Knowledge
Like-for-like- GPT-5.4
- 57.6
- GPT-5.4 nano
- 43.8
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.4 leads
Model comparison
GPT-5.4 vs GPT-5.4 nano
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
GPT-5.4 has the higher public score estimate, 73.25 versus 65.98, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
13 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-5.4
GPT-5.4 has the larger documented context window.
Confidence: documented
Chat turn cost
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.
Confidence: listed-rates
Cache-heavy agent loop cost
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.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
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.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
24 GPT-5.4 only
13 Shared
- Shared results
- 13
- GPT-5.4 only
- 24
- GPT-5.4 nano only
- 0
- Like-for-like categories
- 2 / 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.
Math
Like-for-like- GPT-5.4
- 42.5
- GPT-5.4 nano
- 21.0
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.4 leads
Agentic
Directional only- GPT-5.4
- 77.2
- GPT-5.4 nano
- 42.9
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Multimodal
Directional only- GPT-5.4
- 73.2
- GPT-5.4 nano
- 66.1
- Weighted basis
- 3 vs 1 rows
- Reading
- Directional only
Coding
Not comparable- GPT-5.4
- 57.7
- GPT-5.4 nano
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4
- 74.0
- GPT-5.4 nano
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.4 | GPT-5.4 nano | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 57.6 | 43.8 | Like-for-like2 vs 2 rows | GPT-5.4 leads |
| Math | 42.5 | 21.0 | Like-for-like2 vs 2 rows | GPT-5.4 leads |
| Agentic | 77.2 | 42.9 | Directional only3 vs 2 rows | Directional only |
| Multimodal | 73.2 | 66.1 | Directional only3 vs 1 rows | Directional only |
| Coding | 57.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 74.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | 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.
OSWorld-Verified
Agentic
Terminal-Bench 2.0
Agentic
FrontierMath v2 (Tiers 1-3)
Math
FrontierMath v2 (Tier 4)
Math
MMMU-Pro
Multimodal
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-5.4
- $0.01
- Fits in one request
- GPT-5.4 nano
- $0.00082
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.4
- $0.17
- Fits in one request
- GPT-5.4 nano
- $0.01375
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.4
- $0.25
- Fits in one request
- GPT-5.4 nano
- $0.0205
- Fits in one request
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.4GPT-5.4 nano
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4
1.05M
OpenAI pricingGPT-5.4 nano
API model ID
GPT-5.4
gpt-5.4
OpenAI pricingGPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationCached-input rate
A 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 pricingDocumented inputs
GPT-5.4
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogDocumented outputs
GPT-5.4
Not sourced
GPT-5.4 nano
Provider availability
GPT-5.4
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GPT-5.4
Reasoning
GPT-5.4 nano
Reasoning
Weight access
GPT-5.4
Proprietary
GPT-5.4 nano
Proprietary
License
GPT-5.4
Proprietary
GPT-5.4 nano
Proprietary
Release date
GPT-5.4
2026-03-05
GPT-5.4 nano
2026-03-17
If you are choosing between sibling variants
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-5.4 has the higher public score estimate, 73.25 versus 65.98, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.17 vs $0.01375. Cache-heavy agent loop: $0.25 vs $0.0205.
- Context tradeoff
- GPT-5.4 has the larger documented window (1.05M).
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 evidence37 rows
Agentic
Terminal-Bench 2.0
GPT-5.475.1%SourceGPT-5.4 nano46.3%SourceGPT-5.4 leads this result
CyberGym
GPT-5.479.0%SourceGPT-5.4 nano—Not directly comparable
BrowseComp
GPT-5.482.7%SourceGPT-5.4 nano—Not directly comparable
OSWorld-Verified
GPT-5.475%SourceGPT-5.4 nano39%SourceGPT-5.4 leads this result
MCP Atlas
GPT-5.470.6%SourceGPT-5.4 nano56.1%SourceGPT-5.4 leads this result
Toolathlon
GPT-5.454.6%SourceGPT-5.4 nano35.5%SourceGPT-5.4 leads this result
τ²-bench results
Shared sourceGPT-5.498.9%GPT-5.4 nano92.5%GPT-5.4 leads this result
Claw-Eval
GPT-5.460.3%SourceGPT-5.4 nano—Not directly comparable
DeepSearchQA
GPT-5.473.6%SourceGPT-5.4 nano—Not directly comparable
Gert Labs
GPT-5.464.89%SourceGPT-5.4 nano—Not directly comparable
ResearchClawBench
GPT-5.415.3%SourceGPT-5.4 nano—Not directly comparable
JobBench
GPT-5.438.9%SourceGPT-5.4 nano—Not directly comparable
ExploitGym
GPT-5.46.0%SourceGPT-5.4 nano—Not directly comparable
Coding
LiveCodeBench Pro
GPT-5.487.5%SourceGPT-5.4 nano—Not directly comparable
SWE-bench Pro
GPT-5.457.7%SourceGPT-5.4 nano—Not directly comparable
React Native Evals
GPT-5.485.3%SourceGPT-5.4 nano—Not directly comparable
Vibe Code Bench
Shared sourceGPT-5.467.42%GPT-5.4 nano26.10%GPT-5.4 leads this result
Reasoning
Knowledge
GPQA
GPT-5.492.8%SourceGPT-5.4 nano82.8%SourceGPT-5.4 leads this result
HLE
GPT-5.452.1%SourceGPT-5.4 nano37.7%SourceGPT-5.4 leads this result
HLE w/o tools
GPT-5.439.8%SourceGPT-5.4 nano24.3%SourceGPT-5.4 leads this result
GPQA-D
GPT-5.492.8%SourceGPT-5.4 nano—Not directly comparable
HealthBench Hard
GPT-5.440.1%SourceGPT-5.4 nano—Not directly comparable
MedXpertQA (Text)
GPT-5.459.6%SourceGPT-5.4 nano—Not directly comparable
HealthBench Professional
GPT-5.448.1%SourceGPT-5.4 nano—Not directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.447.600%GPT-5.4 nano25.860%GPT-5.4 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.427.100%GPT-5.4 nano6.250%GPT-5.4 leads this result
Multimodal
MMMU-Pro
GPT-5.481.2%SourceGPT-5.4 nano66.1%SourceGPT-5.4 leads this result
OfficeQA Pro
GPT-5.453.2%SourceGPT-5.4 nano—Not directly comparable
MMMU-Pro w/ Python
GPT-5.482.1%SourceGPT-5.4 nano69.5%SourceGPT-5.4 leads this result
CharXiv
GPT-5.482.8%SourceGPT-5.4 nano—Not directly comparable
ERQA
GPT-5.465.4%SourceGPT-5.4 nano—Not directly comparable
SimpleVQA
GPT-5.461.1%SourceGPT-5.4 nano—Not directly comparable
ScreenSpot Pro
GPT-5.485.4%SourceGPT-5.4 nano—Not directly comparable
ZeroBench
GPT-5.441.0%SourceGPT-5.4 nano—Not directly comparable
MedXpertQA (MM)
GPT-5.477.1%SourceGPT-5.4 nano—Not directly comparable
Frequently asked questions
Which is better, GPT-5.4 or GPT-5.4 nano?
GPT-5.4 has the higher public score estimate, 73.25 versus 65.98, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.4 or GPT-5.4 nano?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.4 or GPT-5.4 nano?
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-5.4 or GPT-5.4 nano?
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.
Which has the larger context window, GPT-5.4 or GPT-5.4 nano?
GPT-5.4 has the larger documented context window: 1.05M, compared with 400K.
Related comparisons
Last updated July 30, 2026
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