GLM-5.3 vs Solar Pro 4

Updated November 12, 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-5.3

Z.AI

60.98/100

Supported · Public rank #48

90% interval 51.2–70.8

Model B

Solar Pro 4

Upstage

—

Evidence status unavailable

90% interval unavailable

API price per 1M tokens

GLM-5.3: No comparable API price

Solar Pro 4: $0.30 input / $1.20 output

Context window

GLM-5.3: 1M

Solar Pro 4: 512K

Released

GLM-5.3: August 14, 2026

Solar Pro 4: August 11, 2026

Shared results: 2
GLM-5.3 only: 36
Solar Pro 4 only: 8
Like-for-like categories: 0 / 8

Supported: GLM-5.3How 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-5.3
GLM-5.3 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
Solar Pro 4 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
Solar Pro 4 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
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.

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.

53.7GLM-5.3—Solar Pro 4

Not comparable · BenchAlign v5.8

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

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.

What do you need a model for?

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

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.

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

Not comparable

GLM-5.3: 66.7; Supported · #14/135
Solar Pro 4: Not ranked
Basis: BenchAlign v5.8 lane · 17 vs 4 public rows
Reading: Not comparable

Coding

Not comparable

GLM-5.3: 53.7; Supported · #33/162
Solar Pro 4: Not ranked
Basis: BenchAlign v5.8 lane · 16 vs 3 public rows
Reading: Not comparable

Reasoning

Not comparable

GLM-5.3: 57.3; Supported · #36/160
Solar Pro 4: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 0 public rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.3: Not ranked
Solar Pro 4: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 0 public rows
Reading: Not comparable

Knowledge

Not comparable

GLM-5.3: 59.9; Supported · #48/180
Solar Pro 4: Not ranked
Basis: BenchAlign v5.8 lane · 2 vs 2 public rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3: Not ranked
Solar Pro 4: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3: Not ranked
Solar Pro 4: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3: 67.0; Unranked · 2 rankable rows
Solar Pro 4: Not ranked
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3 and Solar Pro 4
Category	GLM-5.3	Solar Pro 4	Basis	Reading
Agentic	66.7Supported · #14/135	Not ranked	Not comparableBenchAlign v5.8 lane · 17 vs 4 public rows	Not comparable
Coding	53.7Supported · #33/162	Not ranked	Not comparableBenchAlign v5.8 lane · 16 vs 3 public rows	Not comparable
Reasoning	57.3Supported · #36/160	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows	Not comparable
Knowledge	59.9Supported · #48/180	Not ranked	Not comparableBenchAlign v5.8 lane · 2 vs 2 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	67.0Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 1 weighted rows	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
Solar Pro 4: $0.0009; Fits in one request

GLM-5.3 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
Solar Pro 4: $0.0186; Fits in one request

GLM-5.3 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
Solar Pro 4: $0.03; Fits in one request

GLM-5.3 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-5.3Solar Pro 4

Context window

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

GLM-5.3

Z.AI GLM-5.3 model card

Solar Pro 4

512K

Upstage Solar Pro 4 launch post

API model ID

GLM-5.3

zai-org/GLM-5.3

Z.AI GLM-5.3 model card

Solar Pro 4

solar-pro4

Upstage Solar Pro 4 launch post

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

Solar Pro 4

$0.06 per 1M cached input tokens

Upstage Solar Pro 4 launch post

Documented inputs

GLM-5.3

text

Z.AI GLM-5.3 guide

Solar Pro 4

Not sourced

Documented outputs

GLM-5.3

text

Z.AI GLM-5.3 guide

Solar Pro 4

Not sourced

Provider availability

GLM-5.3

Not sourced

Solar Pro 4

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Solar Pro 4

Reasoning

Weight access

GLM-5.3

Open Weight

Solar Pro 4

Proprietary

License

GLM-5.3

Open Weight

Solar Pro 4

Proprietary

Release date

GLM-5.3

2026-08-14

Solar Pro 4

2026-08-11

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-5.3 has the larger documented window (1M).

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

Questions

Which is better, GLM-5.3 or Solar Pro 4?

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-5.3 or Solar Pro 4?

Solar Pro 4 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GLM-5.3 or Solar Pro 4?

Solar Pro 4 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-5.3 or Solar Pro 4?

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 Solar Pro 4?

GLM-5.3 has the larger documented context window: 1M, compared with 512K.

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

Solar Pro 4

API / mo$1,125

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 evidence46 rows

Agentic

Terminal-Bench 4.1
GLM-5.341.20%
Source
Solar Pro 4—
Not directly comparable
Finch
GLM-5.365.1%
Source
Solar Pro 4—
Not directly comparable
b3 Agent Security
GLM-5.393.0%
Source
Solar Pro 4—
Not directly comparable
Cyber Misuse Refusal
GLM-5.387.0%
Source
Solar Pro 4—
Not directly comparable
Terminal-Bench 2.1
GLM-5.388.2%
Source
Solar Pro 457.0%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Solar Pro 4—
Not directly comparable
CyberGym
GLM-5.384.5%
Source
Solar Pro 4—
Not directly comparable
ExploitGym
GLM-5.315.0%
Source
Solar Pro 4—
Not directly comparable
Toolathlon-Verified
GLM-5.373.0%
Source
Solar Pro 4—
Not directly comparable
AutomationBench
GLM-5.348.2%
Source
Solar Pro 4—
Not directly comparable
Agents' Last Exam
GLM-5.328.5%
Source
Solar Pro 4—
Not directly comparable
HLE w/ tools
GLM-5.362.5%
Source
Solar Pro 4—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.371.5%
Source
Solar Pro 4—
Not directly comparable
Hermes Index
GLM-5.339.2%
Source
Solar Pro 4—
Not directly comparable
Hermes Bench
GLM-5.371.8%
Source
Solar Pro 4—
Not directly comparable
MazeBench
GLM-5.30%
Source
Solar Pro 4—
Not directly comparable
CWE-bench
GLM-5.342.0%
Source
Solar Pro 4—
Not directly comparable
BrowseComp
GLM-5.3—
Solar Pro 449.2%
Source
Not directly comparable
MCP Atlas
GLM-5.3—
Solar Pro 461.4%
Source
Not directly comparable
APEX-Agents
GLM-5.3—
Solar Pro 418.7%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.388.2%
Source
Solar Pro 457.0%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Solar Pro 4—
Not directly comparable
DeepSWE
GLM-5.366.9%
Source
Solar Pro 4—
Not directly comparable
NL2Repo
GLM-5.358%
Source
Solar Pro 4—
Not directly comparable
ProgramBench
GLM-5.319.0%
Source
Solar Pro 4—
Not directly comparable
FrontierSWE
GLM-5.378.1%
Source
Solar Pro 4—
Not directly comparable
sweMarathon
GLM-5.342.5%
Source
Solar Pro 4—
Not directly comparable
PostTrainBench (lab runs)
GLM-5.339.8%
Source
Solar Pro 4—
Not directly comparable
VulcanBench v3
GLM-5.378.3%
Source
Solar Pro 4—
Not directly comparable
OpenHarmony Bench
GLM-5.360.8%
Source
Solar Pro 4—
Not directly comparable
FrontierSWE v2
GLM-5.330.2%
Source
Solar Pro 4—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.380.5%
Source
Solar Pro 4—
Not directly comparable
SWE-bench (Vals)
GLM-5.395.4%
Source
Solar Pro 4—
Not directly comparable
Bug Hunt Bench
GLM-5.319.0 fixes
Source
Solar Pro 4—
Not directly comparable
CursorBench 4.0
GLM-5.342.6%
Source
Solar Pro 4—
Not directly comparable
PostTrainBench
GLM-5.334.5%
Source
Solar Pro 4—
Not directly comparable
SWE-bench Verified
GLM-5.3—
Solar Pro 470.6%
Source
Not directly comparable
LiveCodeBench
GLM-5.3—
Solar Pro 487.8%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.388.1%
Source
Solar Pro 4—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.386.8%
Source
Solar Pro 4—
Not directly comparable
GPQA-D
GLM-5.3—
Solar Pro 489.0%
Source
Not directly comparable
MMLU-Pro
GLM-5.3—
Solar Pro 486.3%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
GLM-5.331.5%
Source
Solar Pro 4—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GLM-5.368.772%
Source
Solar Pro 4—
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.329.268%
Source
Solar Pro 4—
Not directly comparable
AIME26
GLM-5.3—
Solar Pro 495.3%
Source
Not directly comparable

46 public results · 2 shared

Related comparisons

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Last updated November 12, 2026

GLM-5.3 vs Solar Pro 4

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

Or describe your own 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-5.3 or Solar Pro 4?

Which is better for coding, GLM-5.3 or Solar Pro 4?

Which is better for agentic tasks, GLM-5.3 or Solar Pro 4?

Which costs less, GLM-5.3 or Solar Pro 4?

Which has the larger context window, GLM-5.3 or Solar Pro 4?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Instruction following

Math

Related comparisons

Watch GLM-5.3 vs Solar Pro 4