Knowledge
Like-for-like- GPT-5.4 mini
- 55.6
- Supported · #52/183
- Nemotron 3 Nano Omni 30B A3B
- 38.7
- Supported · #143/183
- Basis
- BenchAlign lane · 5 vs 3 public rows
- Reading
- GPT-5.4 mini leads · intervals overlap
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Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.4 mini has the higher public score estimate, 61.11 versus 40.6, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 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 mini
GPT-5.4 mini has the larger documented context window.
Confidence: documented
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Nemotron 3 Nano Omni 30B A3B 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
GPT-5.4 mini and Nemotron 3 Nano Omni 30B A3B are 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.
4 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 | GPT-5.4 mini | Nemotron 3 Nano Omni 30B A3B | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 55.6Supported · #52/183 | 38.7Supported · #143/183 | Like-for-likeBenchAlign lane · 5 vs 3 public rows | GPT-5.4 mini leads · intervals overlap |
| Agentic | 39.1Estimated · #119/152 | 44.3Estimated · #90/152 | Directional onlyBenchAlign lane · 6 vs 2 public rows | Directional only |
| Coding | 42.7Supported · #104/151 | 40.2Estimated · #116/151 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Multimodal | 57.2#31/48 | 39.3#42/48 | Directional onlyProvisional lane · 1 vs 1 weighted rows | Directional only |
| Instruction following | 89.8#23/123 | 75.9#59/123 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 73.9Unranked · 2 rankable rows | 48.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 44.5Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | 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.
GPQA
Knowledge
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
Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.
50K fresh input + 3K output tokens
Nemotron 3 Nano Omni 30B A3B has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Nemotron 3 Nano Omni 30B A3B 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.
GPT-5.4 mini
Nemotron 3 Nano Omni 30B A3B
256K
GPT-5.4 mini
gpt-5.4-mini
OpenAI GPT-5.4 mini model documentationNemotron 3 Nano Omni 30B A3B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 mini
$0.075 per 1M cached input tokens
OpenAI pricingNemotron 3 Nano Omni 30B A3B
No comparable hosted API rate
GPT-5.4 mini
text, image
OpenAI model catalogNemotron 3 Nano Omni 30B A3B
Not sourced
GPT-5.4 mini
Nemotron 3 Nano Omni 30B A3B
Not sourced
GPT-5.4 mini
Generally Available · OpenAI Responses API
OpenAI model catalogNemotron 3 Nano Omni 30B A3B
Not sourced
GPT-5.4 mini
Reasoning
Nemotron 3 Nano Omni 30B A3B
Reasoning
GPT-5.4 mini
Proprietary
Nemotron 3 Nano Omni 30B A3B
Open Weight
GPT-5.4 mini
Proprietary
Nemotron 3 Nano Omni 30B A3B
Open Weight
GPT-5.4 mini
2026-03-17
Nemotron 3 Nano Omni 30B A3B
2026-04-28
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.
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon
Not directly comparable
τ²-bench results
GPT-5.4 mini leads this result
Terminal-Bench 2.1 (Vals)
Not directly comparable
OSWorld
Not directly comparable
Vibe Code Bench
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
LiveCodeBench v5
Not directly comparable
SciCode
Not directly comparable
GPQA
GPT-5.4 mini leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
MMLU-Pro
Not directly comparable
GPQA-D
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
MMMU
Not directly comparable
MMLongBench-Doc
Not directly comparable
CharXiv
Not directly comparable
ScreenSpot Pro
Not directly comparable
Video-MME (w/o subtitle)
Not directly comparable
AI2D_TEST
Not directly comparable
RefCOCO (avg)
Not directly comparable
IFBench
Not directly comparable
GPT-5.4 mini has the higher public score estimate, 61.11 versus 40.6, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GPT-5.4 mini scores higher for coding on the public lane, 42.7 to 40.2. Nemotron 3 Nano Omni 30B A3B 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.
Nemotron 3 Nano Omni 30B A3B scores higher for agentic tasks on the public lane, 44.3 to 39.1. GPT-5.4 mini and Nemotron 3 Nano Omni 30B A3B are 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 mini has the larger documented context window: 400K, compared with 256K.
Last updated September 10, 2026
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