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Data

Data as of November 12, 2026 · How the score is built

Superseded.DeepReinforce AI has newer models in this line:Ornith-1.5-9B
SupersededOpen WeightReasoning

Released June 2026256K context

Ornith-1.0-9B

Decision readingOrnith-1.0-9B is tracked, but not publicly ranked yet. The profile exposes 7 sourced benchmark rows and leaves unsupported fields blank until a published record exists.

Released June 2026 — see all recent releases

Email me when its price, rank or availability changes.

Find alternativesCompare Ornith-1.0-9B

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

Unranked

field median 49.6Not eligible for a public rank

Price

Self-hosted; infrastructure cost varies

input median $0.60No comparable first-party hosted token rate

Speed

Not measured

field median 92 tok/sTime to first token not measured

Context

256Ktokens

field median 256,000Reported for this model; direct source link not stored

Strongest published evidence

Published rows are visible, but no category has enough eligible evidence for a comparative rank.

Validate before choosing

7 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.

Source-linked · 7 displayable benchmark rows

Category 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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticWeight 22%2 benchmarksVerified
Score pending
CodingRank Not rankedWeight 20%5 benchmarksVerified
29.4
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeWeight 12%0 benchmarksNot measured
Not measured
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathWeight 5%0 benchmarksNot measured
Not measured

7 of 899 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How 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.

  1. Agentic2/2 verified
  2. Coding5/5 verified
  3. ReasoningNot measured
  4. MultimodalNot measured
  5. KnowledgeNot measured
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. MathNot measured
Verified sourceProvisionalNot measured

Benchmark ledger

Every result says who ran the test. For this model, DeepReinforce AI ran all 7. Coding opens by default. The tick marks the best source-verified result in the catalog; provisional leaders do not set the reference.

Overall rank
Not ranked yet
No benchmark-run or independent results yet
Reported by DeepReinforce AI
7
Shown with that label
Run by a benchmark
0
None yet
Independent runs
0
None yet
Coding5 rows
Coding benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
SWE-bench Pro42.9%

Best verified: Claude Fable 5.2 · 93%

Gap50.1 behindReported by DeepReinforce AIWeighted benchmark26% ref. weight
SWE Multilingual52%

Best verified: Claude Fable 5.2 · 96.6%

Gap44.6 behindReported by DeepReinforce AIWeighted benchmark5% ref. weight
Terminal-Bench 2.143.1%

Best verified: SWE-2 · 92.8%

Gap49.7 behindReported by DeepReinforce AIWeighted benchmarkScored in Agentic
SWE-bench VerifiedSoftware Engineering Benchmark Verified69.4%

Best verified: Claude Opus 5 · 96%

Gap26.6 behindReported by DeepReinforce AIShown only
NL2Repo27.2%

Best verified: DeepSeek V4.1 Flash 1017 · 68.2%

Gap41 behindReported by DeepReinforce AIShown only
Agentic2 rows
Agentic benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
Terminal-Bench 2.143.1%

Best verified: SWE-2 · 92.8%

Gap49.7 behindReported by DeepReinforce AIWeighted benchmark9% ref. weight
Claw-Eval63.1%

Best verified: Ornith-1.5-397B · 81.4%

Gap18.3 behindReported by DeepReinforce AIShown only

Outlined bars are lab-reported. Filled bars are benchmark-run or independent. Bars run 0–100; the dark tick marks the best source-verified value.

Lineage

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.

  1. June 2026 · you are here

    Ornith-1.0-9B

    Not publicly ranked · Price not listed

  2. Aug 18, 2026

    Ornith-1.5-9B

    Score 32.5 · Price not listed

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 published
Context window
256K
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
9B total · DENSE
Availability
Not sourced yet
Cloud regions
Not tracked yet
Lifecycle
Superseded
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing record
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.

We track Ornith-1.0-9B, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.

Ornith-1.0-9B is a open weight model with a 256K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Official exact-value snapshot from DeepReinforce's Ornith 1.0 launch page and matching Hugging Face model card. BenchLM maps Terminal-Bench 2.1 Terminus-2, SWE-bench Verified/Pro/Multilingual, NL2Repo, and ClawEval. Terminal-Bench Claude Code and SWE Atlas rows remain out of schema.

Ornith-1.0-9B sits in the Ornith 1.0 family with Ornith-1.0-397B, Ornith-1.0-35B. 7 of 899 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Last updated November 12, 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 parameters

Questions

How does Ornith-1.0-9B perform overall in AI benchmarks?

Ornith-1.0-9B has 7 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.

Is Ornith-1.0-9B good for coding and programming?

Ornith-1.0-9B 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-1.0-9B good for agentic tool use and computer tasks?

Ornith-1.0-9B has source-displayable benchmark coverage for agentic tool use and computer tasks, 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-1.0-9B open source?

Ornith-1.0-9B is an open-weight model from DeepReinforce 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-1.0-9B?

Ornith-1.0-9B belongs to the Ornith 1.0 family. Related tracked variants include Ornith-1.0-397B, Ornith-1.0-35B. 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-1.0-9B have full benchmark coverage on BenchLM?

No. Ornith-1.0-9B currently has 7 source-displayable rows. 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-1.0-9B?

Ornith-1.0-9B has a reported context window of 256K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.

Compare Ornith-1.0-9B with every tracked model1028 comparisons