Agentic
Directional only- Atria Dawn Preview
- 57.9
- Estimated · #31/153
- GPT-5.4
- 52.5
- Supported · #41/153
- Basis
- BenchAlign lane · 11 vs 14 public rows
- Reading
- Directional only
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Evidence status unavailable
90% interval unavailable
Updated September 14, 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.
5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Atria Dawn Preview is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Atria Dawn Preview is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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
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
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Each row shows the public-lane category score for both models: the BenchAlign 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.
| Category | Atria Dawn Preview | GPT-5.4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 57.9Estimated · #31/153 | 52.5Supported · #41/153 | Directional onlyBenchAlign lane · 11 vs 14 public rows | Directional only |
| Coding | 51.6Estimated · #51/152 | 53.9Supported · #42/152 | Directional onlyBenchAlign lane · 2 vs 4 public rows | Directional only |
| Reasoning | Not ranked | 57.2#17/20 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 69.2Supported · #15/183 | Not comparableBenchAlign lane · 0 vs 7 public rows | Not comparable |
| Math | Not ranked | 64.5Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 69.3#20/48 | Not comparableProvisional lane · 0 vs 3 weighted rows | Not comparable |
| Instruction following | Not ranked | 90.6#19/123 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
BrowseComp
Agentic
SWE-bench Pro
Coding
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.
1K fresh input + 500 output tokens
Atria Dawn Preview has no comparable published API token rate.
50K fresh input + 3K output tokens
Atria Dawn Preview has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Atria Dawn Preview has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Atria Dawn Preview
GPT-5.4
1.05M
OpenAI pricingAtria Dawn Preview
Atria-Dawn-Preview
Atria Dawn Preview model cardGPT-5.4
gpt-5.4
OpenAI pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Atria Dawn Preview
No comparable hosted API rate
Atria Dawn Preview model cardGPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingAtria Dawn Preview
Not sourced
GPT-5.4
Not sourced
Atria Dawn Preview
Not sourced
GPT-5.4
Not sourced
Atria Dawn Preview
Not sourced
GPT-5.4
Not sourced
Atria Dawn Preview
Reasoning
GPT-5.4
Reasoning
Atria Dawn Preview
Open Weight
GPT-5.4
Proprietary
Atria Dawn Preview
Open Weight
GPT-5.4
Proprietary
Atria Dawn Preview
2026-09-14
GPT-5.4
2026-03-05
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
AutomationBench
Not directly comparable
BFCL v4
Not directly comparable
CyberGym
Atria Dawn Preview leads this result
DeepSearchQA
Atria Dawn Preview leads this result
BrowseComp
Atria Dawn Preview leads this result
skillsBench
Not directly comparable
MLE-Bench Lite
Not directly comparable
WideResearch
Not directly comparable
τ³-bench results
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
JobBench
Atria Dawn Preview leads this result
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
ExploitGym
Not directly comparable
ApprenticeBench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Atria Dawn Preview leads this result
LiveCodeBench Pro
Not directly comparable
React Native Evals
Not directly comparable
Vibe Code Bench
Not directly comparable
GPQA
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA-D
Not directly comparable
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
HealthBench Professional
Not directly comparable
MMMU-Pro
Not directly comparable
OfficeQA Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Not directly comparable
ERQA
Not directly comparable
SimpleVQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
ZeroBench
Not directly comparable
MedXpertQA (MM)
Not directly comparable
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.
GPT-5.4 scores higher for coding on the public lane, 53.9 to 51.6. Atria Dawn Preview is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Atria Dawn Preview scores higher for agentic tasks on the public lane, 57.9 to 52.5. Atria Dawn Preview 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.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
GPT-5.4 has the larger documented context window: 1.05M, compared with 256K.
Last updated September 14, 2026
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