Long documents
Prompts that approach the documented context limit
o4-mini
o4-mini has the larger documented context window.
Confidence: documented
GPT-4o vs o4-mini
Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- GPT-4o only
- 2
- o4-mini only
- 7
- Like-for-like categories
- 0 / 8
Estimated: GPT-4o. 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.
Chat turn cost
1K fresh input + 500 output tokens
o4-mini
o4-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
o4-mini
o4-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-4o and o4-mini are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4o and o4-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 does not fit this workload in one request. o4-mini does not fit this workload in one request. GPT-4o has no published cached-input rate, so cached tokens use its listed input rate. o4-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.
—GPT-4o—o4-mini
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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-4o only
7 o4-mini only
1 category rests 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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.
Instruction following
Directional only- GPT-4o
- 39.0
- #121/148
- o4-mini
- 83.9
- #53/148
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4o
- Not ranked
- o4-mini
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-4o
- Not ranked
- o4-mini
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4o
- 59.7
- Unranked · 2 rankable rows
- o4-mini
- 67.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4o
- Not ranked
- o4-mini
- 66.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-4o
- 27.9
- Estimated · #162/184
- o4-mini
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4o
- 30.2
- #16/17
- o4-mini
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4o
- 27.0
- Unranked · 1 rankable row
- o4-mini
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-4o | o4-mini | Basis | Reading |
|---|---|---|---|---|
| Instruction following | 39.0#121/148 | 83.9#53/148 | 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 |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Reasoning | 59.7Unranked · 2 rankable rows | 67.8Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 66.4Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 27.9Estimated · #162/184 | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 3 public rows | Not comparable |
| Multilingual | 30.2#16/17 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 27.0Unranked · 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, 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.
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-4o
- $0.0075
- Fits in one request
- o4-mini
- $0.0033
- Fits in one request
o4-mini has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-4o
- $0.155
- Fits in one request
- o4-mini
- $0.0682
- Fits in one request
o4-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-4o
- $0.65
- Does not fit in one request
- Cached input priced at the published list-input rate
- o4-mini
- $0.286
- Does not fit in one request
- Cached input priced at the published list-input rate
GPT-4o does not fit this workload in one request. o4-mini does not fit this workload in one request. GPT-4o has no published cached-input rate, so cached tokens use its listed input rate. o4-mini has no published cached-input rate, so cached tokens use its listed input rate.
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-4oo4-mini
Context window
Maximum documented context; output-token limits may be lower.
GPT-4o
128K
o4-mini
200K
API model ID
GPT-4o
Not sourced
o4-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-4o
Not published
o4-mini
Not published
Documented inputs
GPT-4o
Not sourced
o4-mini
Not sourced
Documented outputs
GPT-4o
Not sourced
o4-mini
Not sourced
Provider availability
GPT-4o
Not sourced
o4-mini
Not sourced
Reasoning profile
GPT-4o
Non-Reasoning
o4-mini
Reasoning
Weight access
GPT-4o
Proprietary
o4-mini
Proprietary
License
GPT-4o
Proprietary
o4-mini
Proprietary
Release date
GPT-4o
2024-05-13
o4-mini
2025-04-16
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.155 vs $0.0682. Cache-heavy agent loop: $0.65 vs $0.286.
- Context tradeoff
- o4-mini has the larger documented window (200K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-4o or o4-mini?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-4o or o4-mini?
GPT-4o and o4-mini are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-4o or o4-mini?
GPT-4o and o4-mini are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4o or o4-mini?
For the stated presets, chat costs $0.0075 on GPT-4o and $0.0033 on o4-mini; repository review costs $0.155 and $0.0682; the cache-heavy agent loop costs $0.65 and $0.286. GPT-4o does not fit this workload in one request. o4-mini does not fit this workload in one request. GPT-4o has no published cached-input rate, so cached tokens use its listed input rate. o4-mini has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-4o or o4-mini?
o4-mini has the larger documented context window: 200K, 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
9 public results · 0 shared
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Last updated October 17, 2026