Models · /models/gpt-5-4-nano
GPT-5.4 nano
OpenAI · Closed weights · budget · registry tag 2026 nano
textcodevisiondocumentsearch16 aliases10 official receipts
Build / data stamp
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Data snapshot May 1, 2026Registry verification passed9 providers · 826 tracked modelsPage refreshed May 7, 2026
Model pages expose the current registry snapshot and page stamp so stale deployments are visible without reading the code.
Score passport by benchmark
Each row keeps the benchmark receipt, source family, raw metric, and percentile inside its exact comparable group.
Thin verified coverageThis model currently reads as thin verified coverage across the resolved evidence surface.
Intelligence Index
AA · Chat / text · Composite
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
27.6% percentile inside its comparable group
14Raw benchmark value
Time to first token
AA · Chat / text · Speed / cost
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
2.5% percentile inside its comparable group
102.74sRaw benchmark value
Long Context Reasoning
AA · Long context · Objective
It checks whether long-context claims survive contact with retrieval, memory, or long-document tasks.
20% percentile inside its comparable group
75.6%Raw benchmark value
Text Arena
AR · Chat / text · Human
It tests whether the model is actually useful in normal conversational turns, not just on narrow correctness tasks.
53.5% percentile inside its comparable group
1,320Raw benchmark value
Code Arena
AR · Coding · Human
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
56.7% percentile inside its comparable group
1,393Raw benchmark value
Vision Arena
AR · Vision understanding · Human
It is useful when the model must read charts, UI, screenshots, or visual scenes rather than text alone.
45.5% percentile inside its comparable group
1,147Raw benchmark value
WebDev Arena
AR · Coding · Human
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
56.7% percentile inside its comparable group
1,393Raw benchmark value
Search Arena
AR · Search / tool use · Human
It matters when the model must browse, call tools, and recover useful answers from external systems.
14.8% percentile inside its comparable group
1,133Raw benchmark value
TutorBench
SL · Reasoning / math / science · Rubric
It is one of the cleaner reads on deliberate reasoning strength rather than style or popularity.
60% percentile inside its comparable group
55.3%Raw benchmark value
VTB
SL · Vision understanding · Rubric
It is useful when the model must read charts, UI, screenshots, or visual scenes rather than text alone.
45.5% percentile inside its comparable group
17%Raw benchmark value
PRBench Legal
SL · Professional reasoning · Rubric
Applied legal reasoning on professional-domain tasks.
66.7% percentile inside its comparable group
49%Raw benchmark value
MASK
SL · Safety · Rubric
Whether a model stays honest instead of covertly optimizing against the user.
46.2% percentile inside its comparable group
79.3%Raw benchmark value
MultiNRC
SL · Reasoning / math / science · Rubric
It is one of the cleaner reads on deliberate reasoning strength rather than style or popularity.
40% percentile inside its comparable group
52.1%Raw benchmark value
Multimodal mix
OC · Document understanding · Objective
It matters when the job is reading PDFs, tables, forms, or mixed-layout documents rather than plain chat.
71.4% percentile inside its comparable group
75.4%Raw benchmark value
EnigmaEval
SL · Reasoning / math / science · Rubric
It is one of the cleaner reads on deliberate reasoning strength rather than style or popularity.
60% percentile inside its comparable group
64%Raw benchmark value
VISTA
SL · Vision understanding · Rubric
It is useful when the model must read charts, UI, screenshots, or visual scenes rather than text alone.
71.4% percentile inside its comparable group
79%Raw benchmark value
Terminal-Bench 2.0
TERMINAL-BENCH · Coding · Objective
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
4.3% percentile inside its comparable group
11.5%Raw benchmark value
Debugging
BB · Coding · Rubric
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
10% percentile inside its comparable group
81.2%Raw benchmark value
Security
BB · Coding · Rubric
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
22.2% percentile inside its comparable group
80%Raw benchmark value
Speed throughput
BB · Coding · Speed / cost
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
70% percentile inside its comparable group
227.8 t/sRaw benchmark value
Speed TTFT
BB · Coding · Speed / cost
It tells you whether the model can generate, repair, and reason over code under evaluator pressure rather than marketing examples.
60% percentile inside its comparable group
941.00msRaw benchmark value