GPT-5 Mini vs MiniMax M2.1
MiniMax M2.1 leads GPT-5 Mini on the Artificial Analysis Intelligence Index, 20.9 to 20.6, and MiniMax M2.1 costs 1.7x less per output token ($1.20 vs $2.00 per million). Updated October 22, 2026; 33 benchmarks have a result for both.
GPT-5 Mini or MiniMax M2.1: which to pick
Only GPT-5 Mini has a coding score, 34.2 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding.
Pick MiniMax M2.1 for output tokens 1.7x cheaper ($1.20 vs $2.00 per million) and work measured by SWE-bench Verified (Vals AI) or Terminal-Bench, where it leads.
- Shared results
- 33
- GPT-5 Mini only
- 68
- MiniMax M2.1 only
- 1
- 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
Only GPT-5 Mini has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiniMax M2.1
MiniMax M2.1 leads, 20.9 against 20.6.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5 Mini
GPT-5 Mini leads, 80.3% against 78.5%.
Same board, both measured
Math
AIME
GPT-5 Mini
GPT-5 Mini leads, 91.5% against 77.9%.
Same board, both measured
Knowledge
MMLU Pro
MiniMax M2.1
MiniMax M2.1 leads, 87.0% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
MiniMax M2.1
MiniMax M2.1 leads, 1201 against 1125.
Same board, both measured
Long documents
Listed context window
GPT-5 Mini
GPT-5 Mini takes 400K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2.1
GPT-5 Mini $0.0013, MiniMax M2.1 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Mini
GPT-5 Mini $0.08, MiniMax M2.1 $0.59.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 Mini 34.2 from 2 benchmarks, MiniMax M2.1 not benchmarked.
- Widest category
- Agentic work, 14.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: 3D: GPT-5 Mini 1036 against MiniMax M2.1 1164, 16.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Mini $2.00 against MiniMax M2.1 $1.20 per million output tokens, 1.7x apart.
- Context window
- GPT-5 Mini takes 400K tokens, MiniMax M2.1 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-5 Mini | Bars | MiniMax M2.1 | Ties |
|---|---|---|---|---|
| Coding | 3 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 2 | ||
| Math | 5 | 0 | ||
| Knowledge | 6 | 2 | ||
| Web design | 0 | 7 | ||
| General | 2 | 1 | ||
| All shared boards | 17 | 16 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| SWE-bench Verified | 59.8% | Not measured | Only GPT-5 Mini measured |
| SWE-bench Multilingual | 39.7% | Not measured | Only GPT-5 Mini measured |
| LiveCodeBench (Vals AI) | 86.6% | 81.8% | GPT-5 Mini |
| SWE-bench Verified (Vals AI) | 60.8% | 74.8% | MiniMax M2.1 |
| Vibe Code Bench (Vals AI) | 14.2% | Not measured | Only GPT-5 Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.8% | 81.0% | GPT-5 Mini |
| SciCode | 39.0% | Not measured | Only GPT-5 Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 28.8% | GPT-5 Mini |
| Terminal-Bench | 34.8% | 36.6% | MiniMax M2.1 |
| WeirdML | 52.7% | Not measured | Only GPT-5 Mini measured |
| ALE-Bench | Not measured | 623.8 | Only MiniMax M2.1 measured |
| LMArena Text: coding | 1406 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: software and IT | 1394 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.6 | 20.9 | MiniMax M2.1 |
| Epoch Capabilities Index | 145.3 | Not measured | Only GPT-5 Mini measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 71.1% | 85.4% | MiniMax M2.1 |
| Vending-Bench 2 | -$31 | Not measured | Only GPT-5 Mini measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.5% | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | 78.5% | GPT-5 Mini |
| MMMU Pro (Vals AI) | 78.9% | Not measured | Only GPT-5 Mini measured |
| GPQA Diamond (Artificial Analysis run) | 82.8% | 83.0% | MiniMax M2.1 |
| Humanity's Last Exam (Artificial Analysis run) | 21.5% | 23.2% | MiniMax M2.1 |
| GPQA Diamond (Epoch AI run) | 75.0% | Not measured | Only GPT-5 Mini measured |
| ARC-AGI | 54.3% | Not measured | Only GPT-5 Mini measured |
| ARC-AGI-2 | 4.4% | Not measured | Only GPT-5 Mini measured |
| Chess puzzles | 24.0% | Not measured | Only GPT-5 Mini measured |
| Mystery game puzzles | 10.0% | Not measured | Only GPT-5 Mini measured |
| VPCT | 40.2% | Not measured | Only GPT-5 Mini measured |
| LMArena Text: hard prompts | 1380 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: expert | 1379 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: hard prompts in English | 1383 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: diagrams | 1202 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: homework | 1210 | Not measured | Only GPT-5 Mini measured |
| Kagi LLM Benchmark | 70.3% | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| AIME | 91.5% | 77.9% | GPT-5 Mini |
| MATH 500 (Vals AI) | 94.8% | 89.0% | GPT-5 Mini |
| MGSM (Vals AI) | 92.6% | 87.8% | GPT-5 Mini |
| AA Math Index | 90.7 | 82.7 | GPT-5 Mini |
| AIME 2025 (Artificial Analysis run) | 90.7% | 82.7% | GPT-5 Mini |
| OTIS Mock AIME | 86.7% | Not measured | Only GPT-5 Mini measured |
| FrontierMath, tiers 1 to 3 | 46.7% | Not measured | Only GPT-5 Mini measured |
| 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% | Not measured | Only GPT-5 Mini measured |
| LMArena Text: math | 1378 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: mathematical work | 1380 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 87.0% | MiniMax M2.1 |
| LegalBench (Vals AI) | 81.8% | 75.5% | GPT-5 Mini |
| CaseLaw (Vals AI) | 68.5% | 55.8% | GPT-5 Mini |
| CorpFin (Vals AI) | 60.2% | 58.9% | GPT-5 Mini |
| TaxEval (Vals AI) | 75.2% | 66.3% | GPT-5 Mini |
| MortgageTax (Vals AI) | 66.9% | Not measured | Only GPT-5 Mini measured |
| MedQA (Vals AI) | 96.1% | 91.2% | GPT-5 Mini |
| MedCode (Vals AI) | 43.0% | 34.1% | GPT-5 Mini |
| MMLU Pro (Artificial Analysis run) | 83.7% | 87.5% | MiniMax M2.1 |
| SimpleQA Verified | 21.6% | Not measured | Only GPT-5 Mini measured |
| LMArena Text: science | 1371 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: medicine and healthcare | 1380 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: legal and government | 1375 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: business and finance | 1358 | Not measured | Only GPT-5 Mini measured |
| FACTS Benchmark Suite | 38.6% | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1125 | 1201 | MiniMax M2.1 |
| Design Arena: UI components | 1103 | 1220 | MiniMax M2.1 |
| Design Arena: game dev | 1129 | 1134 | MiniMax M2.1 |
| Design Arena: data viz | 1131 | 1206 | MiniMax M2.1 |
| Design Arena: 3D | 1036 | 1164 | MiniMax M2.1 |
| Design Arena: all code categories | 1114 | 1190 | MiniMax M2.1 |
| Design Arena: SVG | 1091 | 1131 | MiniMax M2.1 |
| Design Arena: ASCII art | 1125 | 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 index21
| Benchmark | GPT-5 Mini | MiniMax M2.1 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 43.0% | Not measured | Only GPT-5 Mini measured |
| MedScribe (Vals AI) | 80.6% | 80.8% | MiniMax M2.1 |
| IFBench | 75.4% | 69.9% | GPT-5 Mini |
| Long context reasoning (AA-LCR) | 72.3% | 67.7% | GPT-5 Mini |
| DTBench | 80.5% | Not measured | Only GPT-5 Mini measured |
| LMCA | 34.2% | Not measured | Only GPT-5 Mini measured |
| Fiction.LiveBench | 69.4% | Not measured | Only GPT-5 Mini measured |
| Creative writing (Lech Mazur) | 83.1% | Not measured | Only GPT-5 Mini measured |
| LMArena Text | 1373 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: instruction following | 1357 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: creative writing | 1325 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision | 1202 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: English | 1379 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: non-English | 1363 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Chinese | 1386 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: German | 1366 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Japanese | 1341 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Korean | 1308 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: French | 1386 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Spanish | 1354 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Russian | 1362 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: Polish | 1378 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: multi-turn | 1363 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: longer queries | 1355 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: writing and language | 1344 | Not measured | Only GPT-5 Mini measured |
| LMArena Text: entertainment and media | 1340 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: English | 1216 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: Chinese | 1214 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: reading text | 1199 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: humor | 1205 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: captioning | 1217 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: recognizing things | 1226 | Not measured | Only GPT-5 Mini measured |
| LMArena Vision: creative writing | 1181 | Not measured | Only GPT-5 Mini measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Mini | MiniMax M2.1 | 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
MiniMax M2.1 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 | MiniMax M2.1 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | December 23, 2025 |
| Context window | 400K | 205K |
| Input $/Mtok | $0.25 | $0.30 |
| Output $/Mtok | $2.00 | $1.20 |
| Coding index | 34.2 | Not benchmarked |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.08 | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Mini better than MiniMax M2.1 for coding?
Only GPT-5 Mini has a coding score, 34.2 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding. Pick MiniMax M2.1 for output tokens 1.7x cheaper ($1.20 vs $2.00 per million) and work measured by SWE-bench Verified (Vals AI) or Terminal-Bench, where it leads.
Which is cheaper, GPT-5 Mini or MiniMax M2.1?
GPT-5 Mini costs $0.25 per million input tokens and $2.00 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GPT-5 Mini costs $0.08 and MiniMax M2.1 $0.59.
Which has the bigger context window, GPT-5 Mini or MiniMax M2.1?
GPT-5 Mini, with 400K tokens against 205K.
How many benchmarks is the GPT-5 Mini vs MiniMax M2.1 comparison based on?
33 benchmarks have a result for both models as of October 22, 2026. GPT-5 Mini's coding index rests on 2 independent benchmarks and MiniMax M2.1's on 1. Where only one model has a result, the page says so instead of naming a winner.