GPT-4o-mini (2024-07-18) vs MiniMax M2
GPT-4o-mini (2024-07-18) costs 1.7x less per output token than MiniMax M2 ($0.60 vs $1.02 per million). Updated October 23, 2026; 26 benchmarks have a result for both.
GPT-4o-mini (2024-07-18) or MiniMax M2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o-mini (2024-07-18) for output tokens 1.7x cheaper ($0.60 vs $1.02 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 26
- GPT-4o-mini (2024-07-18) only
- 20
- MiniMax M2 only
- 24
- Like-for-like categories
- 5 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
No call
Only MiniMax M2 has a AA Intelligence Index result, 18.6.
Directional only
Web design
Design Arena: websites
No call
Only MiniMax M2 has a Design Arena: websites result, 1143.
Directional only
Long documents
Listed context window
MiniMax M2
MiniMax M2 takes 205K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini (2024-07-18)
GPT-4o-mini (2024-07-18) $0.0004, MiniMax M2 $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2 has the number, $0.702.
Directional only
At a glance
- Coding index
- GPT-4o-mini (2024-07-18) not benchmarked, MiniMax M2 not benchmarked.
- Widest category
- Reasoning, 16.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena Text: expert: GPT-4o-mini (2024-07-18) 1235 against MiniMax M2 1338, 17.2 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini (2024-07-18) $0.60 against MiniMax M2 $1.02 per million output tokens, 1.7x apart.
- Context window
- GPT-4o-mini (2024-07-18) takes 128K tokens, MiniMax M2 205K.
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-4o-mini (2024-07-18) | Bars | MiniMax M2 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 15 | ||
| All shared boards | 0 | 26 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| Aider polyglot | 3.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| SWE-bench Verified | Not measured | 61.0% | Only MiniMax M2 measured |
| LMArena WebDev | Not measured | 1297 | Only MiniMax M2 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 82.6% | Only MiniMax M2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 25.8% | Only MiniMax M2 measured |
| Terminal-Bench | Not measured | 30.0% | Only MiniMax M2 measured |
| WeirdML | 11.8% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: coding | 1289 | 1371 | MiniMax M2 |
| LMArena Text: software and IT | 1291 | 1373 | MiniMax M2 |
| LMArena WebDev: HTML | Not measured | 1306 | Only MiniMax M2 measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 18.6 | Only MiniMax M2 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 86.8% | Only MiniMax M2 measured |
| BALROG | 17.4% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Vending-Bench 2 | Not measured | $161 | Only MiniMax M2 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 77.7% | Only MiniMax M2 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 13.7% | Only MiniMax M2 measured |
| GPQA Diamond (Epoch AI run) | 37.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| SimpleBench | 10.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Chess puzzles | 3.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Mystery game puzzles | 12.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| VPCT | 34.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1267 | 1357 | MiniMax M2 |
| LMArena Text: expert | 1235 | 1338 | MiniMax M2 |
| LMArena Text: hard prompts in English | 1278 | 1376 | MiniMax M2 |
| Kagi LLM Benchmark | Not measured | 57.8% | Only MiniMax M2 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 78.3 | Only MiniMax M2 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 78.3% | Only MiniMax M2 measured |
| OTIS Mock AIME | 6.9% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| FrontierMath, tiers 1 to 3 | 0.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MATH level 5 | 52.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1267 | 1351 | MiniMax M2 |
| LMArena Text: mathematical work | 1271 | 1368 | MiniMax M2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 82.0% | Only MiniMax M2 measured |
| SimpleQA Verified | 8.3% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MMLU | 76.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| GeoBench | 64.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: science | 1286 | 1349 | MiniMax M2 |
| LMArena Text: medicine and healthcare | 1282 | 1367 | MiniMax M2 |
| LMArena Text: legal and government | 1305 | 1349 | MiniMax M2 |
| LMArena Text: business and finance | 1265 | 1336 | MiniMax M2 |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1143 | Only MiniMax M2 measured |
| Design Arena: UI components | Not measured | 1134 | Only MiniMax M2 measured |
| Design Arena: game dev | Not measured | 1118 | Only MiniMax M2 measured |
| Design Arena: data viz | Not measured | 1140 | Only MiniMax M2 measured |
| Design Arena: 3D | Not measured | 1098 | Only MiniMax M2 measured |
| Design Arena: all code categories | Not measured | 1135 | Only MiniMax M2 measured |
| Design Arena: SVG | Not measured | 1100 | Only MiniMax M2 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index015
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | Leader |
|---|---|---|---|
| IFBench | Not measured | 72.3% | Only MiniMax M2 measured |
| Long context reasoning (AA-LCR) | Not measured | 64.3% | Only MiniMax M2 measured |
| Creative writing (Lech Mazur) | 67.2% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text | 1286 | 1340 | MiniMax M2 |
| LMArena Text: instruction following | 1258 | 1329 | MiniMax M2 |
| LMArena Text: creative writing | 1268 | 1287 | MiniMax M2 |
| LMArena Vision | 1066 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: English | 1301 | 1364 | MiniMax M2 |
| LMArena Text: non-English | 1266 | 1313 | MiniMax M2 |
| LMArena Text: Chinese | 1265 | 1366 | MiniMax M2 |
| LMArena Text: German | 1272 | 1354 | MiniMax M2 |
| LMArena Text: Japanese | 1216 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Korean | 1195 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: French | 1297 | 1336 | MiniMax M2 |
| LMArena Text: Spanish | 1276 | 1326 | MiniMax M2 |
| LMArena Text: Russian | 1275 | 1330 | MiniMax M2 |
| LMArena Text: Polish | 1244 | 1311 | MiniMax M2 |
| LMArena Text: multi-turn | 1285 | 1361 | MiniMax M2 |
| LMArena Text: longer queries | 1289 | 1331 | MiniMax M2 |
| LMArena Text: writing and language | 1282 | 1295 | MiniMax M2 |
| LMArena Text: entertainment and media | 1253 | 1298 | MiniMax M2 |
| LMArena Vision: English | 1072 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision: Chinese | 1033 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | MiniMax M2 | 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-4o-mini (2024-07-18) costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini (2024-07-18) costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini (2024-07-18) 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 (2024-07-18) | MiniMax M2 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | October 23, 2025 |
| Context window | 128K | 205K |
| Input $/Mtok | $0.15 | $0.255 |
| Output $/Mtok | $0.60 | $1.02 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.702 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o-mini (2024-07-18) better than MiniMax M2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o-mini (2024-07-18) for output tokens 1.7x cheaper ($0.60 vs $1.02 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-4o-mini (2024-07-18) or MiniMax M2?
GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 per million output tokens; MiniMax M2 costs $0.255 and $1.02. Cost per solved task is known for MiniMax M2 only.
Which has the bigger context window, GPT-4o-mini (2024-07-18) or MiniMax M2?
MiniMax M2, with 205K tokens against 128K.
How many benchmarks is the GPT-4o-mini (2024-07-18) vs MiniMax M2 comparison based on?
26 benchmarks have a result for both models as of October 23, 2026. GPT-4o-mini (2024-07-18)'s coding index rests on 0 independent benchmarks and MiniMax M2's on 1. Where only one model has a result, the page says so instead of naming a winner.