GPT-5.1-Codex-Mini vs o3
GPT-5.1-Codex-Mini leads o3 on the Artificial Analysis Intelligence Index, 20.4 to 20.2, and GPT-5.1-Codex-Mini costs 4.0x less per output token ($2.00 vs $8.00 per million). Updated October 22, 2026; 16 benchmarks have a result for both.
GPT-5.1-Codex-Mini or o3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Mini for output tokens 4.0x cheaper ($2.00 vs $8.00 per million) and a larger context window (400K against 200K tokens).
- Shared results
- 16
- GPT-5.1-Codex-Mini only
- 6
- o3 only
- 86
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 20.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o3 has a GPQA Diamond result, 84.1%.
Directional only
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
No call
Only o3 has a MMLU Pro result, 85.6%.
Directional only
Web design
Design Arena: websites
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 1111 against 1036.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini $0.0013, o3 $0.0060 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only o3 has the number, $0.571.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, o3 not benchmarked.
- Widest category
- Agentic work, 17.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-5.1-Codex-Mini 62.9% against o3 80.7%, 17.8 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against o3 $8.00 per million output tokens, 4.0x apart.
- Context window
- GPT-5.1-Codex-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-5.1-Codex-Mini | Bars | o3 | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 2 | 0 | ||
| Knowledge | 0 | 1 | ||
| Web design | 4 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 8 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 81.3% | Only o3 measured |
| SWE-bench Verified | Not measured | 58.4% | Only o3 measured |
| SWE-bench Multimodal | Not measured | 36.0% | Only o3 measured |
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Vals AI) | Not measured | 83.9% | Only o3 measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | 80.8% | GPT-5.1-Codex-Mini |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 37.1% | o3 |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| GSO-Bench | Not measured | 8.8% | Only o3 measured |
| WeirdML | Not measured | 52.4% | Only o3 measured |
| ALE-Bench | Not measured | 933.5 | Only o3 measured |
| LMArena Text: coding | Not measured | 1407 | Only o3 measured |
| LMArena Text: software and IT | Not measured | 1415 | Only o3 measured |
| LMArena WebDev: HTML | 1253 | Not measured | Only GPT-5.1-Codex-Mini measured |
| SciCode | Not measured | 9.2% | Only o3 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 20.2 | GPT-5.1-Codex-Mini |
| Epoch Capabilities Index | Not measured | 146.7 | Only o3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | 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 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 63.0% | Only o3 measured |
| BrowseComp-Plus | Not measured | 65.9% | Only o3 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.1% | Only o3 measured |
| MMMU Pro (Vals AI) | Not measured | 80.4% | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 81.3% | 82.7% | o3 |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 20.1% | o3 |
| GPQA Diamond (Epoch AI run) | Not measured | 81.8% | Only o3 measured |
| ARC-AGI | Not measured | 60.8% | Only o3 measured |
| ARC-AGI-2 | Not measured | 6.5% | Only o3 measured |
| SimpleBench | Not measured | 53.1% | Only o3 measured |
| Chess puzzles | Not measured | 37.0% | Only o3 measured |
| Mystery game puzzles | Not measured | 29.0% | Only o3 measured |
| VPCT | Not measured | 52.0% | Only o3 measured |
| LMArena Text: hard prompts | Not measured | 1402 | Only o3 measured |
| LMArena Text: expert | Not measured | 1402 | Only o3 measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only o3 measured |
| LMArena Vision: diagrams | Not measured | 1220 | Only o3 measured |
| LMArena Vision: homework | Not measured | 1234 | Only o3 measured |
| Kagi LLM Benchmark | Not measured | 67.6% | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| AIME | Not measured | 85.3% | Only o3 measured |
| MATH 500 (Vals AI) | Not measured | 94.6% | Only o3 measured |
| MGSM (Vals AI) | Not measured | 91.8% | Only o3 measured |
| AA Math Index | 91.7 | 88.3 | GPT-5.1-Codex-Mini |
| AIME 2025 (Artificial Analysis run) | 91.7% | 88.3% | GPT-5.1-Codex-Mini |
| OTIS Mock AIME | Not measured | 84.4% | Only o3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 33.3% | Only o3 measured |
| MATH level 5 | Not measured | 97.8% | Only o3 measured |
| LMArena Text: math | Not measured | 1426 | Only o3 measured |
| LMArena Text: mathematical work | Not measured | 1423 | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 85.6% | Only o3 measured |
| LegalBench (Vals AI) | Not measured | 83.8% | Only o3 measured |
| CorpFin (Vals AI) | Not measured | 59.7% | Only o3 measured |
| TaxEval (Vals AI) | Not measured | 74.6% | Only o3 measured |
| MortgageTax (Vals AI) | Not measured | 65.7% | Only o3 measured |
| MedQA (Vals AI) | Not measured | 96.1% | Only o3 measured |
| MedCode (Vals AI) | Not measured | 47.3% | Only o3 measured |
| MMLU Pro (Artificial Analysis run) | 82.0% | 85.3% | o3 |
| SimpleQA Verified | Not measured | 49.4% | Only o3 measured |
| GeoBench | Not measured | 74.0% | Only o3 measured |
| LMArena Text: science | Not measured | 1429 | Only o3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1448 | Only o3 measured |
| LMArena Text: legal and government | Not measured | 1428 | Only o3 measured |
| LMArena Text: business and finance | Not measured | 1393 | Only o3 measured |
| FACTS Benchmark Suite | Not measured | 44.4% | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | 1036 | GPT-5.1-Codex-Mini |
| Design Arena: UI components | 1078 | 1017 | GPT-5.1-Codex-Mini |
| Design Arena: game dev | 1092 | 1034 | GPT-5.1-Codex-Mini |
| Design Arena: data viz | 1097 | 1200 | o3 |
| Design Arena: 3D | 988 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | 1093 | 1020 | GPT-5.1-Codex-Mini |
| Design Arena: SVG | 978 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | 1105 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.1-Codex-Mini | o3 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 41.8% | Only o3 measured |
| MedScribe (Vals AI) | Not measured | 76.7% | Only o3 measured |
| IFBench | 67.9% | 71.4% | o3 |
| Long context reasoning (AA-LCR) | 66.7% | 74.7% | o3 |
| DTBench | Not measured | 84.8% | Only o3 measured |
| LMCA | Not measured | 39.7% | Only o3 measured |
| Fiction.LiveBench | Not measured | 88.9% | Only o3 measured |
| Creative writing (Lech Mazur) | Not measured | 83.9% | Only o3 measured |
| CL-bench | Not measured | 17.8% | Only o3 measured |
| LMArena Text | Not measured | 1410 | Only o3 measured |
| LMArena Text: instruction following | Not measured | 1368 | Only o3 measured |
| LMArena Text: creative writing | Not measured | 1359 | Only o3 measured |
| LMArena Vision | Not measured | 1214 | Only o3 measured |
| LMArena Text: English | Not measured | 1417 | Only o3 measured |
| LMArena Text: non-English | Not measured | 1401 | Only o3 measured |
| LMArena Text: Chinese | Not measured | 1437 | Only o3 measured |
| LMArena Text: German | Not measured | 1420 | Only o3 measured |
| LMArena Text: Japanese | Not measured | 1403 | Only o3 measured |
| LMArena Text: Korean | Not measured | 1370 | Only o3 measured |
| LMArena Text: French | Not measured | 1430 | Only o3 measured |
| LMArena Text: Spanish | Not measured | 1395 | Only o3 measured |
| LMArena Text: Russian | Not measured | 1406 | Only o3 measured |
| LMArena Text: Polish | Not measured | 1438 | Only o3 measured |
| LMArena Text: multi-turn | Not measured | 1405 | Only o3 measured |
| LMArena Text: longer queries | Not measured | 1372 | Only o3 measured |
| LMArena Text: writing and language | Not measured | 1372 | Only o3 measured |
| LMArena Text: entertainment and media | Not measured | 1373 | Only o3 measured |
| LMArena Vision: English | Not measured | 1221 | Only o3 measured |
| LMArena Vision: Chinese | Not measured | 1237 | Only o3 measured |
| LMArena Vision: reading text | Not measured | 1218 | Only o3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only o3 measured |
| LMArena Vision: captioning | Not measured | 1210 | Only o3 measured |
| LMArena Vision: recognizing things | Not measured | 1234 | Only o3 measured |
| LMArena Vision: creative writing | Not measured | 1201 | Only o3 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-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-5.1-Codex-Mini costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-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-5.1-Codex-Mini | OpenAI o3 |
|---|---|---|
| Maker | OpenAI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | April 16, 2025 |
| Context window | 400K | 200K |
| Input $/Mtok | $0.25 | $2.00 |
| Output $/Mtok | $2.00 | $8.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | $0.571 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Mini better than o3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for output tokens 4.0x cheaper ($2.00 vs $8.00 per million) and a larger context window (400K against 200K tokens).
Which is cheaper, GPT-5.1-Codex-Mini or o3?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; o3 costs $2.00 and $8.00. Cost per solved task is known for o3 only.
Which has the bigger context window, GPT-5.1-Codex-Mini or o3?
GPT-5.1-Codex-Mini, with 400K tokens against 200K.
How many benchmarks is the GPT-5.1-Codex-Mini vs o3 comparison based on?
16 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and o3's on 0. Where only one model has a result, the page says so instead of naming a winner.