GPT-4o-mini vs GPT-5 Image
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- 6.7
- Output $/M
- $0.60
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GPT-4o-mini has the edge where output tokens cost 16.7x less.
- Shared results
- 0
- GPT-4o-mini only
- 27
- GPT-5 Image only
- 3
- Like-for-like categories
- 0 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-4o-mini has a AA Intelligence Index result, 6.7.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o-mini has a GPQA Diamond result, 44.2%.
Directional only
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o-mini has a MMLU Pro result, 62.7%.
Directional only
Image generation
Design Arena: images
No call
Only GPT-5 Image has a Design Arena: images result, 1191.
Directional only
Long documents
Listed context window
GPT-5 Image
GPT-5 Image takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini
GPT-4o-mini $0.0004, GPT-5 Image $0.015 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini not benchmarked, GPT-5 Image not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- GPT-4o-mini $0.60 against GPT-5 Image $10.00 per million output tokens, 16.7x apart.
- Context window
- GPT-4o-mini takes 128K tokens, GPT-5 Image 400K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
CodingDirectional onlySix of these boards feed the Vibe Coding Index3 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| AA Coding Index | 11.4 | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Vals AI) | 26.4% | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Artificial Analysis run) | 23.4% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | Not measured | Only GPT-4o-mini measured |
| Epoch Capabilities Index | 126.5 | Not measured | Only GPT-4o-mini measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | Not measured | Only GPT-4o-mini measured |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | Not measured | Only GPT-4o-mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only GPT-4o-mini measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| AIME | 11.5% | Not measured | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | 72.6% | Not measured | Only GPT-4o-mini measured |
| MGSM (Vals AI) | 86.2% | Not measured | Only GPT-4o-mini measured |
| AA Math Index | 14.7 | Not measured | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | 14.7% | Not measured | Only GPT-4o-mini measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | Not measured | Only GPT-4o-mini measured |
| CorpFin (Vals AI) | 45.5% | Not measured | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | 60.5% | Not measured | Only GPT-4o-mini measured |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | Not measured | Only GPT-4o-mini measured |
| MMLU Pro (Artificial Analysis run) | 64.8% | Not measured | Only GPT-4o-mini measured |
| MMLU | 81.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| IFBench | 31.0% | Not measured | Only GPT-4o-mini measured |
| DTBench | 54.4% | Not measured | Only GPT-4o-mini measured |
| LMCA | 10.4% | Not measured | Only GPT-4o-mini measured |
| PIQA | 88.7% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 42 boards here | |||
Image generationDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o-mini | GPT-5 Image | Leader |
|---|---|---|---|
| Design Arena: images | Not measured | 1191 | Only GPT-5 Image measured |
| Design Arena: graphic design | Not measured | 1172 | Only GPT-5 Image measured |
| Design Arena: logos | Not measured | 1202 | Only GPT-5 Image measured |
| Neither model has a result on the other 2 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-4o-mini costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-4o-mini | OpenAI GPT-5 Image |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | July 18, 2024 | October 14, 2025 |
| Context window | 128K | 400K |
| Input $/Mtok | $0.15 | $10.00 |
| Output $/Mtok | $0.60 | $10.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o-mini better than GPT-5 Image for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-4o-mini has the edge where output tokens cost 16.7x less.
Which is cheaper, GPT-4o-mini or GPT-5 Image?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 per million output tokens; GPT-5 Image costs $10.00 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o-mini or GPT-5 Image?
GPT-5 Image, with 400K tokens against 128K.
How many benchmarks is the GPT-4o-mini vs GPT-5 Image comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini's coding index rests on 0 independent benchmarks and GPT-5 Image's on 0. Where only one model has a result, the page says so instead of naming a winner.