Agentic
Directional only- GPT-5.3 Codex
- 71.4
- Ornith-1.0-397B
- 77.5
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
GPT-5.3 Codex vs Ornith-1.0-397B
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
3 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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-5.3 Codex
GPT-5.3 Codex 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are 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: rate-fallback
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 GPT-5.3 Codex only
3 Shared
4 Ornith-1.0-397B only
- Shared results
- 3
- GPT-5.3 Codex only
- 5
- Ornith-1.0-397B only
- 4
- Like-for-like categories
- 0 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- GPT-5.3 Codex
- 67.2
- Ornith-1.0-397B
- 74.6
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.3 Codex
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.3 Codex
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.3 Codex
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.3 Codex
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.3 Codex
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.3 Codex
- Not measured
- Ornith-1.0-397B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.3 Codex | Ornith-1.0-397B | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 71.4 | 77.5 | Directional only2 vs 1 rows | Directional only |
| Coding | 67.2 | 74.6 | Directional only3 vs 2 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
SWE-bench Pro
Coding
GPT-5.3 Codex: 56.8%Ornith-1.0-397B: 62.2%Normalized gap 5.4GPT-5.3 Codex source Ornith-1.0-397B sourceSWE-bench Verified
Coding
GPT-5.3 Codex: 85%Ornith-1.0-397B: 82.4%Normalized gap 2.6GPT-5.3 Codex source Ornith-1.0-397B sourceTerminal-Bench 2.0
Agentic
GPT-5.3 Codex: 77.3%Ornith-1.0-397B: 77.5%Normalized gap 0.2GPT-5.3 Codex source Ornith-1.0-397B source
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-5.3 Codex
- $0.00875
- Fits in one request
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
Ornith-1.0-397B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.3 Codex
- $0.1295
- Fits in one request
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
Ornith-1.0-397B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.3 Codex
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Ornith-1.0-397B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
GPT-5.3 Codex has no published cached-input rate, so cached tokens use its listed input rate. Ornith-1.0-397B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.3 CodexOrnith-1.0-397B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.3 Codex
Ornith-1.0-397B
256K
API model ID
GPT-5.3 Codex
gpt-5.3-codex
OpenAI GPT-5.3 Codex model documentationOrnith-1.0-397B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.3 Codex
Not published
Ornith-1.0-397B
No comparable hosted API rate
Documented inputs
GPT-5.3 Codex
text, image
OpenAI model catalogOrnith-1.0-397B
Not sourced
Documented outputs
GPT-5.3 Codex
Ornith-1.0-397B
Not sourced
Provider availability
GPT-5.3 Codex
Generally Available · OpenAI Responses API
OpenAI model catalogOrnith-1.0-397B
Not sourced
Reasoning profile
GPT-5.3 Codex
Reasoning
Ornith-1.0-397B
Reasoning
Weight access
GPT-5.3 Codex
Proprietary
Ornith-1.0-397B
Open Weight
License
GPT-5.3 Codex
Proprietary
Ornith-1.0-397B
Open Weight
Release date
GPT-5.3 Codex
2026-02-05
Ornith-1.0-397B
2026-06-01
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.3 Codex has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence12 rows
Agentic
Terminal-Bench 2.0
GPT-5.3 Codex77.3%SourceOrnith-1.0-397B77.5%SourceOrnith-1.0-397B leads this result
OSWorld-Verified
GPT-5.3 Codex64.7%SourceOrnith-1.0-397B—Not directly comparable
Gert Labs
GPT-5.3 Codex57.47%SourceOrnith-1.0-397B—Not directly comparable
JobBench
GPT-5.3 Codex33.7%SourceOrnith-1.0-397B—Not directly comparable
Claw-Eval
GPT-5.3 Codex—Ornith-1.0-397B77.1%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.3 Codex85%SourceOrnith-1.0-397B82.4%SourceGPT-5.3 Codex leads this result
SWE-bench Pro
GPT-5.3 Codex56.8%SourceOrnith-1.0-397B62.2%SourceOrnith-1.0-397B leads this result
SWE-Rebench
GPT-5.3 Codex58.2%SourceOrnith-1.0-397B—Not directly comparable
Vibe Code Bench
GPT-5.3 Codex61.77%SourceOrnith-1.0-397B—Not directly comparable
SWE Multilingual
GPT-5.3 Codex—Ornith-1.0-397B78.9%SourceNot directly comparable
NL2Repo
GPT-5.3 Codex—Ornith-1.0-397B48.2%SourceNot directly comparable
Terminal-Bench 2.0
GPT-5.3 Codex—Ornith-1.0-397B77.5%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.3 Codex or Ornith-1.0-397B?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.3 Codex or Ornith-1.0-397B?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.3 Codex or Ornith-1.0-397B?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.3 Codex or Ornith-1.0-397B?
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-5.3 Codex or Ornith-1.0-397B?
GPT-5.3 Codex has the larger documented context window: 400K, compared with 256K.
Related comparisons
Last updated August 13, 2026
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