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
GPT-5.1-Codex vs GPT-5.4 nano
Updated October 20, 2026. Rank says GPT-5.1-Codex is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
GPT-5.1-Codex has the higher public point estimate, 50.37 versus 46.83. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- GPT-5.1-Codex only
- 2
- GPT-5.4 nano only
- 21
- Like-for-like categories
- 0 / 8
Estimated: GPT-5.1-Codex · Supported: GPT-5.4 nano. Conditional ranges do not establish rank confidence.How the comparison works
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.
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-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.1-Codex is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.1-Codex is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Which one for a specific job
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.
33.7GPT-5.1-Codex29.5GPT-5.4 nano
Directional only · BenchAlign v5.8
GPT-5.1-Codex 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.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 GPT-5.1-Codex only
2 Shared
21 GPT-5.4 nano only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
LiveCodeBench (Vals)Coding
Normalized gap 1.6GPT-5.1-Codex:85.6%GPT-5.4 nano:84.0%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each 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.
Coding
Directional only- GPT-5.1-Codex
- 33.7
- Estimated · #83/152
- GPT-5.4 nano
- 29.5
- Supported · #99/152
- Basis
- BenchAlign v5.8 lane · 2 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.1-Codex
- 52.0
- Estimated · #74/187
- GPT-5.4 nano
- 39.9
- Supported · #122/187
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.1-Codex
- 85.6
- #49/149
- GPT-5.4 nano
- 93.3
- #11/149
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.4 nano
- 33.0
- Supported · #90/131
- Basis
- BenchAlign v5.8 lane · 2 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.1-Codex
- 72.6
- Unranked · 2 rankable rows
- GPT-5.4 nano
- 40.6
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1-Codex
- 71.3
- Unranked · 1 rankable row
- GPT-5.4 nano
- 52.1
- #49/55
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.4 nano
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.4 nano
- 52.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-5.1-Codex | GPT-5.4 nano | Basis | Reading |
|---|---|---|---|---|
| Coding | 33.7Estimated · #83/152 | 29.5Supported · #99/152 | Directional onlyBenchAlign v5.8 lane · 2 vs 4 public rows | Directional only |
| Knowledge | 52.0Estimated · #74/187 | 39.9Supported · #122/187 | Directional onlyBenchAlign v5.8 lane · 0 vs 5 public rows | Directional only |
| Instruction following | 85.6#49/149 | 93.3#11/149 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 33.0Supported · #90/131 | Not comparableBenchAlign v5.8 lane · 2 vs 6 public rows | Not comparable |
| Reasoning | 72.6Unranked · 2 rankable rows | 40.6Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 71.3Unranked · 1 rankable row | 52.1#49/55 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 52.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 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.
Supported evidence per lane · bars run 0–100Methodology
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-Codex
- $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-Codex
- $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-Codex
- $0.15
- 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.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.1-CodexGPT-5.4 nano
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1-Codex
GPT-5.4 nano
API model ID
GPT-5.1-Codex
gpt-5.1-codex
OpenAI GPT-5.1-Codex model documentationGPT-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-Codex
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex model documentationGPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingDocumented inputs
GPT-5.1-Codex
Not sourced
GPT-5.4 nano
text, image
OpenAI model catalogDocumented outputs
GPT-5.1-Codex
Not sourced
GPT-5.4 nano
Provider availability
GPT-5.1-Codex
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GPT-5.1-Codex
Reasoning
GPT-5.4 nano
Reasoning
Weight access
GPT-5.1-Codex
Proprietary
GPT-5.4 nano
Proprietary
License
GPT-5.1-Codex
Proprietary
GPT-5.4 nano
Proprietary
Release date
GPT-5.1-Codex
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-5.1-Codex has the higher public point estimate, 50.37 versus 46.83. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.0925 vs $0.01375. Cache-heavy agent loop: $0.15 vs $0.0205.
- Context tradeoff
- Both models list 400K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.1-Codex or GPT-5.4 nano?
GPT-5.1-Codex has the higher public point estimate, 50.37 versus 46.83. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.1-Codex or GPT-5.4 nano?
GPT-5.1-Codex scores higher for coding on the public lane, 33.7 to 29.5. GPT-5.1-Codex 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-5.1-Codex or GPT-5.4 nano?
GPT-5.1-Codex is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.1-Codex or GPT-5.4 nano?
For the stated presets, chat costs $0.00625 on GPT-5.1-Codex and $0.00082 on GPT-5.4 nano; repository review costs $0.0925 and $0.01375; the cache-heavy agent loop costs $0.15 and $0.0205. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.1-Codex or GPT-5.4 nano?
Both models list the same context window, 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence25 rows
Agentic
Gert Labs
GPT-5.1-Codex49.68%SourceGPT-5.4 nano—Not directly comparable
JobBench
GPT-5.1-Codex26.2%SourceGPT-5.4 nano—Not directly comparable
Terminal-Bench 2.0
GPT-5.1-Codex—GPT-5.4 nano46.3%SourceNot directly comparable
OSWorld-Verified
GPT-5.1-Codex—GPT-5.4 nano39%SourceNot directly comparable
MCP Atlas
GPT-5.1-Codex—GPT-5.4 nano56.1%SourceNot directly comparable
Toolathlon
GPT-5.1-Codex—GPT-5.4 nano35.5%SourceNot directly comparable
τ²-bench results
GPT-5.1-Codex—GPT-5.4 nano92.5%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.1-Codex—GPT-5.4 nano41.6%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceGPT-5.1-Codex13.12%GPT-5.4 nano26.10%GPT-5.4 nano leads this result
LiveCodeBench (Vals)
GPT-5.1-Codex85.6%SourceGPT-5.4 nano84.0%SourceGPT-5.1-Codex leads this result
Terminal-Bench 2.0
GPT-5.1-Codex—GPT-5.4 nano46.3%SourceNot directly comparable
SWE-bench (Vals)
GPT-5.1-Codex—GPT-5.4 nano69.8%SourceNot directly comparable
Reasoning
Multimodal
ExtractBench (LlamaIndex)
GPT-5.1-Codex—GPT-5.4 nano74.90%SourceNot directly comparable
ParseBench
GPT-5.1-Codex—GPT-5.4 nano43.35%SourceNot directly comparable
MMMU-Pro
GPT-5.1-Codex—GPT-5.4 nano66.1%SourceNot directly comparable
MMMU-Pro w/ Python
GPT-5.1-Codex—GPT-5.4 nano69.5%SourceNot directly comparable
Knowledge
GPQA
GPT-5.1-Codex—GPT-5.4 nano82.8%SourceNot directly comparable
HLE
GPT-5.1-Codex—GPT-5.4 nano37.7%SourceNot directly comparable
HLE w/o tools
GPT-5.1-Codex—GPT-5.4 nano24.3%SourceNot directly comparable
GPQA Diamond (Vals)
GPT-5.1-Codex—GPT-5.4 nano77.5%SourceNot directly comparable
MMLU-Pro (Vals)
GPT-5.1-Codex—GPT-5.4 nano77.2%SourceNot directly comparable
Math
25 public results · 2 shared
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Last updated October 20, 2026