Long documents
Prompts that approach the documented context limit
GPT-5.4 nano
GPT-5.4 nano has the larger documented context window.
Confidence: documented
GPT-5.4 nano vs Ornith-1.5-9B
Updated October 2, 2026. Rank says GPT-5.4 nano is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GPT-5.4 nano has the higher public point estimate, 51.46 versus 28.63. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- GPT-5.4 nano only
- 16
- Ornith-1.5-9B only
- 12
- Like-for-like categories
- 0 / 8
Supported: GPT-5.4 nano · 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.
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: limitedAgentic 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: limitedChat 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-ratesCache-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-ratesRepository 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
31.1GPT-5.4 nano—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.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
16 GPT-5.4 nano only
4 Shared
12 Ornith-1.5-9B only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 17.5GPT-5.4 nano:37.7%Ornith-1.5-9B:20.2%HLE w/o toolsKnowledge
Normalized gap 4.1GPT-5.4 nano:24.3%Ornith-1.5-9B:20.2%GPQAKnowledge
Normalized gap 3.6GPT-5.4 nano:82.8%Ornith-1.5-9B:86.4%
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 nano
- 31.8
- Supported · #77/119
- Ornith-1.5-9B
- 18.0
- Estimated · #101/119
- Basis
- BenchAlign v5.8 lane · 6 vs 7 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.4 nano
- 37.9
- Supported · #105/171
- Ornith-1.5-9B
- 30.6
- Estimated · #134/171
- Basis
- BenchAlign v5.8 lane · 5 vs 4 public rows
- Reading
- Directional only
Coding
Not comparable- GPT-5.4 nano
- 31.1
- Supported · #91/144
- Ornith-1.5-9B
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4 nano
- 37.0
- Unranked · 4 rankable rows
- Ornith-1.5-9B
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 nano
- 24.9
- #46/49
- Ornith-1.5-9B
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- 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 nano
- 91.9
- #9/125
- Ornith-1.5-9B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 43.8
- Unranked · 2 rankable rows
- Ornith-1.5-9B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | Ornith-1.5-9B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 31.8Supported · #77/119 | 18.0Estimated · #101/119 | Directional onlyBenchAlign v5.8 lane · 6 vs 7 public rows | Directional only |
| Knowledge | 37.9Supported · #105/171 | 30.6Estimated · #134/171 | Directional onlyBenchAlign v5.8 lane · 5 vs 4 public rows | Directional only |
| Coding | 31.1Supported · #91/144 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 5 public rows | Not comparable |
| Reasoning | 37.0Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 24.9#46/49 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 91.9#9/125 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 43.8Unranked · 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 nano
- $0.00082
- 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 nano
- $0.01375
- 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 nano
- $0.0205
- 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-5.4 nanoOrnith-1.5-9B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
Ornith-1.5-9B
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationOrnith-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 nano
$0.02 per 1M cached input tokens
OpenAI pricingOrnith-1.5-9B
No comparable hosted API rate
Ornith-1.5-9B model cardDocumented inputs
GPT-5.4 nano
text, image
OpenAI model catalogOrnith-1.5-9B
Not sourced
Documented outputs
GPT-5.4 nano
Ornith-1.5-9B
Not sourced
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogOrnith-1.5-9B
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
Ornith-1.5-9B
Reasoning
Weight access
GPT-5.4 nano
Proprietary
Ornith-1.5-9B
Open Weight
License
GPT-5.4 nano
Proprietary
Ornith-1.5-9B
Open Weight
Release date
GPT-5.4 nano
2026-03-17
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 nano has the higher public point estimate, 51.46 versus 28.63. Their conditional score ranges 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 nano has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.4 nano or Ornith-1.5-9B?
GPT-5.4 nano has the higher public point estimate, 51.46 versus 28.63. Their conditional score ranges 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 nano 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 nano or Ornith-1.5-9B?
GPT-5.4 nano scores higher for agentic tasks on the public lane, 31.8 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 nano 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 nano or Ornith-1.5-9B?
GPT-5.4 nano has the larger documented context window: 400K, 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 evidence32 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%SourceOrnith-1.5-9B—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%SourceOrnith-1.5-9B—Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%SourceOrnith-1.5-9B54.2%SourceGPT-5.4 nano leads this result
Toolathlon
GPT-5.4 nano35.5%SourceOrnith-1.5-9B—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%SourceOrnith-1.5-9B—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 nano41.6%SourceOrnith-1.5-9B—Not directly comparable
Terminal-Bench 2.1
GPT-5.4 nano—Ornith-1.5-9B46.2%SourceNot directly comparable
HLE w/ tools
GPT-5.4 nano—Ornith-1.5-9B30.5%SourceNot directly comparable
Toolathlon-Verified
GPT-5.4 nano—Ornith-1.5-9B41.2%SourceNot directly comparable
WideResearch
GPT-5.4 nano—Ornith-1.5-9B59.5%SourceNot directly comparable
BrowseComp
GPT-5.4 nano—Ornith-1.5-9B56.4%SourceNot directly comparable
Claw-Eval
GPT-5.4 nano—Ornith-1.5-9B66.5%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5.4 nano26.10%SourceOrnith-1.5-9B—Not directly comparable
LiveCodeBench (Vals)
GPT-5.4 nano84.0%SourceOrnith-1.5-9B—Not directly comparable
SWE-bench (Vals)
GPT-5.4 nano69.8%SourceOrnith-1.5-9B—Not directly comparable
Terminal-Bench 2.1
GPT-5.4 nano—Ornith-1.5-9B46.2%SourceNot directly comparable
SWE-bench Verified
GPT-5.4 nano—Ornith-1.5-9B70.6%SourceNot directly comparable
SWE-bench Pro
GPT-5.4 nano—Ornith-1.5-9B47.5%SourceNot directly comparable
SWE Multilingual
GPT-5.4 nano—Ornith-1.5-9B54.4%SourceNot directly comparable
NL2Repo
GPT-5.4 nano—Ornith-1.5-9B32.4%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.4 nano82.8%SourceOrnith-1.5-9B86.4%SourceOrnith-1.5-9B leads this result
HLE
GPT-5.4 nano37.7%SourceOrnith-1.5-9B20.2%SourceGPT-5.4 nano leads this result
HLE w/o tools
GPT-5.4 nano24.3%SourceOrnith-1.5-9B20.2%SourceGPT-5.4 nano leads this result
GPQA Diamond (Vals)
GPT-5.4 nano77.5%SourceOrnith-1.5-9B—Not directly comparable
MMLU-Pro (Vals)
GPT-5.4 nano77.2%SourceOrnith-1.5-9B—Not directly comparable
GPQA-D
GPT-5.4 nano—Ornith-1.5-9B86.4%SourceNot directly comparable
Math
32 public results · 4 shared
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Last updated October 2, 2026