GPT-5 Mini vs o3
Updated October 21, 2026. 86 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick o3 for coding: it scores 70.7 on the Vibe Coding Index against 59.8 for GPT-5 Mini.
Pick GPT-5 Mini when output tokens cost 4.0x less, or when a solved task costs $0.08 against $0.571.
- Shared results
- 86
- GPT-5 Mini only
- 12
- o3 only
- 11
- Like-for-like categories
- 8 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, four public coding benchmarks
o3
o3 scores 70.7 against 59.8.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5 Mini
GPT-5 Mini leads, 20.6 against 20.2.
Same board, both measured
Reasoning
GPQA Diamond
o3
o3 leads, 84.1% against 80.3%.
Same board, both measured
Math
AIME
GPT-5 Mini
GPT-5 Mini leads, 91.5% against 85.3%.
Same board, both measured
Knowledge
MMLU Pro
o3
o3 leads, 85.6% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
GPT-5 Mini
GPT-5 Mini leads, 1125 against 1036.
Same board, both measured
Long documents
Listed context window
GPT-5 Mini
GPT-5 Mini takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Mini
GPT-5 Mini $0.0013, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Mini
GPT-5 Mini $0.08, o3 $0.571.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 Mini 59.8 from 1 benchmark, o3 70.7 from 2 benchmarks.
- Widest category
- Web design, 11.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- SimpleQA Verified: GPT-5 Mini 21.6% against o3 49.4%, 27.8 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Mini $2.00 against o3 $8.00 per million output tokens, 4.0x apart.
- Context window
- GPT-5 Mini takes 400K tokens, o3 200K.
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.
| Category | GPT-5 Mini | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 4 | 3 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 13 | ||
| Math | 8 | 2 | ||
| Knowledge | 3 | 9 | 1 tie | |
| Web design | 4 | 1 | ||
| General | 4 | 29 | ||
| All shared boards | 26 | 59 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index43
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | 59.8% | 58.4% | GPT-5 Mini |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| SWE-bench Multilingual | 39.7% | Not measured | Only GPT-5 Mini measured |
| LiveCodeBench (Vals AI) | 86.6% | 83.9% | GPT-5 Mini |
| SWE-bench Verified (Vals AI) | 60.8% | Not measured | Only GPT-5 Mini measured |
| Vibe Code Bench (Vals AI) | 14.2% | Not measured | Only GPT-5 Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.8% | 80.8% | GPT-5 Mini |
| SciCode | 39.0% | Not measured | Only GPT-5 Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 37.1% | o3 |
| Terminal-Bench | 34.8% | Not measured | Only GPT-5 Mini measured |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | 52.7% | 52.4% | GPT-5 Mini |
| ALE-Bench | Not measured | 933.5 | Only o3 measured |
| LMArena Text: coding | 1406 | 1407 | o3 |
| LMArena Text: software and IT | 1394 | 1415 | o3 |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.6 | 20.2 | GPT-5 Mini |
| Epoch Capabilities Index | 145.3 | 146.7 | o3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 71.1% | 80.7% | o3 |
| METR time horizons | Not measured | 65.4% | Only o3 measured |
| GDPval | Not measured | 30.8% | Only o3 measured |
| DeepResearch Bench | Not measured | 45.2% | Only o3 measured |
| OSWorld | Not measured | 23.0% | Only o3 measured |
| Vending-Bench 2 | -$31 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index213
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | 84.1% | o3 |
| MMMU Pro (Vals AI) | 78.9% | 80.4% | o3 |
| GPQA Diamond (Artificial Analysis run) | 82.8% | 82.7% | GPT-5 Mini |
| Humanity's Last Exam (Artificial Analysis run) | 21.5% | 20.1% | GPT-5 Mini |
| GPQA Diamond (Epoch AI run) | 75.0% | 81.8% | o3 |
| ARC-AGI | 54.3% | 60.8% | o3 |
| ARC-AGI-2 | 4.4% | 6.5% | o3 |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | 24.0% | 37.0% | o3 |
| Mystery game puzzles | 10.0% | 29.0% | o3 |
| VPCT | 40.2% | 52.0% | o3 |
| LMArena Text: hard prompts | 1380 | 1402 | o3 |
| LMArena Text: expert | 1379 | 1402 | o3 |
| LMArena Text: hard prompts in English | 1383 | 1411 | o3 |
| LMArena Vision: diagrams | 1202 | 1220 | o3 |
| LMArena Vision: homework | 1210 | 1234 | o3 |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index82
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| AIME | 91.5% | 85.3% | GPT-5 Mini |
| MATH 500 (Vals AI) | 94.8% | 94.6% | GPT-5 Mini |
| MGSM (Vals AI) | 92.6% | 91.8% | GPT-5 Mini |
| AA Math Index | 90.7 | 88.3 | GPT-5 Mini |
| AIME 2025 (Artificial Analysis run) | 90.7% | 88.3% | GPT-5 Mini |
| OTIS Mock AIME | 86.7% | 84.4% | GPT-5 Mini |
| FrontierMath, tiers 1 to 3 | 46.7% | 33.3% | GPT-5 Mini |
| FrontierMath, tier 4 | 12.2% | Not measured | Only GPT-5 Mini measured |
| ProofBench | 9.0% | Not measured | Only GPT-5 Mini measured |
| MATH level 5 | 97.8% | 97.8% | GPT-5 Mini |
| LMArena Text: math | 1378 | 1426 | o3 |
| LMArena Text: mathematical work | 1380 | 1423 | o3 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index39
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 85.6% | o3 |
| LegalBench (Vals AI) | 81.8% | 83.8% | o3 |
| CaseLaw (Vals AI) | 68.5% | Not measured | Only GPT-5 Mini measured |
| CorpFin (Vals AI) | 60.2% | 59.7% | GPT-5 Mini |
| TaxEval (Vals AI) | 75.2% | 74.6% | GPT-5 Mini |
| MortgageTax (Vals AI) | 66.9% | 65.7% | GPT-5 Mini |
| MedQA (Vals AI) | 96.1% | 96.1% | Tie |
| MedCode (Vals AI) | 43.0% | 47.3% | o3 |
| MMLU Pro (Artificial Analysis run) | 83.7% | 85.3% | o3 |
| SimpleQA Verified | 21.6% | 49.4% | o3 |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | 1371 | 1429 | o3 |
| LMArena Text: medicine and healthcare | 1380 | 1448 | o3 |
| LMArena Text: legal and government | 1375 | 1428 | o3 |
| LMArena Text: business and finance | 1358 | 1393 | o3 |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1125 | 1036 | GPT-5 Mini |
| Design Arena: UI components | 1106 | 1016 | GPT-5 Mini |
| Design Arena: game dev | 1130 | 1034 | GPT-5 Mini |
| Design Arena: data viz | 1131 | 1200 | o3 |
| Design Arena: 3D | 1037 | Not measured | Only GPT-5 Mini measured |
| Design Arena: all code categories | 1115 | 1021 | GPT-5 Mini |
| Design Arena: SVG | 1091 | Not measured | Only GPT-5 Mini measured |
| Design Arena: ASCII art | 1124 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index429
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Mini | o3 | Leader |
|---|---|---|---|
| Neither model has a result on 5 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-5 Mini costs less
Repository review
50K input + 3K output tokens
GPT-5 Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5 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-5 Mini | OpenAI o3 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 7, 2025 | April 16, 2025 |
| Context window | 400K | 200K |
| Input $/Mtok | $0.25 | $2.00 |
| Output $/Mtok | $2.00 | $8.00 |
| Coding index | 59.8 | 70.7 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.08 | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Mini better than o3 for coding?
Pick o3 for coding: it scores 70.7 on the Vibe Coding Index against 59.8 for GPT-5 Mini. Pick GPT-5 Mini when output tokens cost 4.0x less, or when a solved task costs $0.08 against $0.571.
Which is cheaper, GPT-5 Mini or o3?
GPT-5 Mini costs $0.25 per million input tokens and $2.00 per million output tokens; o3 costs $2.00 and $8.00. Per solved SWE-bench Verified task, GPT-5 Mini costs $0.08 and o3 $0.571.
Which has the bigger context window, GPT-5 Mini or o3?
GPT-5 Mini, with 400K tokens against 200K.
How many benchmarks is the GPT-5 Mini vs o3 comparison based on?
86 benchmarks have a result for both models as of October 21, 2026. GPT-5 Mini's coding index rests on 1 independent benchmark and o3's on 2. Where only one model has a result, the page says so instead of naming a winner.