Math
Like-for-like- GPT-5.1
- 26.4
- GPT-5.4 nano
- 21.0
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.1 leads
Model comparison
GPT-5.1 vs GPT-5.4 nano
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.4 nano has the higher public score estimate, 65.98 versus 52.62, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
3 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.
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. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 GPT-5.1 only
3 Shared
10 GPT-5.4 nano only
- Shared results
- 3
- GPT-5.1 only
- 1
- GPT-5.4 nano only
- 10
- Like-for-like categories
- 1 / 8
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.
Agentic
Not comparable- GPT-5.1
- Not measured
- GPT-5.4 nano
- 42.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.1
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.1
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.1
- Not measured
- GPT-5.4 nano
- 43.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1
- Not measured
- GPT-5.4 nano
- 66.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.1
- Not measured
- GPT-5.4 nano
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.1 | GPT-5.4 nano | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 26.4 | 21.0 | Like-for-like2 vs 2 rows | GPT-5.1 leads |
| Agentic | Not measured | 42.9 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 43.8 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 66.1 | Not comparable0 vs 1 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.
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
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-5.1
- $0.00625
- 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-5.1
- $0.0925
- 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-5.1
- $0.375
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.4 nano
- $0.0205
- Fits in one request
GPT-5.4 nano has the lower modeled cost
GPT-5.1 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-5.1GPT-5.4 nano
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1
400K
GPT-5.4 nano
API model ID
GPT-5.1
Not sourced
GPT-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-5.1
Not published
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
GPT-5.1
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogDocumented outputs
GPT-5.1
Not sourced
GPT-5.4 nano
Provider availability
GPT-5.1
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GPT-5.1
Reasoning
GPT-5.4 nano
Reasoning
Weight access
GPT-5.1
Proprietary
GPT-5.4 nano
Proprietary
License
GPT-5.1
Proprietary
GPT-5.4 nano
Proprietary
Release date
GPT-5.1
2025-11-13
GPT-5.4 nano
2026-03-17
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, 65.98 versus 52.62, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0925 vs $0.01375. Cache-heavy agent loop: $0.375 vs $0.0205.
- Context tradeoff
- Both models list 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 evidence14 rows
Agentic
Gert Labs
GPT-5.141.24%SourceGPT-5.4 nano—Not directly comparable
Terminal-Bench 2.0
GPT-5.1—GPT-5.4 nano46.3%SourceNot directly comparable
OSWorld-Verified
GPT-5.1—GPT-5.4 nano39%SourceNot directly comparable
MCP Atlas
GPT-5.1—GPT-5.4 nano56.1%SourceNot directly comparable
Toolathlon
GPT-5.1—GPT-5.4 nano35.5%SourceNot directly comparable
τ²-bench results
GPT-5.1—GPT-5.4 nano92.5%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceGPT-5.124.61%GPT-5.4 nano26.10%GPT-5.4 nano leads this result
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.131.034%GPT-5.4 nano25.860%GPT-5.1 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.112.500%GPT-5.4 nano6.250%GPT-5.1 leads this result
Multimodal
Frequently asked questions
Which is better, GPT-5.1 or GPT-5.4 nano?
GPT-5.4 nano has the higher public score estimate, 65.98 versus 52.62, 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-5.1 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-5.1 or GPT-5.4 nano?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.1 or GPT-5.4 nano?
For the stated presets, chat costs $0.00625 on GPT-5.1 and $0.00082 on GPT-5.4 nano; repository review costs $0.0925 and $0.01375; the cache-heavy agent loop costs $0.375 and $0.0205. GPT-5.1 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.1 or GPT-5.4 nano?
Both models list the same context window, 400K.
Related comparisons
Last updated July 30, 2026
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