GPT-5.4 vs Ornith-1.5-9B

Updated October 2, 2026. Rank says GPT-5.4 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.4 has the higher public point estimate, 68.91 versus 28.63. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.4

OpenAI

68.91/100

Supported · Public rank #23

90% interval 64.7–73.1

Model B

Ornith-1.5-9B

Ornith AI

28.63/100

Estimated · Public rank #180

Conditional range 14.3–43.0

Shared results: 8
GPT-5.4 only: 31
Ornith-1.5-9B only: 8
Like-for-like categories: 0 / 8

Supported: GPT-5.4 · Estimated: Ornith-1.5-9B. 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
GPT-5.4
GPT-5.4 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-1.5-9B 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
Ornith-1.5-9B 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.

49.6GPT-5.4—Ornith-1.5-9B

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

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.

HLEKnowledge
Normalized gap 31.9
GPT-5.4:52.1%
Ornith-1.5-9B:20.2%
GPT-5.4 source Ornith-1.5-9B source
BrowseCompAgentic
Normalized gap 26.3
GPT-5.4:82.7%
Ornith-1.5-9B:56.4%
GPT-5.4 source Ornith-1.5-9B source
HLE w/o toolsKnowledge
Normalized gap 19.6
GPT-5.4:39.8%
Ornith-1.5-9B:20.2%
GPT-5.4 source Ornith-1.5-9B source
SWE-bench ProCoding
Normalized gap 10.2
GPT-5.4:57.7%
Ornith-1.5-9B:47.5%
GPT-5.4 source Ornith-1.5-9B source
GPQAKnowledge
Normalized gap 6.4
GPT-5.4:92.8%
Ornith-1.5-9B:86.4%
GPT-5.4 source Ornith-1.5-9B 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 v5.8 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

GPT-5.4: 48.7; Supported · #43/119
Ornith-1.5-9B: 18.0; Estimated · #101/119
Basis: BenchAlign v5.8 lane · 14 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

GPT-5.4: 66.1; Supported · #24/171
Ornith-1.5-9B: 30.6; Estimated · #134/171
Basis: BenchAlign v5.8 lane · 7 vs 4 public rows
Reading: Directional only

Coding

Not comparable

GPT-5.4: 49.6; Supported · #44/144
Ornith-1.5-9B: Not ranked
Basis: BenchAlign v5.8 lane · 5 vs 5 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-5.4: 60.6; #21/27
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.4: 69.3; #20/49
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 3 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4: Not ranked
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.4: 89.2; #19/125
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.4: 64.4; Unranked · 2 rankable rows
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.4 and Ornith-1.5-9B
Category	GPT-5.4	Ornith-1.5-9B	Basis	Reading
Agentic	48.7Supported · #43/119	18.0Estimated · #101/119	Directional onlyBenchAlign v5.8 lane · 14 vs 7 public rows	Directional only
Knowledge	66.1Supported · #24/171	30.6Estimated · #134/171	Directional onlyBenchAlign v5.8 lane · 7 vs 4 public rows	Directional only
Coding	49.6Supported · #44/144	Not ranked	Not comparableBenchAlign v5.8 lane · 5 vs 5 public rows	Not comparable
Reasoning	60.6#21/27	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	69.3#20/49	Not ranked	Not comparableProvisional lane · 3 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	89.2#19/125	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	64.4Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 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

GPT-5.4: $0.01; Fits in one request
Ornith-1.5-9B: Self-hosted; infrastructure cost varies; Fits in one request

Ornith-1.5-9B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.4: $0.17; Fits in one request
Ornith-1.5-9B: Self-hosted; infrastructure cost varies; Fits in one request

Ornith-1.5-9B has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.4: $0.25; Fits in one request
Ornith-1.5-9B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Ornith-1.5-9B 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.

SpecificationGPT-5.4Ornith-1.5-9B

Context window

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

GPT-5.4

1.05M

OpenAI pricing

Ornith-1.5-9B

262K

Ornith-1.5-9B model card

API model ID

GPT-5.4

gpt-5.4

OpenAI pricing

Ornith-1.5-9B

Not sourced

Cached-input rate

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

GPT-5.4

$0.25 per 1M cached input tokens

OpenAI pricing

Ornith-1.5-9B

No comparable hosted API rate

Ornith-1.5-9B model card

Documented inputs

GPT-5.4

Not sourced

Ornith-1.5-9B

Not sourced

Documented outputs

GPT-5.4

Not sourced

Ornith-1.5-9B

Not sourced

Provider availability

GPT-5.4

Not sourced

Ornith-1.5-9B

Not sourced

Reasoning profile

GPT-5.4

Reasoning

Ornith-1.5-9B

Reasoning

Weight access

GPT-5.4

Proprietary

Ornith-1.5-9B

Open Weight

License

GPT-5.4

Proprietary

Ornith-1.5-9B

Open Weight

Release date

GPT-5.4

2026-03-05

Ornith-1.5-9B

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: GPT-5.4 has the higher public point estimate, 68.91 versus 28.63. 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: GPT-5.4 has the larger documented window (1.05M).

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

Questions

Which is better, GPT-5.4 or Ornith-1.5-9B?

GPT-5.4 has the higher public point estimate, 68.91 versus 28.63. 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, GPT-5.4 or Ornith-1.5-9B?

Ornith-1.5-9B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-5.4 or Ornith-1.5-9B?

GPT-5.4 scores higher for agentic tasks on the public lane, 48.7 to 18. Ornith-1.5-9B 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, GPT-5.4 or Ornith-1.5-9B?

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, GPT-5.4 or Ornith-1.5-9B?

GPT-5.4 has the larger documented context window: 1.05M, 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 evidence47 rows

Agentic

Terminal-Bench 2.0
GPT-5.475.1%
Source
Ornith-1.5-9B—
Not directly comparable
CyberGym
GPT-5.479.0%
Source
Ornith-1.5-9B—
Not directly comparable
BrowseComp
GPT-5.482.7%
Source
Ornith-1.5-9B56.4%
Source
GPT-5.4 leads this result
OSWorld-Verified
GPT-5.475%
Source
Ornith-1.5-9B—
Not directly comparable
MCP Atlas
GPT-5.470.6%
Source
Ornith-1.5-9B54.2%
Source
GPT-5.4 leads this result
Toolathlon
GPT-5.454.6%
Source
Ornith-1.5-9B—
Not directly comparable
τ²-bench results
GPT-5.498.9%
Source
Ornith-1.5-9B—
Not directly comparable
Claw-Eval
GPT-5.460.3%
Source
Ornith-1.5-9B66.5%
Source
Ornith-1.5-9B leads this result
DeepSearchQA
GPT-5.473.6%
Source
Ornith-1.5-9B—
Not directly comparable
Gert Labs
GPT-5.464.89%
Source
Ornith-1.5-9B—
Not directly comparable
ResearchClawBench
GPT-5.415.3%
Source
Ornith-1.5-9B—
Not directly comparable
JobBench
GPT-5.438.9%
Source
Ornith-1.5-9B—
Not directly comparable
ExploitGym
GPT-5.46.0%
Source
Ornith-1.5-9B—
Not directly comparable
ApprenticeBench
GPT-5.411%
Source
Ornith-1.5-9B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.4—
Ornith-1.5-9B46.2%
Source
Not directly comparable
HLE w/ tools
GPT-5.4—
Ornith-1.5-9B30.5%
Source
Not directly comparable
Toolathlon-Verified
GPT-5.4—
Ornith-1.5-9B41.2%
Source
Not directly comparable
WideResearch
GPT-5.4—
Ornith-1.5-9B59.5%
Source
Not directly comparable

Coding

LiveCodeBench Pro
GPT-5.487.5%
Source
Ornith-1.5-9B—
Not directly comparable
SWE-bench Pro
GPT-5.457.7%
Source
Ornith-1.5-9B47.5%
Source
GPT-5.4 leads this result
React Native Evals
GPT-5.485.3%
Source
Ornith-1.5-9B—
Not directly comparable
Vibe Code Bench
GPT-5.467.42%
Source
Ornith-1.5-9B—
Not directly comparable
PostTrainBench v1.1
GPT-5.419.0%
Source
Ornith-1.5-9B—
Not directly comparable
Terminal-Bench 2.1
GPT-5.4—
Ornith-1.5-9B46.2%
Source
Not directly comparable
SWE-bench Verified
GPT-5.4—
Ornith-1.5-9B70.6%
Source
Not directly comparable
SWE Multilingual
GPT-5.4—
Ornith-1.5-9B54.4%
Source
Not directly comparable
NL2Repo
GPT-5.4—
Ornith-1.5-9B32.4%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.474.0%
Source
Ornith-1.5-9B—
Not directly comparable
ARC-AGI-3
GPT-5.40.2%
Source
Ornith-1.5-9B—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.481.2%
Source
Ornith-1.5-9B—
Not directly comparable
OfficeQA Pro
GPT-5.453.2%
Source
Ornith-1.5-9B—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.482.1%
Source
Ornith-1.5-9B—
Not directly comparable
CharXiv
GPT-5.482.8%
Source
Ornith-1.5-9B—
Not directly comparable
ERQA
GPT-5.465.4%
Source
Ornith-1.5-9B—
Not directly comparable
SimpleVQA
GPT-5.461.1%
Source
Ornith-1.5-9B—
Not directly comparable
ScreenSpot Pro
GPT-5.485.4%
Source
Ornith-1.5-9B—
Not directly comparable
ZeroBench
GPT-5.441.0%
Source
Ornith-1.5-9B—
Not directly comparable
MedXpertQA (MM)
GPT-5.477.1%
Source
Ornith-1.5-9B—
Not directly comparable

Knowledge

GPQA
GPT-5.492.8%
Source
Ornith-1.5-9B86.4%
Source
GPT-5.4 leads this result
HLE
GPT-5.452.1%
Source
Ornith-1.5-9B20.2%
Source
GPT-5.4 leads this result
HLE w/o tools
GPT-5.439.8%
Source
Ornith-1.5-9B20.2%
Source
GPT-5.4 leads this result
GPQA-D
GPT-5.492.8%
Source
Ornith-1.5-9B86.4%
Source
GPT-5.4 leads this result
HealthBench Hard
GPT-5.440.1%
Source
Ornith-1.5-9B—
Not directly comparable
MedXpertQA (Text)
GPT-5.459.6%
Source
Ornith-1.5-9B—
Not directly comparable
HealthBench Professional
GPT-5.448.1%
Source
Ornith-1.5-9B—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GPT-5.447.600%
Source
Ornith-1.5-9B—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.427.100%
Source
Ornith-1.5-9B—
Not directly comparable

47 public results · 8 shared

Related comparisons

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Last updated October 2, 2026

GPT-5.4 vs Ornith-1.5-9B

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

Knowledge

Coding

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, GPT-5.4 or Ornith-1.5-9B?

Which is better for coding, GPT-5.4 or Ornith-1.5-9B?

Which is better for agentic tasks, GPT-5.4 or Ornith-1.5-9B?

Which costs less, GPT-5.4 or Ornith-1.5-9B?

Which has the larger context window, GPT-5.4 or Ornith-1.5-9B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.4 vs Ornith-1.5-9B