GPT-4o-mini vs MiniMax M2.7
MiniMax M2.7 leads GPT-4o-mini on the Artificial Analysis Intelligence Index, 22.8 to 6.7, and GPT-4o-mini costs 2.0x less per output token ($0.60 vs $1.20 per million). Updated October 22, 2026; 12 benchmarks have a result for both.
GPT-4o-mini or MiniMax M2.7: which to pick
Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-4o-mini has none, so the two cannot be ranked on coding.
Pick GPT-4o-mini for output tokens 2.0x cheaper ($0.60 vs $1.20 per million).
- Shared results
- 12
- GPT-4o-mini only
- 17
- MiniMax M2.7 only
- 62
- Like-for-like categories
- 6 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
Only MiniMax M2.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiniMax M2.7
MiniMax M2.7 leads, 22.8 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only MiniMax M2.7 has a AA Agentic Index result, 15.3.
Directional only
Reasoning
GPQA Diamond
MiniMax M2.7
MiniMax M2.7 leads, 86.6% against 44.2%.
Same board, both measured
Math
AIME
MiniMax M2.7
MiniMax M2.7 leads, 91.0% against 11.5%.
Same board, both measured
Knowledge
MMLU Pro
MiniMax M2.7
MiniMax M2.7 leads, 80.4% against 62.7%.
Same board, both measured
Web design
Design Arena: websites
No call
Only MiniMax M2.7 has a Design Arena: websites result, 1243.
Directional only
Long documents
Listed context window
MiniMax M2.7
MiniMax M2.7 takes 205K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini
GPT-4o-mini $0.0004, MiniMax M2.7 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.7 has the number, $0.64.
Directional only
At a glance
- Coding index
- GPT-4o-mini not benchmarked, MiniMax M2.7 36.2 from 3 benchmarks.
- Widest category
- Math, 79.6 points apart on average over 1 shared benchmark.
- Widest single gap
- AIME: GPT-4o-mini 11.5% against MiniMax M2.7 91.0%, 79.6 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini $0.60 against MiniMax M2.7 $1.20 per million output tokens, 2.0x apart.
- Context window
- GPT-4o-mini takes 128K tokens, MiniMax M2.7 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 | Bars | MiniMax M2.7 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 3 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1399 | Only MiniMax M2.7 measured |
| AA Coding Index | 11.4 | 52.6 | MiniMax M2.7 |
| LiveCodeBench (Vals AI) | 26.4% | 79.9% | MiniMax M2.7 |
| SWE-bench Verified (Vals AI) | Not measured | 73.8% | Only MiniMax M2.7 measured |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only MiniMax M2.7 measured |
| Code migration (Vals AI) | Not measured | 8.7% | Only MiniMax M2.7 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only MiniMax M2.7 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 48.7% | Only MiniMax M2.7 measured |
| LiveCodeBench (Artificial Analysis run) | 23.4% | Not measured | Only GPT-4o-mini measured |
| SciCode | Not measured | 50.1% | Only MiniMax M2.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 39.4% | Only MiniMax M2.7 measured |
| Terminal-Bench | Not measured | 45.1% | Only MiniMax M2.7 measured |
| WeirdML | Not measured | 37.0% | Only MiniMax M2.7 measured |
| ALE-Bench | Not measured | 599.3 | Only MiniMax M2.7 measured |
| LMArena Text: coding | Not measured | 1454 | Only MiniMax M2.7 measured |
| LMArena Text: software and IT | Not measured | 1446 | Only MiniMax M2.7 measured |
| LMArena WebDev: React | Not measured | 1388 | Only MiniMax M2.7 measured |
| LMArena WebDev: HTML | Not measured | 1401 | Only MiniMax M2.7 measured |
| SciCode | 1.5% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 22.8 | MiniMax M2.7 |
| Epoch Capabilities Index | 126.5 | 145.8 | MiniMax M2.7 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 15.3 | Only MiniMax M2.7 measured |
| Finance Agent (Vals AI) | Not measured | 27.9% | Only MiniMax M2.7 measured |
| Tax Agent Bench (Vals AI) | Not measured | 6.9% | Only MiniMax M2.7 measured |
| Legal Research Bench (Vals AI) | Not measured | 10.6% | Only MiniMax M2.7 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 28.4% | Only MiniMax M2.7 measured |
| τ²-Bench Telecom | Not measured | 84.8% | Only MiniMax M2.7 measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | 86.6% | MiniMax M2.7 |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | 87.4% | MiniMax M2.7 |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 29.6% | MiniMax M2.7 |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: hard prompts | Not measured | 1422 | Only MiniMax M2.7 measured |
| LMArena Text: expert | Not measured | 1443 | Only MiniMax M2.7 measured |
| LMArena Text: hard prompts in English | Not measured | 1441 | Only MiniMax M2.7 measured |
| Kagi LLM Benchmark | 28.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| AIME | 11.5% | 91.0% | MiniMax M2.7 |
| 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 |
| ProofBench | Not measured | 3.0% | Only MiniMax M2.7 measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: math | Not measured | 1421 | Only MiniMax M2.7 measured |
| LMArena Text: mathematical work | Not measured | 1437 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | 80.4% | MiniMax M2.7 |
| LegalBench (Vals AI) | Not measured | 84.0% | Only MiniMax M2.7 measured |
| CaseLaw (Vals AI) | Not measured | 60.9% | Only MiniMax M2.7 measured |
| CorpFin (Vals AI) | 45.5% | 61.2% | MiniMax M2.7 |
| TaxEval (Vals AI) | 60.5% | 66.6% | MiniMax M2.7 |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | Not measured | Only GPT-4o-mini measured |
| MedCode (Vals AI) | Not measured | 34.4% | Only MiniMax M2.7 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 |
| LMArena Text: science | Not measured | 1416 | Only MiniMax M2.7 measured |
| LMArena Text: medicine and healthcare | Not measured | 1409 | Only MiniMax M2.7 measured |
| LMArena Text: legal and government | Not measured | 1403 | Only MiniMax M2.7 measured |
| LMArena Text: business and finance | Not measured | 1407 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1243 | Only MiniMax M2.7 measured |
| Design Arena: UI components | Not measured | 1210 | Only MiniMax M2.7 measured |
| Design Arena: game dev | Not measured | 1209 | Only MiniMax M2.7 measured |
| Design Arena: data viz | Not measured | 1233 | Only MiniMax M2.7 measured |
| Design Arena: 3D | Not measured | 1195 | Only MiniMax M2.7 measured |
| Design Arena: all code categories | Not measured | 1233 | Only MiniMax M2.7 measured |
| Design Arena: SVG | Not measured | 1137 | Only MiniMax M2.7 measured |
| Design Arena: ASCII art | Not measured | 1136 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o-mini | MiniMax M2.7 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 79.9% | Only MiniMax M2.7 measured |
| IFBench | 31.0% | 75.7% | MiniMax M2.7 |
| Long context reasoning (AA-LCR) | Not measured | 78.3% | Only MiniMax M2.7 measured |
| DTBench | 54.4% | Not measured | Only GPT-4o-mini measured |
| LMCA | 10.4% | Not measured | Only GPT-4o-mini measured |
| ExploitBench | Not measured | 13.3% | Only MiniMax M2.7 measured |
| PIQA | 88.7% | Not measured | Only GPT-4o-mini measured |
| LMArena Text | Not measured | 1405 | Only MiniMax M2.7 measured |
| LMArena Text: instruction following | Not measured | 1405 | Only MiniMax M2.7 measured |
| LMArena Text: creative writing | Not measured | 1354 | Only MiniMax M2.7 measured |
| LMArena Text: English | Not measured | 1426 | Only MiniMax M2.7 measured |
| LMArena Text: non-English | Not measured | 1382 | Only MiniMax M2.7 measured |
| LMArena Text: Chinese | Not measured | 1442 | Only MiniMax M2.7 measured |
| LMArena Text: German | Not measured | 1398 | Only MiniMax M2.7 measured |
| LMArena Text: Japanese | Not measured | 1260 | Only MiniMax M2.7 measured |
| LMArena Text: Korean | Not measured | 1314 | Only MiniMax M2.7 measured |
| LMArena Text: French | Not measured | 1424 | Only MiniMax M2.7 measured |
| LMArena Text: Spanish | Not measured | 1402 | Only MiniMax M2.7 measured |
| LMArena Text: Russian | Not measured | 1383 | Only MiniMax M2.7 measured |
| LMArena Text: Polish | Not measured | 1387 | Only MiniMax M2.7 measured |
| LMArena Text: multi-turn | Not measured | 1412 | Only MiniMax M2.7 measured |
| LMArena Text: longer queries | Not measured | 1418 | Only MiniMax M2.7 measured |
| LMArena Text: writing and language | Not measured | 1376 | Only MiniMax M2.7 measured |
| LMArena Text: entertainment and media | Not measured | 1360 | Only MiniMax M2.7 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | MiniMax M2.7 | 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 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 | MiniMax M2.7 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | March 18, 2026 |
| Context window | 128K | 205K |
| Input $/Mtok | $0.15 | $0.30 |
| Output $/Mtok | $0.60 | $1.20 |
| Coding index | Not benchmarked | 36.2 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.64 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o-mini better than MiniMax M2.7 for coding?
Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GPT-4o-mini has none, so the two cannot be ranked on coding. Pick GPT-4o-mini for output tokens 2.0x cheaper ($0.60 vs $1.20 per million).
Which is cheaper, GPT-4o-mini or MiniMax M2.7?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.7 only.
Which has the bigger context window, GPT-4o-mini or MiniMax M2.7?
MiniMax M2.7, with 205K tokens against 128K.
How many benchmarks is the GPT-4o-mini vs MiniMax M2.7 comparison based on?
12 benchmarks have a result for both models as of October 22, 2026. GPT-4o-mini's coding index rests on 0 independent benchmarks and MiniMax M2.7's on 3. Where only one model has a result, the page says so instead of naming a winner.