not directly comparable
GDP.pdf (no tools)
Claude Opus 583.4%
Source
GLM-5.1—
Not directly comparable
GDP.pdf (tools)
Claude Opus 585.5%
Source
GLM-5.1—
Not directly comparable
OfficeQA
Claude Opus 578.1%
Source
GLM-5.1—
Not directly comparable
OfficeQA Pro
Claude Opus 566.9%
Source
GLM-5.1—
Not directly comparable

Frequently asked questions

Which is better, Claude Opus 5 or GLM-5.1?

Claude Opus 5 has the higher public score, 80.66 versus 64.41, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Opus 5 or GLM-5.1?

Claude Opus 5 leads the public coding lane, 75.6 to 56.8, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Claude Opus 5 or GLM-5.1?

Claude Opus 5 scores higher for agentic tasks on the public lane, 77.4 to 50.1. GLM-5.1 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, Claude Opus 5 or GLM-5.1?

For the stated presets, chat costs $0.0175 on Claude Opus 5 and $0.0036 on GLM-5.1; repository review costs $0.325 and $0.0832; the cache-heavy agent loop costs $0.45 and $0.352. GLM-5.1 does not fit this workload in one request. GLM-5.1 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-5.1?

Claude Opus 5 has the larger documented context window: 1M, compared with 203K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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Claude Opus 5 vs GLM-5.1

Which one for your work

Coding work

Long documents

Chat turn cost

Repository review cost

Agentic work

Cache-heavy agent loop cost

What is actually comparable

Category results, on a stated basis

Coding

Knowledge

Agentic

Reasoning

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Frequently asked questions

Which is better, Claude Opus 5 or GLM-5.1?

Which is better for coding, Claude Opus 5 or GLM-5.1?

Which is better for agentic tasks, Claude Opus 5 or GLM-5.1?

Which costs less, Claude Opus 5 or GLM-5.1?

Which has the larger context window, Claude Opus 5 or GLM-5.1?

Related comparisons

Watch Claude Opus 5 vs GLM-5.1