Ornith 1.5 released
Ornith AI · Model release
Source confirmed
Data as of October 2, 2026 · How the score is built
CurrentOpen WeightReasoning
Ornith-1.5-9B
Decision readingOrnith-1.5-9B scores 28.6 out of 100 and ranks #184 of 212. This profile shows 16 source-displayable benchmark rows; its strongest eligible category is Agentic at #101. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Aug 18, 2026 — see all recent releases
Ornith-1.5-9B will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
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Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
28.6/100
field median 52.2#184 of 212 ranked models
Public
#184of 212
Verified —
Price
Self-hosted; infrastructure cost varies
input median $0.95No comparable first-party hosted token rate
Speed
Not measured
field median 89 tok/sTime to first token not measured
Context
262Ktokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Agentic ranks #101. Particularly useful for coding agents, browser research, and computer-use workflows.
Validate before choosing
16 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 16 displayable benchmark rows
Follow model changesCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #101 of 119Percentile 15thWeight 22%7 benchmarksVerified | 18.0 | 7 benchmarks | Verified | |||
| CodingRank Not rankedWeight 20%5 benchmarksVerified | 26.6 | 5 benchmarks | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeRank #134 of 171Percentile 22ndWeight 12%4 benchmarksVerified | 30.6 | 4 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
16 of 645 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic7/7 verified
- Coding5/5 verified
- ReasoningNot measured
- MultimodalNot measured
- Knowledge4/4 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot measured
Verified sourceProvisionalNot measured
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Ornith-1.5-9B category percentile values
- Agentic15th percentile
- CodingNot eligible
- ReasoningNot eligible
- MultimodalNot eligible
- Knowledge22nd percentile
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#101/119
- CodingNot ranked
- ReasoningNot ranked
- MultimodalNot ranked
- Knowledge#134/171
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
Top decileTop quartileMid-fieldNot eligible
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding5 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score47.5% | Versus best verified row Best verified: Claude Opus 5.5 · 89.9% | Gap42.4 behind | Weight26% ref. weight | Provider exact |
| SWE Multilingual | Score54.4% | Versus best verified row Best verified: Claude Opus 5.5 · 93.9% | Gap39.5 behind | Weight5% ref. weight | Provider exact |
| Terminal-Bench 2.1 | Score46.2% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap46.6 behind | WeightScored in Agentic | Provider exact |
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score70.6% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap25.4 behind | WeightDisplay only | Provider exact |
| NL2Repo | Score32.4% | Versus best verified row Best verified: DeepSeek V4.1 Flash · 65.4% | Gap33 behind | WeightDisplay only | Provider exact |
Agentic7 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.1 | Score46.2% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap46.6 behind | Weight8% ref. weight | Provider exact |
| BrowseComp | Score56.4% | Versus best verified row Best verified: Atria Dawn Preview · 92.5% | Gap36.1 behind | Weight8% ref. weight | Provider exact |
| MCP Atlas | Score54.2% | Versus best verified row Best verified: Muse Spark 1.1 · 88.1% | Gap33.9 behind | Weight4% ref. weight | Provider exact |
| HLE w/ toolsHumanity's Last Exam with tools | Score30.5% | Versus best verified row Best verified: Claude Opus 5.5 · 67.7% | Gap37.2 behind | Weight3% ref. weight | Provider exact |
| Toolathlon-Verified | Score41.2% | Versus best verified row Best verified: Claude Opus 5 · 80.6% | Gap39.4 behind | Weight3% ref. weight | Provider exact |
| WideResearch | Score59.5% | Versus best verified row Best verified: Hy4 preview · 83.9% | Gap24.4 behind | WeightDisplay only | Provider exact |
| Claw-Eval | Score66.5% | Versus best verified row Best verified: Ornith-1.5-397B · 81.4% | Gap14.9 behind | WeightDisplay only | Provider exact |
Knowledge4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HLEHumanity's Last Exam | Score20.2% | Versus best verified row Best verified: Claude Fable 5.1 · 65% | Gap44.8 behind | Weight44% ref. weight | Provider exact |
| HLE w/o toolsHumanity's Last Exam without tools | Score20.2% | Versus best verified row Best verified: Claude Opus 5.5 · 64.4% | Gap44.2 behind | Weight7% ref. weight | Provider exact |
| GPQAGraduate-Level Google-Proof Q&A | Score86.4% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap9.6 behind | Weight3% ref. weight | Provider exact |
| GPQA-DGPQA Diamond | Score86.4% | Versus best verified row Best verified: GPT-6 Astra · 96.0% | Gap9.6 behind | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 16 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Radar
Full release historyOrnith-1.5-9B release history
Radar confirmed these at the source. Use Ornith-1.5-9B in your work? Explore Radar to follow supported changes and choose your alerts.
Radar
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not publishedOrnith-1.5-9B model card
- Context window
- 262KOrnith-1.5-9B model card
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Ornith publishes the MIT-licensed 9B dense checkpoint for self-hosted and mobile deployment. Its official Hugging Face configuration specifies a 262,144-token context window; no first-party hosted API or token rate is published for this exact model.
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing recordOrnith-1.5-9B model card
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Ornith-1.5-9B ranks #184 of 212 on the public leaderboard with a score of 28.63/100. It does not yet have enough sourced coverage for a verified position.
Ornith-1.5-9B is a open weight model with a 262K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Ornith publishes the MIT-licensed 9B dense checkpoint for self-hosted and mobile deployment. Its official Hugging Face configuration specifies a 262,144-token context window; no first-party hosted API or token rate is published for this exact model.
Official exact-value snapshot from Ornith's August 2026 Ornith-1.5 launch page. BenchLM maps the directly comparable provider-reported rows, including the versioned Terminal-Bench 2.1 result as distinct display evidence; the source says all Ornith-1.5 results are averaged over five runs. SWE Atlas QnA and Terminal-Bench Claude Code remain outside the current schema. The family remains unranked while independent benchmark-native coverage is unavailable.
Ornith-1.5-9B sits in the Ornith 1.5 family with Ornith-1.5-397B, Ornith-1.5-35B-A3B. Its explicit predecessor is Ornith-1.0-9B. 16 of 645 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Agentic at #101, while its lowest eligible position is Knowledge at #134. particularly useful for coding agents, browser research, and computer-use workflows.
Last updated October 2, 2026. Runtime fields remain blank until a sourced snapshot exists.
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersQuestions
How does Ornith-1.5-9B perform overall in AI benchmarks?
Ornith-1.5-9B ranks #184 out of 212 models on the public BenchAlign leaderboard, with a score of 28.63/100. Its evidence status is Estimated, and this profile shows 16 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Ornith-1.5-9B good for knowledge and understanding?
Ornith-1.5-9B ranks #134 out of 171 eligible models for knowledge and understanding, with a public category score of 30.6/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Ornith-1.5-9B good for coding and programming?
Ornith-1.5-9B has source-displayable benchmark coverage for coding and programming, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is Ornith-1.5-9B good for agentic tool use and computer tasks?
Ornith-1.5-9B ranks #101 out of 119 eligible models for agentic tool use and computer tasks, with a public category score of 18/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Ornith-1.5-9B open source?
Ornith-1.5-9B is an open-weight model from Ornith AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Which sibling models are related to Ornith-1.5-9B?
Ornith-1.5-9B belongs to the Ornith 1.5 family. Related tracked variants include Ornith-1.5-397B, Ornith-1.5-35B-A3B. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does Ornith-1.5-9B have full benchmark coverage on BenchLM?
No. Ornith-1.5-9B currently has 16 source-displayable rows across 645 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Ornith-1.5-9B?
Ornith-1.5-9B has a documented context window of 262K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.