GLM-5.1 vs o4-mini (high)

Updated September 23, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality 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

GLM-5.1

Z.AI

57.71/100

Supported · Public rank #48

90% interval 47.6–67.8

Model B

o4-mini (high)

OpenAI

—

Evidence status unavailable

90% interval unavailable

Shared results: 2
GLM-5.1 only: 24
o4-mini (high) only: 0
Like-for-like categories: 0 / 8

Supported: GLM-5.1How 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
GLM-5.1
GLM-5.1 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
O4-mini (high) 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
O4-mini (high) is not ranked on the public lane for agentic, so no winner is named for agentic.
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
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. o4-mini (high) 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. o4-mini (high) has no comparable published API token rate.
Confidence: rate-fallback
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.

51.5GLM-5.1—o4-mini (high)

Not comparable · BenchAlign v5.6

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 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.

FrontierMath v2 (Tiers 1-3)Math
Normalized gap 8.6
GLM-5.1:33.448%
o4-mini (high):24.828%
Shared source
FrontierMath v2 (Tier 4)Math
Normalized gap 6.3
GLM-5.1:12.500%
o4-mini (high):6.250%
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 v5.6 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-5.1: 42.1; Estimated · #43/105
o4-mini (high): Not ranked
Basis: BenchAlign v5.6 lane · 9 vs 0 public rows
Reading: Not comparable

Coding

Not comparable

GLM-5.1: 51.5; Supported · #36/135
o4-mini (high): Not ranked
Basis: BenchAlign v5.6 lane · 7 vs 0 public rows
Reading: Not comparable

Reasoning

Not comparable

GLM-5.1: 71.7; Unranked · 2 rankable rows
o4-mini (high): Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.1: Not ranked
o4-mini (high): Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

GLM-5.1: 50.2; Supported · #54/160
o4-mini (high): Not ranked
Basis: BenchAlign v5.6 lane · 4 vs 0 public rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.1: Not ranked
o4-mini (high): Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.1: 92.4; #4/124
o4-mini (high): Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.1: 63.8; #3/7
o4-mini (high): 43.0; Unranked · 2 rankable rows
Basis: Provisional lane · 4 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.1 and o4-mini (high)
Category	GLM-5.1	o4-mini (high)	Basis	Reading
Agentic	42.1Estimated · #43/105	Not ranked	Not comparableBenchAlign v5.6 lane · 9 vs 0 public rows	Not comparable
Coding	51.5Supported · #36/135	Not ranked	Not comparableBenchAlign v5.6 lane · 7 vs 0 public rows	Not comparable
Reasoning	71.7Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	50.2Supported · #54/160	Not ranked	Not comparableBenchAlign v5.6 lane · 4 vs 0 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	92.4#4/124	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	63.8#3/7	43.0Unranked · 2 rankable rows	Not comparableProvisional lane · 4 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) 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-5.1: $0.0036; Fits in one request
o4-mini (high): API rate not published; Fits in one request

o4-mini (high) has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.1: $0.0832; Fits in one request
o4-mini (high): API rate not published; Fits in one request

o4-mini (high) has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-5.1: $0.352; Does not fit in one request; Cached input priced at the published list-input rate
o4-mini (high): API rate not published; Does not fit in one request; Cached-input rate unavailable

GLM-5.1 does not fit this workload in one request. o4-mini (high) 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. o4-mini (high) 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.

SpecificationGLM-5.1o4-mini (high)

Context window

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

GLM-5.1

203K

o4-mini (high)

200K

API model ID

GLM-5.1

Not sourced

o4-mini (high)

Not sourced

Cached-input rate

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

GLM-5.1

Not published

o4-mini (high)

No comparable hosted API rate

Documented inputs

GLM-5.1

Not sourced

o4-mini (high)

Not sourced

Documented outputs

GLM-5.1

Not sourced

o4-mini (high)

Not sourced

Provider availability

GLM-5.1

Not sourced

o4-mini (high)

Not sourced

Reasoning profile

GLM-5.1

Reasoning

o4-mini (high)

Reasoning

Weight access

GLM-5.1

Open Weight

o4-mini (high)

Proprietary

License

GLM-5.1

Open Weight

o4-mini (high)

Proprietary

Release date

GLM-5.1

2026-04-07

o4-mini (high)

2025-04-16

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: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GLM-5.1 has the larger documented window (203K).

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

Questions

Which is better, GLM-5.1 or o4-mini (high)?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GLM-5.1 or o4-mini (high)?

O4-mini (high) is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GLM-5.1 or o4-mini (high)?

O4-mini (high) is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-5.1 or o4-mini (high)?

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-5.1 or o4-mini (high)?

GLM-5.1 has the larger documented context window: 203K, compared with 200K.

Self-host vs API cost

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

GLM-5.1

API / mo$4,350

Self-host / mo$18,221

Break-even264M/day

o4-mini (high)

API / mo$0

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 evidence26 rows

Agentic

Terminal-Bench 2.0
GLM-5.163.5%
Source
o4-mini (high)—
Not directly comparable
BrowseComp
GLM-5.168%
Source
o4-mini (high)—
Not directly comparable
τ³-bench results
GLM-5.170.6%
Source
o4-mini (high)—
Not directly comparable
MCP Atlas
GLM-5.171.8%
Source
o4-mini (high)—
Not directly comparable
CyberGym
GLM-5.168.7%
Source
o4-mini (high)—
Not directly comparable
Claw-Eval
GLM-5.162.3%
Source
o4-mini (high)—
Not directly comparable
Gert Labs
GLM-5.160.11%
Source
o4-mini (high)—
Not directly comparable
ResearchClawBench
GLM-5.118.2%
Source
o4-mini (high)—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%
Source
o4-mini (high)—
Not directly comparable

Coding

SWE-bench Pro
GLM-5.158.4%
Source
o4-mini (high)—
Not directly comparable
NL2Repo
GLM-5.142.7%
Source
o4-mini (high)—
Not directly comparable
SWE-Rebench
GLM-5.162.7%
Source
o4-mini (high)—
Not directly comparable
Vibe Code Bench
GLM-5.131.46%
Source
o4-mini (high)—
Not directly comparable
OpenHarmony Bench
GLM-5.152.3%
Source
o4-mini (high)—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%
Source
o4-mini (high)—
Not directly comparable
SWE-bench (Vals)
GLM-5.176.4%
Source
o4-mini (high)—
Not directly comparable

Knowledge

GPQA-D
GLM-5.186.2%
Source
o4-mini (high)—
Not directly comparable
HLE
GLM-5.152.3%
Source
o4-mini (high)—
Not directly comparable
GPQA Diamond (Vals)
GLM-5.184.5%
Source
o4-mini (high)—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.186.9%
Source
o4-mini (high)—
Not directly comparable

Math

AIME26
GLM-5.195.3%
Source
o4-mini (high)—
Not directly comparable
HMMT Nov 2025
GLM-5.194.0%
Source
o4-mini (high)—
Not directly comparable
HMMT Feb 2026
GLM-5.182.6%
Source
o4-mini (high)—
Not directly comparable
MMAnswerBench
GLM-5.183.8%
Source
o4-mini (high)—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared source
GLM-5.133.448%
o4-mini (high)24.828%
GLM-5.1 leads this result
FrontierMath v2 (Tier 4)
Shared source
GLM-5.112.500%
o4-mini (high)6.250%
GLM-5.1 leads this result

26 public results · 2 shared

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Last updated September 23, 2026

GLM-5.1 vs o4-mini (high)

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

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

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, GLM-5.1 or o4-mini (high)?

Which is better for coding, GLM-5.1 or o4-mini (high)?

Which is better for agentic tasks, GLM-5.1 or o4-mini (high)?

Which costs less, GLM-5.1 or o4-mini (high)?

Which has the larger context window, GLM-5.1 or o4-mini (high)?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Related comparisons

Watch GLM-5.1 vs o4-mini (high)