Knowledge
Like-for-like- GPT-5.4 nano
- 47.4
- Supported · #97/183
- o3-mini
- 39.7
- Supported · #134/183
- Basis
- BenchAlign lane · 5 vs 2 public rows
- Reading
- GPT-5.4 nano leads · intervals overlap
GPT-5.4 nano vs o3-mini
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.4 nano has the higher public score estimate, 59.57 versus 46.83, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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
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 nano
GPT-5.4 nano 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
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
O3-mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
O3-mini is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
17 GPT-5.4 nano only
1 Shared
4 o3-mini only
- Shared results
- 1
- GPT-5.4 nano only
- 17
- o3-mini only
- 4
- Like-for-like categories
- 1 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
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.
Coding
Directional only- GPT-5.4 nano
- 37.1
- Supported · #126/151
- o3-mini
- 45.3
- Estimated · #88/151
- Basis
- BenchAlign lane · 3 vs 1 public rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.4 nano
- 34.6
- Supported · #133/152
- o3-mini
- Not ranked
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4 nano
- 73.7
- Unranked · 2 rankable rows
- o3-mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 nano
- 43.9
- Unranked · 2 rankable rows
- o3-mini
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 nano
- Not ranked
- o3-mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 nano
- 23.8
- #45/48
- o3-mini
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4 nano
- 93.2
- #9/123
- o3-mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.4 nano | o3-mini | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 47.4Supported · #97/183 | 39.7Supported · #134/183 | Like-for-likeBenchAlign lane · 5 vs 2 public rows | GPT-5.4 nano leads · intervals overlap |
| Coding | 37.1Supported · #126/151 | 45.3Estimated · #88/151 | Directional onlyBenchAlign lane · 3 vs 1 public rows | Directional only |
| Agentic | 34.6Supported · #133/152 | Not ranked | Not comparableBenchAlign lane · 6 vs 0 public rows | Not comparable |
| Reasoning | 73.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 43.9Unranked · 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 |
| Multimodal | 23.8#45/48 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | 93.2#9/123 | 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.
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.
GPQA
Knowledge
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 nano
- $0.00082
- Fits in one request
- o3-mini
- $0.0033
- 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 nano
- $0.01375
- Fits in one request
- o3-mini
- $0.0682
- 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 nano
- $0.0205
- Fits in one request
- o3-mini
- $0.286
- Does not fit in one request
- Cached input priced at the published list-input rate
o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.4 nanoo3-mini
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 nano
o3-mini
200K
API model ID
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationo3-mini
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingo3-mini
Not published
Documented inputs
GPT-5.4 nano
text, image
OpenAI model catalogo3-mini
Not sourced
Documented outputs
GPT-5.4 nano
o3-mini
Not sourced
Provider availability
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogo3-mini
Not sourced
Reasoning profile
GPT-5.4 nano
Reasoning
o3-mini
Reasoning
Weight access
GPT-5.4 nano
Proprietary
o3-mini
Proprietary
License
GPT-5.4 nano
Proprietary
o3-mini
Proprietary
Release date
GPT-5.4 nano
2026-03-17
o3-mini
2025-01-31
If you already use one of these models
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-5.4 nano has the higher public score estimate, 59.57 versus 46.83, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.01375 vs $0.0682. Cache-heavy agent loop: $0.0205 vs $0.286.
- Context tradeoff
- GPT-5.4 nano has the larger documented window (400K).
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 evidence22 rows
Agentic
Terminal-Bench 2.0
GPT-5.4 nano46.3%Sourceo3-mini—Not directly comparable
OSWorld-Verified
GPT-5.4 nano39%Sourceo3-mini—Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%Sourceo3-mini—Not directly comparable
Toolathlon
GPT-5.4 nano35.5%Sourceo3-mini—Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%Sourceo3-mini—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4 nano41.6%Sourceo3-mini—Not directly comparable
Coding
Knowledge
GPQA
GPT-5.4 nano82.8%Sourceo3-mini77.2%SourceGPT-5.4 nano leads this result
HLE
GPT-5.4 nano37.7%Sourceo3-mini—Not directly comparable
HLE w/o tools
GPT-5.4 nano24.3%Sourceo3-mini—Not directly comparable
GPQA Diamond (Vals)
GPT-5.4 nano77.5%Sourceo3-mini—Not directly comparable
MMLU-Pro (Vals)
GPT-5.4 nano77.2%Sourceo3-mini—Not directly comparable
MMLU
GPT-5.4 nano—o3-mini86.9%SourceNot directly comparable
Math
Multimodal
Instruction following
IFEval
GPT-5.4 nano—o3-mini93.9%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.4 nano or o3-mini?
GPT-5.4 nano has the higher public score estimate, 59.57 versus 46.83, 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 nano or o3-mini?
o3-mini scores higher for coding on the public lane, 45.3 to 37.1. O3-mini 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.
Which is better for agentic tasks, GPT-5.4 nano or o3-mini?
O3-mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.4 nano or o3-mini?
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0033 on o3-mini; repository review costs $0.01375 and $0.0682; the cache-heavy agent loop costs $0.0205 and $0.286. o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.4 nano or o3-mini?
GPT-5.4 nano has the larger documented context window: 400K, compared with 200K.
Related comparisons
Last updated September 10, 2026
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