Long documents
Prompts that approach the documented context limit
GPT-4.1 mini
GPT-4.1 mini has the larger documented context window.
Confidence: documented
GPT-4.1 mini vs GPT-4o mini
Updated November 13, 2026. Rank cannot separate these two. Price, access, and your workload decide. Public scores include evidence status and uncertainty.
Share or export
Decision reading
GPT-4.1 mini has the higher public score estimate, 25.09 versus 24.84, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- API price per 1M tokens
GPT-4.1 mini: $0.40 input / $1.60 output
GPT-4o mini: $0.15 input / $0.60 output
- Context window
GPT-4.1 mini: 1M
GPT-4o mini: 128K
- Released
GPT-4.1 mini: April 14, 2025
GPT-4o mini: July 18, 2024
- Shared results
- 4
- GPT-4.1 mini only
- 4
- GPT-4o mini only
- 1
- Like-for-like categories
- 1 / 8
Supported: GPT-4.1 mini and GPT-4o miniHow 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.
Chat turn cost
1K fresh input + 500 output tokens
GPT-4o mini
GPT-4o mini 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-4o mini
GPT-4o mini 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-4.1 mini and GPT-4o mini are 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-4.1 mini and GPT-4o mini are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-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. GPT-4o mini does not fit this workload in one request. GPT-4.1 mini has no published cached-input rate, so cached tokens use its listed input rate. GPT-4o mini has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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.
17.1GPT-4.1 mini8.0GPT-4o mini
Directional only · BenchAlign v5.8
GPT-4.1 mini 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
Or describe your own job
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 GPT-4.1 mini only
4 Shared
1 GPT-4o mini only
2 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 31.8GPT-4.1 mini:58.2%GPT-4o mini:26.4%MMLU-Pro (Vals)Knowledge
Normalized gap 14.5GPT-4.1 mini:77.2%GPT-4o mini:62.7%
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, reasoning, multimodal, 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.
Knowledge
Like-for-like- GPT-4.1 mini
- 29.2
- Supported · #148/180
- GPT-4o mini
- 7.4
- Supported · #179/180
- Basis
- BenchAlign v5.8 lane · 4 vs 3 public rows
- Reading
- GPT-4.1 mini leads
Coding
Directional only- GPT-4.1 mini
- 17.1
- Estimated · #146/162
- GPT-4o mini
- 8.0
- Estimated · #162/162
- Basis
- BenchAlign v5.8 lane · 2 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-4.1 mini
- 44.2
- #115/157
- GPT-4o mini
- 34.7
- #136/157
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4.1 mini
- Not ranked
- GPT-4o mini
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1 mini
- 23.1
- Estimated · #142/160
- GPT-4o mini
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1 mini
- Not ranked
- GPT-4o mini
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1 mini
- Not ranked
- GPT-4o mini
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1 mini
- 23.4
- Unranked · 1 rankable row
- GPT-4o mini
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 mini | GPT-4o mini | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 29.2Supported · #148/180 | 7.4Supported · #179/180 | Like-for-likeBenchAlign v5.8 lane · 4 vs 3 public rows | GPT-4.1 mini leads |
| Coding | 17.1Estimated · #146/162 | 8.0Estimated · #162/162 | Directional onlyBenchAlign v5.8 lane · 2 vs 2 public rows | Directional only |
| Instruction following | 44.2#115/157 | 34.7#136/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 23.1Estimated · #142/160 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 23.4Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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-4.1 mini
- $0.0012
- Fits in one request
- GPT-4o mini
- $0.00045
- Fits in one request
GPT-4o mini has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4.1 mini
- $0.0248
- Fits in one request
- GPT-4o mini
- $0.0093
- Fits in one request
GPT-4o mini 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-4.1 mini
- $0.104
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-4o mini
- $0.039
- Does not fit in one request
- Cached input priced at the published list-input rate
GPT-4o mini does not fit this workload in one request. GPT-4.1 mini has no published cached-input rate, so cached tokens use its listed input rate. GPT-4o mini has no published cached-input rate, so cached tokens use its listed input rate.
Your workload
Enter a month of tokens to price it at both models’ listed API rates.
- GPT-4.1 mini
- $7.20
- per month
- GPT-4o mini
- $2.70
- per month
Standard listed rates, no batch or volume discounts.
Cached input falls back to the list input rate only where a cached rate is unpublished
Price against score, among all priced models
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
GPT-4.1 miniGPT-4o mini
- 28 cheaper models score at least as high as GPT-4.1 mini. The nearest is Gemini 3.1 Flash-Lite (score 46.6, $1.50 per 1M output tokens).
- 13 models score at least as high as GPT-4o mini for the same price or less. The nearest is MiniMax M3.1 Flash (score 58.7, also $0.60 per 1M output tokens).
Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.
See GPT-4o mini on the full price vs performance chartSpecification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-4.1 miniGPT-4o mini
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1 mini
1M
GPT-4o mini
128K
API model ID
GPT-4.1 mini
Not sourced
GPT-4o 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-4.1 mini
Not published
GPT-4o mini
Not published
Documented inputs
GPT-4.1 mini
text, image
OpenAI model page for gpt-4.1-miniGPT-4o mini
text, image
OpenAI model page for gpt-4o-miniDocumented outputs
GPT-4.1 mini
GPT-4o mini
Provider availability
GPT-4.1 mini
Not sourced
GPT-4o mini
Not sourced
Reasoning profile
GPT-4.1 mini
Non-Reasoning
GPT-4o mini
Non-Reasoning
Weight access
GPT-4.1 mini
Proprietary
GPT-4o mini
Proprietary
License
GPT-4.1 mini
Proprietary
GPT-4o mini
Proprietary
Release date
GPT-4.1 mini
2025-04-14
GPT-4o mini
2024-07-18
If you are considering the documented upgrade path
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-4.1 mini has the higher public score estimate, 25.09 versus 24.84, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0248 vs $0.0093. Cache-heavy agent loop: $0.104 vs $0.039.
- Context tradeoff
- GPT-4.1 mini has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-4.1 mini or GPT-4o mini?
GPT-4.1 mini has the higher public score estimate, 25.09 versus 24.84, 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-4.1 mini or GPT-4o mini?
GPT-4.1 mini scores higher for coding on the public lane, 17.1 to 8. GPT-4.1 mini and GPT-4o mini are 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-4.1 mini or GPT-4o mini?
GPT-4.1 mini and GPT-4o mini are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4.1 mini or GPT-4o mini?
For the stated presets, chat costs $0.0012 on GPT-4.1 mini and $0.00045 on GPT-4o mini; repository review costs $0.0248 and $0.0093; the cache-heavy agent loop costs $0.104 and $0.039. GPT-4o mini does not fit this workload in one request. GPT-4.1 mini has no published cached-input rate, so cached tokens use its listed input rate. GPT-4o mini has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4.1 mini or GPT-4o mini?
GPT-4.1 mini has the larger documented context window: 1M, compared with 128K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence9 rows
Coding
Knowledge
MMLU
GPT-4.1 mini87.5%SourceGPT-4o mini82%SourceGPT-4.1 mini leads this result
GPQA
GPT-4.1 mini64.2%SourceGPT-4o mini—Not directly comparable
GPQA Diamond (Vals)
GPT-4.1 mini67.9%SourceGPT-4o mini44.2%SourceGPT-4.1 mini leads this result
MMLU-Pro (Vals)
GPT-4.1 mini77.2%SourceGPT-4o mini62.7%SourceGPT-4.1 mini leads this result
Instruction following
IFEval
GPT-4.1 mini88.5%SourceGPT-4o mini—Not directly comparable
Math
FrontierMath v2 (Tiers 1-3)
GPT-4.1 mini6.667%SourceGPT-4o mini—Not directly comparable
9 public results · 4 shared
Watch GPT-4.1 mini vs GPT-4o mini
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 7,000+ subscribers.
Last updated November 13, 2026