GLM-5.3 vs Ornith-1.5-35B-A3B

Updated October 2, 2026. Rank says GLM-5.3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GLM-5.3 has the higher public point estimate, 65.7 versus 32.61. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 6 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

65.7/100

Estimated · Public rank #32

Conditional range 51.4–80.0

Model B

Ornith-1.5-35B-A3B

Ornith AI

32.61/100

Estimated · Public rank #152

Conditional range 18.3–47.0

Shared results: 6
GLM-5.3 only: 19
Ornith-1.5-35B-A3B only: 12
Like-for-like categories: 0 / 8

Estimated: GLM-5.3 and Ornith-1.5-35B-A3B. Conditional ranges do not establish rank confidence.How 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
Ornith-1.5-35B-A3B 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
Ornith-1.5-35B-A3B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.

56.6GLM-5.324.0Ornith-1.5-35B-A3B

Directional only · BenchAlign v5.8

GLM-5.3 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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.

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, 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

Directional only

GLM-5.3: 67.4; Supported · #12/119
Ornith-1.5-35B-A3B: 23.5; Estimated · #95/119
Basis: BenchAlign v5.8 lane · 9 vs 7 public rows
Reading: Directional only

Coding

Directional only

GLM-5.3: 56.6; Supported · #27/144
Ornith-1.5-35B-A3B: 24.0; Estimated · #110/144
Basis: BenchAlign v5.8 lane · 13 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5.3: 61.5; Supported · #38/171
Ornith-1.5-35B-A3B: 34.5; Estimated · #119/171
Basis: BenchAlign v5.8 lane · 2 vs 4 public rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.3: 77.1; #12/27
Ornith-1.5-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.3: Not ranked
Ornith-1.5-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3: Not ranked
Ornith-1.5-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3: Not ranked
Ornith-1.5-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3: Not ranked
Ornith-1.5-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3 and Ornith-1.5-35B-A3B
Category	GLM-5.3	Ornith-1.5-35B-A3B	Basis	Reading
Agentic	67.4Supported · #12/119	23.5Estimated · #95/119	Directional onlyBenchAlign v5.8 lane · 9 vs 7 public rows	Directional only
Coding	56.6Supported · #27/144	24.0Estimated · #110/144	Directional onlyBenchAlign v5.8 lane · 13 vs 7 public rows	Directional only
Knowledge	61.5Supported · #38/171	34.5Estimated · #119/171	Directional onlyBenchAlign v5.8 lane · 2 vs 4 public rows	Directional only
Reasoning	77.1#12/27	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
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	Not ranked	Not ranked	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 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
Ornith-1.5-35B-A3B: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate. Ornith-1.5-35B-A3B 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
Ornith-1.5-35B-A3B: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate. Ornith-1.5-35B-A3B 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
Ornith-1.5-35B-A3B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GLM-5.3 has no comparable published API token rate. Ornith-1.5-35B-A3B 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-5.3Ornith-1.5-35B-A3B

Context window

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

GLM-5.3

Z.AI GLM-5.3 model card

Ornith-1.5-35B-A3B

262K

Ornith-1.5-35B-A3B model card

API model ID

GLM-5.3

zai-org/GLM-5.3

Z.AI GLM-5.3 model card

Ornith-1.5-35B-A3B

Not sourced

Cached-input rate

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

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Ornith-1.5-35B-A3B

No comparable hosted API rate

Ornith-1.5-35B-A3B model card

Documented inputs

GLM-5.3

Not sourced

Ornith-1.5-35B-A3B

Not sourced

Documented outputs

GLM-5.3

Not sourced

Ornith-1.5-35B-A3B

Not sourced

Provider availability

GLM-5.3

Not sourced

Ornith-1.5-35B-A3B

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Ornith-1.5-35B-A3B

Reasoning

Weight access

GLM-5.3

Open Weight

Ornith-1.5-35B-A3B

Open Weight

License

GLM-5.3

Open Weight

Ornith-1.5-35B-A3B

Open Weight

Release date

GLM-5.3

2026-08-14

Ornith-1.5-35B-A3B

2026-08-18

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-5.3 has the higher public point estimate, 65.7 versus 32.61. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
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 Ornith-1.5-35B-A3B?

GLM-5.3 has the higher public point estimate, 65.7 versus 32.61. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GLM-5.3 or Ornith-1.5-35B-A3B?

GLM-5.3 scores higher for coding on the public lane, 56.6 to 24. Ornith-1.5-35B-A3B 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, GLM-5.3 or Ornith-1.5-35B-A3B?

GLM-5.3 scores higher for agentic tasks on the public lane, 67.4 to 23.5. Ornith-1.5-35B-A3B 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, GLM-5.3 or Ornith-1.5-35B-A3B?

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 Ornith-1.5-35B-A3B?

GLM-5.3 has the larger documented context window: 1M, compared with 262K.

Benchmark evidence

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

Browse raw public benchmark evidence37 rows

Agentic

Terminal-Bench 2.1
GLM-5.388.2%
Source
Ornith-1.5-35B-A3B67.8%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
CyberGym
GLM-5.384.5%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
ExploitGym
GLM-5.315.0%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
Toolathlon-Verified
GLM-5.373.0%
Source
Ornith-1.5-35B-A3B48.7%
Source
GLM-5.3 leads this result
AutomationBench
GLM-5.348.2%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
Agents' Last Exam
GLM-5.328.5%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
HLE w/ tools
GLM-5.362.5%
Source
Ornith-1.5-35B-A3B33.4%
Source
GLM-5.3 leads this result
Terminal-Bench 2.1 (Vals)
GLM-5.371.5%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
MCP Atlas
GLM-5.3—
Ornith-1.5-35B-A3B70.2%
Source
Not directly comparable
WideResearch
GLM-5.3—
Ornith-1.5-35B-A3B67.8%
Source
Not directly comparable
BrowseComp
GLM-5.3—
Ornith-1.5-35B-A3B67.6%
Source
Not directly comparable
Claw-Eval
GLM-5.3—
Ornith-1.5-35B-A3B72.5%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.388.2%
Source
Ornith-1.5-35B-A3B67.8%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
DeepSWE
GLM-5.366.9%
Source
Ornith-1.5-35B-A3B22.0%
Source
GLM-5.3 leads this result
NL2Repo
GLM-5.358%
Source
Ornith-1.5-35B-A3B46.2%
Source
GLM-5.3 leads this result
ProgramBench
GLM-5.319.0%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
FrontierSWE
GLM-5.378.1%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
sweMarathon
GLM-5.342.5%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
PostTrain Bench
GLM-5.339.8%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
VulcanBench v3
GLM-5.378.3%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
OpenHarmony Bench
GLM-5.360.8%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
FrontierSWE v2
GLM-5.330.2%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.380.5%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
SWE-bench (Vals)
GLM-5.395.4%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
SWE-bench Verified
GLM-5.3—
Ornith-1.5-35B-A3B79%
Source
Not directly comparable
SWE-bench Pro
GLM-5.3—
Ornith-1.5-35B-A3B59.6%
Source
Not directly comparable
SWE Multilingual
GLM-5.3—
Ornith-1.5-35B-A3B71.4%
Source
Not directly comparable
frontierBench
GLM-5.3—
Ornith-1.5-35B-A3B5.1%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.388.1%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.386.8%
Source
Ornith-1.5-35B-A3B—
Not directly comparable
GPQA
GLM-5.3—
Ornith-1.5-35B-A3B89.2%
Source
Not directly comparable
GPQA-D
GLM-5.3—
Ornith-1.5-35B-A3B89.2%
Source
Not directly comparable
HLE
GLM-5.3—
Ornith-1.5-35B-A3B25.6%
Source
Not directly comparable
HLE w/o tools
GLM-5.3—
Ornith-1.5-35B-A3B25.6%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
GLM-5.331.5%
Source
Ornith-1.5-35B-A3B—
Not directly comparable

37 public results · 6 shared

Related comparisons

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Last updated October 2, 2026

GLM-5.3 vs Ornith-1.5-35B-A3B

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

Knowledge

Reasoning

Multimodal

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 Ornith-1.5-35B-A3B?

Which is better for coding, GLM-5.3 or Ornith-1.5-35B-A3B?

Which is better for agentic tasks, GLM-5.3 or Ornith-1.5-35B-A3B?

Which costs less, GLM-5.3 or Ornith-1.5-35B-A3B?

Which has the larger context window, GLM-5.3 or Ornith-1.5-35B-A3B?

Benchmark evidence

Agentic

Coding

Knowledge

Instruction following

Related comparisons

Watch GLM-5.3 vs Ornith-1.5-35B-A3B