GPT-6 Astra vs Ornith-1.5-9B

Updated October 17, 2026. Rank says GPT-6 Astra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-6 Astra has the higher public point estimate, 83.88 versus 31.33. Their conditional score ranges do not 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.

Model A

GPT-6 Astra

OpenAI

83.88/100

Supported · Public rank #3

90% interval 79.4–88.3

Model B

Ornith-1.5-9B

Ornith AI

31.33/100

Estimated · Public rank #172

Conditional range 17.0–45.7

Shared results: 4
GPT-6 Astra only: 36
Ornith-1.5-9B only: 12
Like-for-like categories: 0 / 8

Supported: GPT-6 Astra · 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-6 Astra
GPT-6 Astra 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.

75.5GPT-6 Astra—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.

BrowseCompAgentic
Normalized gap 35.1
GPT-6 Astra:91.5%
Ornith-1.5-9B:56.4%
GPT-6 Astra source Ornith-1.5-9B source
GPQAKnowledge
Normalized gap 9.6
GPT-6 Astra:96%
Ornith-1.5-9B:86.4%
GPT-6 Astra 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-6 Astra: 74.8; Supported · #7/128
Ornith-1.5-9B: 22.4; Estimated · #106/128
Basis: BenchAlign v5.8 lane · 14 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

GPT-6 Astra: 86.2; Supported · #3/184
Ornith-1.5-9B: 35.1; Estimated · #134/184
Basis: BenchAlign v5.8 lane · 7 vs 4 public rows
Reading: Directional only

Coding

Not comparable

GPT-6 Astra: 75.5; Supported · #4/150
Ornith-1.5-9B: Not ranked
Basis: BenchAlign v5.8 lane · 6 vs 5 public rows
Reading: Not comparable

Reasoning

Not comparable

GPT-6 Astra: 93.3; #1/28
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-6 Astra: 87.3; Unranked · 3 rankable rows
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-6 Astra: Not ranked
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-6 Astra: Not ranked
Ornith-1.5-9B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-6 Astra: 97.3; 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-6 Astra and Ornith-1.5-9B
Category	GPT-6 Astra	Ornith-1.5-9B	Basis	Reading
Agentic	74.8Supported · #7/128	22.4Estimated · #106/128	Directional onlyBenchAlign v5.8 lane · 14 vs 7 public rows	Directional only
Knowledge	86.2Supported · #3/184	35.1Estimated · #134/184	Directional onlyBenchAlign v5.8 lane · 7 vs 4 public rows	Directional only
Coding	75.5Supported · #4/150	Not ranked	Not comparableBenchAlign v5.8 lane · 6 vs 5 public rows	Not comparable
Reasoning	93.3#1/28	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	87.3Unranked · 3 rankable rows	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	97.3Unranked · 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-6 Astra: $0.035; 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-6 Astra: $0.65; 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-6 Astra: $0.9; 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-6 AstraOrnith-1.5-9B

Context window

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

GPT-6 Astra

1.05M

OpenAI GPT-6 Astra model documentation

Ornith-1.5-9B

262K

Ornith-1.5-9B model card

API model ID

GPT-6 Astra

gpt-6-astra

OpenAI GPT-6 Astra model documentation

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-6 Astra

$1 per 1M cached input tokens

OpenAI GPT-6 Astra model documentation

Ornith-1.5-9B

No comparable hosted API rate

Ornith-1.5-9B model card

Documented inputs

GPT-6 Astra

text, image

OpenAI GPT-6 Astra model documentation

Ornith-1.5-9B

Not sourced

Documented outputs

GPT-6 Astra

text

OpenAI GPT-6 Astra model documentation

Ornith-1.5-9B

Not sourced

Provider availability

GPT-6 Astra

Limited Availability · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Plus, Pro, Business, and Enterprise, Microsoft Azure, AWS Bedrock

OpenAI GPT-6 Astra model documentation

Ornith-1.5-9B

Not sourced

Reasoning profile

GPT-6 Astra

Reasoning

Ornith-1.5-9B

Reasoning

Weight access

GPT-6 Astra

Proprietary

Ornith-1.5-9B

Open Weight

License

GPT-6 Astra

Proprietary

Ornith-1.5-9B

Open Weight

Release date

GPT-6 Astra

2026-09-03

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-6 Astra has the higher public point estimate, 83.88 versus 31.33. 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-6 Astra has the larger documented window (1.05M).

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

Questions

Which is better, GPT-6 Astra or Ornith-1.5-9B?

GPT-6 Astra has the higher public point estimate, 83.88 versus 31.33. 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-6 Astra 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-6 Astra or Ornith-1.5-9B?

GPT-6 Astra scores higher for agentic tasks on the public lane, 74.8 to 22.4. 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-6 Astra 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-6 Astra or Ornith-1.5-9B?

GPT-6 Astra 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 evidence52 rows

Agentic

BrowseComp
GPT-6 Astra91.5%
Source
Ornith-1.5-9B56.4%
Source
GPT-6 Astra leads this result
OSWorld 2.0
GPT-6 Astra72.6%
Source
Ornith-1.5-9B—
Not directly comparable
Terminal-Bench 4.0
GPT-6 Astra57.90%
Source
Ornith-1.5-9B—
Not directly comparable
Terminal-Bench-Science 0.1
GPT-6 Astra64.6%
Source
Ornith-1.5-9B—
Not directly comparable
ExploitGym
GPT-6 Astra42.4%
Source
Ornith-1.5-9B—
Not directly comparable
Agents' Last Exam
GPT-6 Astra59.3%
Source
Ornith-1.5-9B—
Not directly comparable
AutomationBench
GPT-6 Astra41.4%
Source
Ornith-1.5-9B—
Not directly comparable
HLE w/ tools
GPT-6 Astra57.2%
Source
Ornith-1.5-9B30.5%
Source
GPT-6 Astra leads this result
Terminal-Bench 2.1 (Vals)
GPT-6 Astra87.3%
Source
Ornith-1.5-9B—
Not directly comparable
ApprenticeBench
GPT-6 Astra68%
Source
Ornith-1.5-9B—
Not directly comparable
CWE-bench v1
GPT-6 Astra68.0%
Source
Ornith-1.5-9B—
Not directly comparable
Hermes Index
GPT-6 Astra56.3%
Source
Ornith-1.5-9B—
Not directly comparable
Hermes Bench
GPT-6 Astra73.7%
Source
Ornith-1.5-9B—
Not directly comparable
MazeBench
GPT-6 Astra23%
Source
Ornith-1.5-9B—
Not directly comparable
Terminal-Bench 2.1
GPT-6 Astra—
Ornith-1.5-9B46.2%
Source
Not directly comparable
MCP Atlas
GPT-6 Astra—
Ornith-1.5-9B54.2%
Source
Not directly comparable
Toolathlon-Verified
GPT-6 Astra—
Ornith-1.5-9B41.2%
Source
Not directly comparable
WideResearch
GPT-6 Astra—
Ornith-1.5-9B59.5%
Source
Not directly comparable
Claw-Eval
GPT-6 Astra—
Ornith-1.5-9B66.5%
Source
Not directly comparable

Coding

DeepSWE
GPT-6 Astra74.1%
Source
Ornith-1.5-9B—
Not directly comparable
FrontierCode 1.1 Main
GPT-6 Astra53.3%
Source
Ornith-1.5-9B—
Not directly comparable
FrontierCode 1.1 Extended
GPT-6 Astra64.5%
Source
Ornith-1.5-9B—
Not directly comparable
FrontierSWE v2
GPT-6 Astra65.5%
Source
Ornith-1.5-9B—
Not directly comparable
PostTrainBench v1.1
GPT-6 Astra44.3%
Source
Ornith-1.5-9B—
Not directly comparable
Bug Hunt Bench
GPT-6 Astra45.0 fixes
Source
Ornith-1.5-9B—
Not directly comparable
Terminal-Bench 2.1
GPT-6 Astra—
Ornith-1.5-9B46.2%
Source
Not directly comparable
SWE-bench Verified
GPT-6 Astra—
Ornith-1.5-9B70.6%
Source
Not directly comparable
SWE-bench Pro
GPT-6 Astra—
Ornith-1.5-9B47.5%
Source
Not directly comparable
SWE Multilingual
GPT-6 Astra—
Ornith-1.5-9B54.4%
Source
Not directly comparable
NL2Repo
GPT-6 Astra—
Ornith-1.5-9B32.4%
Source
Not directly comparable

Reasoning

STEMBench
GPT-6 Astra2.81
Source
Ornith-1.5-9B—
Not directly comparable
ARC-AGI-1
GPT-6 Astra98.50%
Source
Ornith-1.5-9B—
Not directly comparable
ARC-AGI-2
GPT-6 Astra95%
Source
Ornith-1.5-9B—
Not directly comparable
ARC-AGI-3
GPT-6 Astra62.7%
Source
Ornith-1.5-9B—
Not directly comparable
GeneBench-Pro
GPT-6 Astra37.8%
Source
Ornith-1.5-9B—
Not directly comparable
MRCR v2 256K-512K
GPT-6 Astra100.0%
Source
Ornith-1.5-9B—
Not directly comparable
MRCR v2 512K-1M
GPT-6 Astra96.3%
Source
Ornith-1.5-9B—
Not directly comparable
GraphWalks BFS 256K–1M
GPT-6 Astra71.8%
Source
Ornith-1.5-9B—
Not directly comparable

Multimodal

ScreenSpot Pro
GPT-6 Astra92.7%
Source
Ornith-1.5-9B—
Not directly comparable
BenchCAD Vision2Code (tools)
GPT-6 Astra0.959
Source
Ornith-1.5-9B—
Not directly comparable

Knowledge

GPQA
GPT-6 Astra96%
Source
Ornith-1.5-9B86.4%
Source
GPT-6 Astra leads this result
GPQA-D
GPT-6 Astra96.0%
Source
Ornith-1.5-9B86.4%
Source
GPT-6 Astra leads this result
HealthBench (raw)
GPT-6 Astra56.9%
Source
Ornith-1.5-9B—
Not directly comparable
HealthBench (length-adjusted)
GPT-6 Astra58.3%
Source
Ornith-1.5-9B—
Not directly comparable
HealthBench Professional
GPT-6 Astra64.7%
Source
Ornith-1.5-9B—
Not directly comparable
HealthBench Professional (raw)
GPT-6 Astra68.2%
Source
Ornith-1.5-9B—
Not directly comparable
HealthBench Hard
GPT-6 Astra36.6%
Source
Ornith-1.5-9B—
Not directly comparable
HLE
GPT-6 Astra—
Ornith-1.5-9B20.2%
Source
Not directly comparable
HLE w/o tools
GPT-6 Astra—
Ornith-1.5-9B20.2%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
GPT-6 Astra8.5%
Source
Ornith-1.5-9B—
Not directly comparable

Math

FrontierMath v2 (Tier 4)
GPT-6 Astra97.600%
Source
Ornith-1.5-9B—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-6 Astra93.684%
Source
Ornith-1.5-9B—
Not directly comparable

52 public results · 4 shared

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Last updated October 17, 2026

GPT-6 Astra 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-6 Astra or Ornith-1.5-9B?

Which is better for coding, GPT-6 Astra or Ornith-1.5-9B?

Which is better for agentic tasks, GPT-6 Astra or Ornith-1.5-9B?

Which costs less, GPT-6 Astra or Ornith-1.5-9B?

Which has the larger context window, GPT-6 Astra or Ornith-1.5-9B?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch GPT-6 Astra vs Ornith-1.5-9B