GPT-6.1 Sol vs MiniMax M2
Updated October 21, 2026. 35 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 35 shared
Decision reading
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 45.3 for MiniMax M2.
Pick MiniMax M2 when output tokens cost 9.8x less.
Model AGPT-6.1 SolOpenAI88.7/1002 independent benchmarksNo cost per solved taskModel BMiniMax M2MiniMax45.3/1002 independent benchmarks$0.702 per solved task
- Shared results
- 35
- GPT-6.1 Sol only
- 60
- MiniMax M2 only
- 14
- Like-for-like categories
- 7 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
GPT-6.1 Sol
GPT-6.1 Sol scores 88.7 against 45.3.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-6.1 Sol
GPT-6.1 Sol leads, 51.8 against 18.6.
Same board, both measured
Web design
Design Arena: websites
GPT-6.1 Sol
GPT-6.1 Sol leads, 1321 against 1144.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2
GPT-6.1 Sol $0.0070, 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-6.1 Sol 88.7 from 2 benchmarks, MiniMax M2 45.3 from 2 benchmarks.
- Widest category
- Coding, 37.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena WebDev: GPT-6.1 Sol 1753 against MiniMax M2 1297, 65.1 points apart on a 0 to 100 scale.
- Output price
- GPT-6.1 Sol $10.00 against MiniMax M2 $1.02 per million output tokens, 9.8x apart.
- Context window
- GPT-6.1 Sol takes 1.05M 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-6.1 Sol | Bars | MiniMax M2 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 6 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 35 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
| Benchmark | GPT-6.1 Sol | MiniMax M2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 61.0% | Only MiniMax M2 measured |
| LiveBench coding | 96.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev | 1753 | 1297 | GPT-6.1 Sol |
| Terminal-Bench 4 (Vals AI) | 55.0% | Not measured | Only GPT-6.1 Sol measured |
| Vibe Code Bench (Vals AI) | 88.9% | Not measured | Only GPT-6.1 Sol measured |
| Code migration (Vals AI) | 65.1% | Not measured | Only GPT-6.1 Sol measured |
| ProgramBench (Vals AI) | 3.0% | Not measured | Only GPT-6.1 Sol measured |
| IOI (Vals AI) | 96.9% | Not measured | Only GPT-6.1 Sol measured |
| SWE-bench Multimodal (Vals AI) | 43.5% | Not measured | Only GPT-6.1 Sol measured |
| CyberBench (Vals AI) | 39.3% | Not measured | Only GPT-6.1 Sol measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 82.6% | Only MiniMax M2 measured |
| SciCode | 55.8% | Not measured | Only GPT-6.1 Sol 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 |
| FrontierCode | 50.2% | Not measured | Only GPT-6.1 Sol measured |
| ALE-Bench | 2743.8 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: coding | 1478 | 1371 | GPT-6.1 Sol |
| LMArena Text: software and IT | 1476 | 1373 | GPT-6.1 Sol |
| LMArena WebDev: React | 1754 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: HTML | 1800 | 1306 | GPT-6.1 Sol |
| LMArena WebDev: from an image | 1702 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Sol | MiniMax M2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 51.8 | 18.6 | GPT-6.1 Sol |
| Epoch Capabilities Index | 168.4 | Not measured | Only GPT-6.1 Sol measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GPT-6.1 Sol | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: agents building mobile apps | 1190 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | 1250 | Not measured | Only GPT-6.1 Sol measured |
| LiveBench research | 83.5% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench long agentic | 73.0% | Not measured | Only GPT-6.1 Sol measured |
| SREBench (Vals AI) | 50.8% | Not measured | Only GPT-6.1 Sol measured |
| Finance Agent (Vals AI) | 52.0% | Not measured | Only GPT-6.1 Sol measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-6.1 Sol measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only GPT-6.1 Sol measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 6.3% | Not measured | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 70.8% | Not measured | Only GPT-6.1 Sol measured |
| BioMysteryBench (Vals AI) | 79.6% | Not measured | Only GPT-6.1 Sol measured |
| Terminal-Bench-Science (Vals AI) | 52.9% | Not measured | Only GPT-6.1 Sol measured |
| Vals Science Index | 59.6% | Not measured | Only GPT-6.1 Sol measured |
| Robo-bench (Vals AI) | 28.3% | Not measured | Only GPT-6.1 Sol measured |
| τ²-Bench Telecom | Not measured | 86.8% | Only MiniMax M2 measured |
| APEX-Agents | 60.0% | Not measured | Only GPT-6.1 Sol measured |
| SpireClimb | 24.7% | Not measured | Only GPT-6.1 Sol measured |
| Vending-Bench 2 | Not measured | $161 | Only MiniMax M2 measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6.1 Sol | MiniMax M2 | Leader |
|---|---|---|---|
| LiveBench reasoning | 92.6% | Not measured | Only GPT-6.1 Sol measured |
| MysteryMechanism (Vals AI) | 46.4% | Not measured | Only GPT-6.1 Sol measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 77.7% | Only MiniMax M2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 52.9% | 13.7% | GPT-6.1 Sol |
| GPQA Diamond (Epoch AI run) | 95.4% | Not measured | Only GPT-6.1 Sol measured |
| ARC-AGI | 98.5% | Not measured | Only GPT-6.1 Sol measured |
| ARC-AGI-2 | 94.2% | Not measured | Only GPT-6.1 Sol measured |
| Chess puzzles | 68.0% | Not measured | Only GPT-6.1 Sol measured |
| Mystery game puzzles | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| EBR-bench | 54.3% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | 1463 | 1357 | GPT-6.1 Sol |
| LMArena Text: expert | 1489 | 1338 | GPT-6.1 Sol |
| LMArena Text: hard prompts in English | 1465 | 1376 | GPT-6.1 Sol |
| LMArena Vision: diagrams | 1306 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: homework | 1356 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6.1 Sol | 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 | 100.0% | Not measured | Only GPT-6.1 Sol measured |
| FrontierMath, tiers 1 to 3 | 93.7% | Not measured | Only GPT-6.1 Sol measured |
| FrontierMath, tier 4 | 100.0% | Not measured | Only GPT-6.1 Sol measured |
| ProofBench | 99.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: math | 1489 | 1351 | GPT-6.1 Sol |
| LMArena Text: mathematical work | 1513 | 1368 | GPT-6.1 Sol |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6.1 Sol | MiniMax M2 | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 48.8% | Not measured | Only GPT-6.1 Sol measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.0% | Only MiniMax M2 measured |
| SimpleQA Verified | 73.9% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: science | 1444 | 1348 | GPT-6.1 Sol |
| LMArena Text: medicine and healthcare | 1433 | 1367 | GPT-6.1 Sol |
| LMArena Text: legal and government | 1471 | 1349 | GPT-6.1 Sol |
| LMArena Text: business and finance | 1445 | 1336 | GPT-6.1 Sol |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-6.1 Sol | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1144 | GPT-6.1 Sol |
| Design Arena: UI components | 1321 | 1136 | GPT-6.1 Sol |
| Design Arena: game dev | 1389 | 1119 | GPT-6.1 Sol |
| Design Arena: data viz | 1278 | 1140 | GPT-6.1 Sol |
| Design Arena: 3D | 1461 | 1098 | GPT-6.1 Sol |
| Design Arena: all code categories | 1345 | 1135 | GPT-6.1 Sol |
| Design Arena: SVG | Not measured | 1098 | Only MiniMax M2 measured |
| LiveBench design | 82.1% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | GPT-6.1 Sol | MiniMax M2 | Leader |
|---|---|---|---|
| Vals Index | 63.9% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench data analysis | 82.7% | Not measured | Only GPT-6.1 Sol measured |
| Public Benefits Bench (Vals AI) | 59.3% | Not measured | Only GPT-6.1 Sol measured |
| SAGE (Vals AI) | 46.5% | Not measured | Only GPT-6.1 Sol measured |
| MedScribe (Vals AI) | 86.5% | Not measured | Only GPT-6.1 Sol measured |
| IFBench | Not measured | 72.3% | Only MiniMax M2 measured |
| Long context reasoning (AA-LCR) | 84.0% | 64.3% | GPT-6.1 Sol |
| DTBench | 96.5% | Not measured | Only GPT-6.1 Sol measured |
| LMCA | 62.8% | Not measured | Only GPT-6.1 Sol measured |
| Furniture assembly | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text | 1443 | 1340 | GPT-6.1 Sol |
| LMArena Text: instruction following | 1466 | 1329 | GPT-6.1 Sol |
| LMArena Text: creative writing | 1425 | 1286 | GPT-6.1 Sol |
| LMArena Vision | 1288 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: English | 1450 | 1364 | GPT-6.1 Sol |
| LMArena Text: non-English | 1433 | 1313 | GPT-6.1 Sol |
| LMArena Text: Chinese | 1487 | 1366 | GPT-6.1 Sol |
| LMArena Text: German | Not measured | 1357 | Only MiniMax M2 measured |
| LMArena Text: French | 1460 | 1336 | GPT-6.1 Sol |
| LMArena Text: Spanish | 1439 | 1326 | GPT-6.1 Sol |
| LMArena Text: Russian | 1457 | 1330 | GPT-6.1 Sol |
| LMArena Text: Polish | Not measured | 1312 | Only MiniMax M2 measured |
| LMArena Text: multi-turn | 1442 | 1361 | GPT-6.1 Sol |
| LMArena Text: longer queries | 1458 | 1331 | GPT-6.1 Sol |
| LMArena Text: writing and language | 1444 | 1295 | GPT-6.1 Sol |
| LMArena Text: entertainment and media | 1426 | 1298 | GPT-6.1 Sol |
| LMArena Vision: English | 1274 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: reading text | 1289 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol | 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-6.1 Sol$0.0070Fits in one request
MiniMax M2$0.0008Fits in one request
MiniMax M2 costs less
Repository review
50K input + 3K output tokens
GPT-6.1 Sol$0.130Fits in one request
MiniMax M2$0.016Fits in one request
MiniMax M2 costs less
Agent loop
200K input + 10K output tokens
GPT-6.1 Sol$0.500Fits in one request
MiniMax M2$0.061Exceeds the context window
MiniMax M2 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-6.1 Sol | MiniMax M2 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | September 29, 2026 | October 23, 2025 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $2.00 | $0.255 |
| Output $/Mtok | $10.00 | $1.02 |
| Coding index | 88.7 | 45.3 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $0.702 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6.1 Sol better than MiniMax M2 for coding?
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 45.3 for MiniMax M2. Pick MiniMax M2 when output tokens cost 9.8x less.
Which is cheaper, GPT-6.1 Sol or MiniMax M2?
GPT-6.1 Sol costs $2.00 per million input tokens and $10.00 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-6.1 Sol or MiniMax M2?
GPT-6.1 Sol, with 1.05M tokens against 205K.
How many benchmarks is the GPT-6.1 Sol vs MiniMax M2 comparison based on?
35 benchmarks have a result for both models as of October 21, 2026. GPT-6.1 Sol's coding index rests on 2 independent benchmarks and MiniMax M2's on 2. Where only one model has a result, the page says so instead of naming a winner.