GPT-6 Sol vs MiniMax M2.5
Updated October 21, 2026. 48 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 59.4 for MiniMax M2.5.
Pick MiniMax M2.5 when output tokens cost 10.5x less.
- Shared results
- 48
- GPT-6 Sol only
- 52
- MiniMax M2.5 only
- 19
- Like-for-like categories
- 8 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 Sol
GPT-6 Sol scores 82.8 against 59.4.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-6 Sol
GPT-6 Sol leads, 47.6 against 22.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only MiniMax M2.5 has a GPQA Diamond result, 82.1%.
Directional only
Math
AIME
No call
Only MiniMax M2.5 has a AIME result, 88.8%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M2.5 has a MMLU Pro result, 80.1%.
Directional only
Web design
Design Arena: websites
GPT-6 Sol
GPT-6 Sol leads, 1292 against 1222.
Same board, both measured
Long documents
Listed context window
GPT-6 Sol
GPT-6 Sol takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2.5
GPT-6 Sol $0.0070, MiniMax M2.5 $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.5 has the number, $0.62.
Directional only
At a glance
- Coding index
- GPT-6 Sol 82.8 from 2 benchmarks, MiniMax M2.5 59.4 from 2 benchmarks.
- Widest category
- Agentic work, 90.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Vending-Bench 2: GPT-6 Sol $14,428 against MiniMax M2.5 -$23, 90.2 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Sol $10.00 against MiniMax M2.5 $0.95 per million output tokens, 10.5x apart.
- Context window
- GPT-6 Sol takes 1.05M tokens, MiniMax M2.5 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 Sol | Bars | MiniMax M2.5 | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 7 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 48 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 75.8% | Only MiniMax M2.5 measured |
| SWE-bench Multilingual | Not measured | 68.3% | Only MiniMax M2.5 measured |
| LiveBench coding | 93.1% | Not measured | Only GPT-6 Sol measured |
| LMArena WebDev | 1683 | 1386 | GPT-6 Sol |
| LiveCodeBench (Vals AI) | Not measured | 79.2% | Only MiniMax M2.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 74.2% | Only MiniMax M2.5 measured |
| Terminal-Bench 4 (Vals AI) | 44.4% | Not measured | Only GPT-6 Sol measured |
| Vibe Code Bench (Vals AI) | 87.8% | 14.8% | GPT-6 Sol |
| Code migration (Vals AI) | 57.2% | Not measured | Only GPT-6 Sol measured |
| ProgramBench (Vals AI) | 2.0% | Not measured | Only GPT-6 Sol measured |
| IOI (Vals AI) | 82.6% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 83.2% | Not measured | Only GPT-6 Sol measured |
| CyberBench (Vals AI) | 78.0% | Not measured | Only GPT-6 Sol measured |
| SciCode | 57.6% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only MiniMax M2.5 measured |
| Terminal-Bench | Not measured | 42.7% | Only MiniMax M2.5 measured |
| FrontierCode | 49.3% | Not measured | Only GPT-6 Sol measured |
| ALE-Bench | 2462.0 | 618.2 | GPT-6 Sol |
| LMArena Text: coding | 1448 | 1380 | GPT-6 Sol |
| LMArena Text: software and IT | 1433 | 1383 | GPT-6 Sol |
| LMArena WebDev: React | 1685 | 1374 | GPT-6 Sol |
| LMArena WebDev: HTML | 1685 | 1414 | GPT-6 Sol |
| LMArena WebDev: from an image | 1660 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.6 | 22.8 | GPT-6 Sol |
| Epoch Capabilities Index | 163.7 | 146.6 | GPT-6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1232 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building full-stack apps | 1238 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building mobile apps | 1216 | Not measured | Only GPT-6 Sol measured |
| LiveBench research | 69.3% | Not measured | Only GPT-6 Sol measured |
| LiveBench browser | 92.0% | Not measured | Only GPT-6 Sol measured |
| LiveBench long agentic | 66.9% | Not measured | Only GPT-6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-6 Sol measured |
| Finance Agent (Vals AI) | 49.0% | Not measured | Only GPT-6 Sol measured |
| Tax Agent Bench (Vals AI) | 15.0% | Not measured | Only GPT-6 Sol measured |
| Legal Research Bench (Vals AI) | 28.9% | Not measured | Only GPT-6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 71.5% | Not measured | Only GPT-6 Sol measured |
| BioMysteryBench (Vals AI) | 74.8% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 30.0% | Not measured | Only GPT-6 Sol measured |
| Vals Science Index | 45.0% | Not measured | Only GPT-6 Sol measured |
| τ²-Bench Telecom | Not measured | 95.3% | Only MiniMax M2.5 measured |
| APEX-Agents | 54.3% | Not measured | Only GPT-6 Sol measured |
| SpireClimb | 7.5% | Not measured | Only GPT-6 Sol measured |
| Vending-Bench 2 | $14,428 | -$23 | GPT-6 Sol |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.1% | Only MiniMax M2.5 measured |
| LiveBench reasoning | 88.7% | Not measured | Only GPT-6 Sol measured |
| MysteryMechanism (Vals AI) | 30.2% | Not measured | Only GPT-6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.8% | Only MiniMax M2.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | 20.5% | GPT-6 Sol |
| GPQA Diamond (Epoch AI run) | 94.3% | Not measured | Only GPT-6 Sol measured |
| ARC-AGI | 95.5% | 63.7% | GPT-6 Sol |
| ARC-AGI-2 | 89.6% | 4.9% | GPT-6 Sol |
| Chess puzzles | 56.0% | Not measured | Only GPT-6 Sol measured |
| Mystery game puzzles | 56.0% | Not measured | Only GPT-6 Sol measured |
| EBR-bench | 53.3% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: hard prompts | 1422 | 1372 | GPT-6 Sol |
| LMArena Text: expert | 1437 | 1378 | GPT-6 Sol |
| LMArena Text: hard prompts in English | 1420 | 1384 | GPT-6 Sol |
| LMArena Vision: diagrams | 1270 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: homework | 1286 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| AIME | Not measured | 88.8% | Only MiniMax M2.5 measured |
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-6 Sol measured |
| FrontierMath, tiers 1 to 3 | 89.8% | Not measured | Only GPT-6 Sol measured |
| FrontierMath, tier 4 | 90.0% | Not measured | Only GPT-6 Sol measured |
| ProofBench | 83.0% | 4.0% | GPT-6 Sol |
| LMArena Text: math | 1407 | 1378 | GPT-6 Sol |
| LMArena Text: mathematical work | 1424 | 1378 | GPT-6 Sol |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 80.1% | Only MiniMax M2.5 measured |
| LegalBench (Vals AI) | Not measured | 80.0% | Only MiniMax M2.5 measured |
| CaseLaw (Vals AI) | Not measured | 53.5% | Only MiniMax M2.5 measured |
| CorpFin (Vals AI) | Not measured | 59.6% | Only MiniMax M2.5 measured |
| TaxEval (Vals AI) | Not measured | 68.2% | Only MiniMax M2.5 measured |
| MedQA (Vals AI) | Not measured | 92.5% | Only MiniMax M2.5 measured |
| MedCode (Vals AI) | 47.1% | Not measured | Only GPT-6 Sol measured |
| SimpleQA Verified | 60.7% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: science | 1389 | 1369 | GPT-6 Sol |
| LMArena Text: medicine and healthcare | 1366 | 1357 | GPT-6 Sol |
| LMArena Text: legal and government | 1396 | 1366 | GPT-6 Sol |
| LMArena Text: business and finance | 1380 | 1356 | GPT-6 Sol |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1292 | 1222 | GPT-6 Sol |
| Design Arena: UI components | 1292 | 1172 | GPT-6 Sol |
| Design Arena: game dev | 1313 | 1176 | GPT-6 Sol |
| Design Arena: data viz | 1269 | 1171 | GPT-6 Sol |
| Design Arena: 3D | 1322 | 1170 | GPT-6 Sol |
| Design Arena: all code categories | 1301 | 1207 | GPT-6 Sol |
| Design Arena: SVG | 1318 | 1147 | GPT-6 Sol |
| Design Arena: ASCII art | 1226 | Not measured | Only GPT-6 Sol measured |
| LiveBench design | 82.2% | Not measured | Only GPT-6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | GPT-6 Sol | MiniMax M2.5 | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.2% | Not measured | Only GPT-6 Sol measured |
| Public Benefits Bench (Vals AI) | 56.6% | Not measured | Only GPT-6 Sol measured |
| SAGE (Vals AI) | 44.8% | Not measured | Only GPT-6 Sol measured |
| MedScribe (Vals AI) | 82.0% | Not measured | Only GPT-6 Sol measured |
| IFBench | Not measured | 71.6% | Only MiniMax M2.5 measured |
| Long context reasoning (AA-LCR) | 83.7% | 73.3% | GPT-6 Sol |
| DTBench | 97.3% | Not measured | Only GPT-6 Sol measured |
| LMCA | 59.1% | Not measured | Only GPT-6 Sol measured |
| CL-bench | Not measured | 11.4% | Only MiniMax M2.5 measured |
| Furniture assembly | 58.3% | Not measured | Only GPT-6 Sol measured |
| CL-bench Life | Not measured | 6.3% | Only MiniMax M2.5 measured |
| LMArena Text | 1398 | 1359 | GPT-6 Sol |
| LMArena Text: instruction following | 1413 | 1353 | GPT-6 Sol |
| LMArena Text: creative writing | 1378 | 1331 | GPT-6 Sol |
| LMArena Vision | 1240 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: English | 1396 | 1380 | GPT-6 Sol |
| LMArena Text: non-English | 1388 | 1338 | GPT-6 Sol |
| LMArena Text: Chinese | 1412 | 1393 | GPT-6 Sol |
| LMArena Text: German | 1379 | 1361 | GPT-6 Sol |
| LMArena Text: Japanese | 1377 | 1171 | GPT-6 Sol |
| LMArena Text: Korean | 1336 | 1233 | GPT-6 Sol |
| LMArena Text: French | 1399 | 1363 | GPT-6 Sol |
| LMArena Text: Spanish | 1387 | 1353 | GPT-6 Sol |
| LMArena Text: Russian | 1402 | 1359 | GPT-6 Sol |
| LMArena Text: Polish | 1372 | 1334 | GPT-6 Sol |
| LMArena Text: multi-turn | 1414 | 1364 | GPT-6 Sol |
| LMArena Text: longer queries | 1416 | 1366 | GPT-6 Sol |
| LMArena Text: writing and language | 1389 | 1345 | GPT-6 Sol |
| LMArena Text: entertainment and media | 1373 | 1329 | GPT-6 Sol |
| LMArena Vision: English | 1229 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: reading text | 1258 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: creative writing | 1219 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6 Sol | MiniMax M2.5 | 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 M2.5 costs less
Repository review
50K input + 3K output tokens
MiniMax M2.5 costs less
Agent loop
200K input + 10K output tokens
MiniMax M2.5 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 Sol | MiniMax M2.5 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | September 22, 2026 | February 12, 2026 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $2.00 | $0.27 |
| Output $/Mtok | $10.00 | $0.95 |
| Coding index | 82.8 | 59.4 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $0.62 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Sol better than MiniMax M2.5 for coding?
Pick GPT-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 59.4 for MiniMax M2.5. Pick MiniMax M2.5 when output tokens cost 10.5x less.
Which is cheaper, GPT-6 Sol or MiniMax M2.5?
GPT-6 Sol costs $2.00 per million input tokens and $10.00 per million output tokens; MiniMax M2.5 costs $0.27 and $0.95. Cost per solved task is known for MiniMax M2.5 only.
Which has the bigger context window, GPT-6 Sol or MiniMax M2.5?
GPT-6 Sol, with 1.05M tokens against 205K.
How many benchmarks is the GPT-6 Sol vs MiniMax M2.5 comparison based on?
48 benchmarks have a result for both models as of October 21, 2026. GPT-6 Sol's coding index rests on 2 independent benchmarks and MiniMax M2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.