Updated October 29, 2026. Rank says GPT-5.4 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
GPT-5.4 has the higher public score, 66.4 versus 46.15, and the 90% score intervals do not overlap. 17 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
GPT-5.4
GPT-5.4 has the higher public coding point estimate, 47.8 to 30.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GPT-5.4
GPT-5.4 has the higher public agentic point estimate, 54.4 to 33.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 has the larger documented context window.
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GPT-5.4 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
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.
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.
ARC-AGI-2Reasoning
Normalized gap 68.3FrontierMath v2 (Tier 4)Math
Normalized gap 36.8OSWorld-VerifiedAgentic
Normalized gap 36.0FrontierMath v2 (Tiers 1-3)Math
Normalized gap 33.7Terminal-Bench 2.0Agentic
Normalized gap 28.8Each 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.
| Category | GPT-5.4 | GPT-5.4 nano | Basis | Reading |
|---|---|---|---|---|
| Agentic | 54.4Supported · #44/135 | 33.7Supported · #97/135 | Like-for-likeBenchAlign v5.8 lane · 15 vs 6 public rows | GPT-5.4 leads |
| Coding | 47.8Supported · #50/156 | 30.1Supported · #100/156 | Like-for-likeBenchAlign v5.8 lane · 6 vs 4 public rows | GPT-5.4 leads · intervals overlap |
| Knowledge | 68.1Supported · #22/179 | 39.4Supported · #117/179 | Like-for-likeBenchAlign v5.8 lane · 7 vs 5 public rows | GPT-5.4 leads |
| Multimodal | 71.3#35/58 | 52.6#51/58 | Directional onlyProvisional lane · 3 vs 1 weighted rows | Directional only |
| Instruction following | 90.7#25/157 | 93.3#12/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 64.2#19/26 | 40.6Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 82.0Unranked · 2 rankable rows | 52.3Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 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.
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
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
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
1.05M
OpenAI pricingGPT-5.4 nano
GPT-5.4
gpt-5.4
OpenAI pricingGPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingGPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingGPT-5.4
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogGPT-5.4
Not sourced
GPT-5.4 nano
GPT-5.4
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogGPT-5.4
Reasoning
GPT-5.4 nano
Reasoning
GPT-5.4
Proprietary
GPT-5.4 nano
Proprietary
GPT-5.4
Proprietary
GPT-5.4 nano
Proprietary
GPT-5.4
2026-03-05
GPT-5.4 nano
2026-03-17
GPT-5.4 has the higher public score, 66.4 versus 46.15, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
GPT-5.4 has the higher public coding point estimate, 47.8 to 30.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-5.4 has the higher public agentic tasks point estimate, 54.4 to 33.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.01 on GPT-5.4 and $0.00082 on GPT-5.4 nano; repository review costs $0.17 and $0.01375; the cache-heavy agent loop costs $0.25 and $0.0205. Costs use the listed standard API rates.
GPT-5.4 has the larger documented context window: 1.05M, compared with 400K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
GPT-5.4 leads this result
CyberGym
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
GPT-5.4 leads this result
MCP Atlas
GPT-5.4 leads this result
Toolathlon
GPT-5.4 leads this result
τ²-bench results
Shared sourceGPT-5.4 leads this result
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
JobBench
Not directly comparable
ExploitGym
Not directly comparable
ApprenticeBench
Not directly comparable
MazeBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
LiveCodeBench Pro
Not directly comparable
SWE-bench Pro
Not directly comparable
React Native Evals
Not directly comparable
Terminal-Bench 2.0
GPT-5.4 leads this result
Vibe Code Bench
Shared sourceGPT-5.4 leads this result
PostTrainBench v1.1
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
ARC-AGI-2
Shared sourceGPT-5.4 leads this result
ARC-AGI-3
Not directly comparable
ARC-AGI-1
Shared sourceGPT-5.4 leads this result
ParseBench
Shared sourceGPT-5.4 leads this result
MMMU-Pro
GPT-5.4 leads this result
OfficeQA Pro
Not directly comparable
MMMU-Pro w/ Python
GPT-5.4 leads this result
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
ExtractBench (LlamaIndex)
Not directly comparable
GPQA
GPT-5.4 leads this result
HLE
GPT-5.4 leads this result
HLE w/o tools
GPT-5.4 leads this result
GPQA-D
Not directly comparable
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
HealthBench Professional
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.4 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.4 leads this result
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Last updated October 29, 2026
