GPT-5.6 Sol Pro vs MiniMax M3
MiniMax M3 costs 16.7x less per output token than GPT-5.6 Sol Pro ($1.20 vs $20.00 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
GPT-5.6 Sol Pro or MiniMax M3: which to pick
Only MiniMax M3 has a coding score, 39.2 on the Vibe Coding Index; GPT-5.6 Sol Pro has none, so the two cannot be ranked on coding.
Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 1.05M tokens) and work measured by APEX-Agents or SimpleBench, where it leads.
- Shared results
- 5
- GPT-5.6 Sol Pro only
- 2
- MiniMax M3 only
- 112
- Like-for-like categories
- 3 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 M3 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only MiniMax M3 has a AA Intelligence Index result, 29.2.
Directional only
Agentic work
AA Agentic Index
No call
Only MiniMax M3 has a AA Agentic Index result, 29.5.
Directional only
Reasoning
GPQA Diamond
No call
Only MiniMax M3 has a GPQA Diamond result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M3 has a MMLU Pro result, 84.2%.
Directional only
Web design
Design Arena: websites
No call
Only MiniMax M3 has a Design Arena: websites result, 1257.
Directional only
Long documents
Listed context window
GPT-5.6 Sol Pro
GPT-5.6 Sol Pro takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M3
GPT-5.6 Sol Pro $0.014, MiniMax M3 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M3 has the number, $0.56.
Directional only
At a glance
- Coding index
- GPT-5.6 Sol Pro not benchmarked, MiniMax M3 39.2 from 6 benchmarks.
- Widest category
- Reasoning, 40.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- Chess puzzles: GPT-5.6 Sol Pro 68.0% against MiniMax M3 13.0%, 55.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol Pro $20.00 against MiniMax M3 $1.20 per million output tokens, 16.7x apart.
- Context window
- GPT-5.6 Sol Pro takes 1.05M tokens, MiniMax M3 1.05M.
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-5.6 Sol Pro | Bars | MiniMax M3 | Ties |
|---|---|---|---|---|
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 5 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index23 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 70.9% | Only MiniMax M3 measured |
| LMArena WebDev | Not measured | 1482 | Only MiniMax M3 measured |
| AA Coding Index | Not measured | 58.6 | Only MiniMax M3 measured |
| LiveCodeBench (Vals AI) | Not measured | 82.2% | Only MiniMax M3 measured |
| SWE-bench Verified (Vals AI) | Not measured | 75.0% | Only MiniMax M3 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 1.0% | Only MiniMax M3 measured |
| Vibe Code Bench (Vals AI) | Not measured | 47.6% | Only MiniMax M3 measured |
| Code migration (Vals AI) | Not measured | 19.9% | Only MiniMax M3 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 53.6% | Only MiniMax M3 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.3% | Only MiniMax M3 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 9.2% | Only MiniMax M3 measured |
| SkillsBench (Vals AI) | Not measured | 51.5% | Only MiniMax M3 measured |
| CyberBench (Vals AI) | Not measured | 65.0% | Only MiniMax M3 measured |
| SciCode | Not measured | 47.1% | Only MiniMax M3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 42.4% | Only MiniMax M3 measured |
| FrontierCode | Not measured | 14.7% | Only MiniMax M3 measured |
| WeirdML | 89.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| ALE-Bench | Not measured | 640.0 | Only MiniMax M3 measured |
| LMArena Text: coding | Not measured | 1470 | Only MiniMax M3 measured |
| LMArena Text: software and IT | Not measured | 1466 | Only MiniMax M3 measured |
| LMArena WebDev: React | Not measured | 1478 | Only MiniMax M3 measured |
| LMArena WebDev: HTML | Not measured | 1455 | Only MiniMax M3 measured |
| LMArena WebDev: from an image | Not measured | 1470 | Only MiniMax M3 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 29.2 | Only MiniMax M3 measured |
| Epoch Capabilities Index | Not measured | 146.9 | Only MiniMax M3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 29.5 | Only MiniMax M3 measured |
| Design Arena: agents building web apps | Not measured | 1200 | Only MiniMax M3 measured |
| Design Arena: agents building full-stack apps | Not measured | 1197 | Only MiniMax M3 measured |
| Design Arena: agents building mobile apps | Not measured | 1169 | Only MiniMax M3 measured |
| Design Arena: agents building Android apps | Not measured | 1158 | Only MiniMax M3 measured |
| Design Arena: agents building games | Not measured | 1159 | Only MiniMax M3 measured |
| Design Arena: HTML slides | Not measured | 1120 | Only MiniMax M3 measured |
| Design Arena: PowerPoint slides | Not measured | 1178 | Only MiniMax M3 measured |
| Design Arena: spreadsheets | Not measured | 1042 | Only MiniMax M3 measured |
| LiveBench research | Not measured | 68.0% | Only MiniMax M3 measured |
| LiveBench browser | Not measured | 8.0% | Only MiniMax M3 measured |
| LiveBench long agentic | Not measured | 12.4% | Only MiniMax M3 measured |
| Finance Agent (Vals AI) | Not measured | 48.3% | Only MiniMax M3 measured |
| Tax Agent Bench (Vals AI) | Not measured | 20.6% | Only MiniMax M3 measured |
| Legal Research Bench (Vals AI) | Not measured | 29.8% | Only MiniMax M3 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 47.8% | Only MiniMax M3 measured |
| τ²-Bench Telecom | Not measured | 88.9% | Only MiniMax M3 measured |
| APEX-Agents | 51.4% | 37.7% | GPT-5.6 Sol Pro |
| OSWorld 2.0 | Not measured | 4.6% | Only MiniMax M3 measured |
| Vending-Bench 2 | Not measured | $2,158 | Only MiniMax M3 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 92.7% | Only MiniMax M3 measured |
| LiveBench reasoning | Not measured | 74.5% | Only MiniMax M3 measured |
| MMMU Pro (Vals AI) | Not measured | 81.2% | Only MiniMax M3 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 92.9% | Only MiniMax M3 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 39.0% | Only MiniMax M3 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 90.9% | Only MiniMax M3 measured |
| SimpleBench | 71.7% | 45.8% | GPT-5.6 Sol Pro |
| Chess puzzles | 68.0% | 13.0% | GPT-5.6 Sol Pro |
| Mystery game puzzles | Not measured | 8.0% | Only MiniMax M3 measured |
| LMArena Text: hard prompts | Not measured | 1447 | Only MiniMax M3 measured |
| LMArena Text: expert | Not measured | 1461 | Only MiniMax M3 measured |
| LMArena Text: hard prompts in English | Not measured | 1455 | Only MiniMax M3 measured |
| LMArena Vision: diagrams | Not measured | 1266 | Only MiniMax M3 measured |
| LMArena Vision: homework | Not measured | 1268 | Only MiniMax M3 measured |
| Neither model has a result on the other 9 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 71.1% | Only MiniMax M3 measured |
| FrontierMath, tier 4 | 80.5% | Not measured | Only GPT-5.6 Sol Pro measured |
| ProofBench | Not measured | 18.0% | Only MiniMax M3 measured |
| LMArena Text: math | Not measured | 1429 | Only MiniMax M3 measured |
| LMArena Text: mathematical work | Not measured | 1448 | Only MiniMax M3 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.2% | Only MiniMax M3 measured |
| LegalBench (Vals AI) | Not measured | 85.4% | Only MiniMax M3 measured |
| CorpFin (Vals AI) | Not measured | 68.1% | Only MiniMax M3 measured |
| TaxEval (Vals AI) | Not measured | 72.7% | Only MiniMax M3 measured |
| MortgageTax (Vals AI) | Not measured | 68.4% | Only MiniMax M3 measured |
| MedCode (Vals AI) | Not measured | 46.3% | Only MiniMax M3 measured |
| LMArena Text: science | Not measured | 1452 | Only MiniMax M3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1441 | Only MiniMax M3 measured |
| LMArena Text: legal and government | Not measured | 1437 | Only MiniMax M3 measured |
| LMArena Text: business and finance | Not measured | 1434 | Only MiniMax M3 measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1257 | Only MiniMax M3 measured |
| Design Arena: UI components | Not measured | 1243 | Only MiniMax M3 measured |
| Design Arena: game dev | Not measured | 1210 | Only MiniMax M3 measured |
| Design Arena: data viz | Not measured | 1236 | Only MiniMax M3 measured |
| Design Arena: 3D | Not measured | 1208 | Only MiniMax M3 measured |
| Design Arena: all code categories | Not measured | 1246 | Only MiniMax M3 measured |
| Design Arena: SVG | Not measured | 1160 | Only MiniMax M3 measured |
| Design Arena: ASCII art | Not measured | 1161 | Only MiniMax M3 measured |
| LiveBench design | Not measured | 66.2% | Only MiniMax M3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.2% | Only MiniMax M3 measured |
| Public Benefits Bench (Vals AI) | Not measured | 64.1% | Only MiniMax M3 measured |
| SAGE (Vals AI) | Not measured | 50.6% | Only MiniMax M3 measured |
| MedScribe (Vals AI) | Not measured | 87.3% | Only MiniMax M3 measured |
| Vals Multimodal Index | Not measured | 60.0% | Only MiniMax M3 measured |
| IFBench | Not measured | 82.9% | Only MiniMax M3 measured |
| Long context reasoning (AA-LCR) | Not measured | 83.0% | Only MiniMax M3 measured |
| DTBench | 96.0% | 78.9% | GPT-5.6 Sol Pro |
| LMCA | 59.2% | 33.7% | GPT-5.6 Sol Pro |
| Surface Evolver Bench | Not measured | 55.0% | Only MiniMax M3 measured |
| LMArena Text | Not measured | 1433 | Only MiniMax M3 measured |
| LMArena Text: instruction following | Not measured | 1433 | Only MiniMax M3 measured |
| LMArena Text: creative writing | Not measured | 1404 | Only MiniMax M3 measured |
| LMArena Vision | Not measured | 1251 | Only MiniMax M3 measured |
| LMArena Text: English | Not measured | 1443 | Only MiniMax M3 measured |
| LMArena Text: non-English | Not measured | 1420 | Only MiniMax M3 measured |
| LMArena Text: Chinese | Not measured | 1466 | Only MiniMax M3 measured |
| LMArena Text: German | Not measured | 1428 | Only MiniMax M3 measured |
| LMArena Text: Japanese | Not measured | 1380 | Only MiniMax M3 measured |
| LMArena Text: Korean | Not measured | 1373 | Only MiniMax M3 measured |
| LMArena Text: French | Not measured | 1444 | Only MiniMax M3 measured |
| LMArena Text: Spanish | Not measured | 1433 | Only MiniMax M3 measured |
| LMArena Text: Russian | Not measured | 1426 | Only MiniMax M3 measured |
| LMArena Text: Polish | Not measured | 1423 | Only MiniMax M3 measured |
| LMArena Text: multi-turn | Not measured | 1443 | Only MiniMax M3 measured |
| LMArena Text: longer queries | Not measured | 1446 | Only MiniMax M3 measured |
| LMArena Text: writing and language | Not measured | 1419 | Only MiniMax M3 measured |
| LMArena Text: entertainment and media | Not measured | 1403 | Only MiniMax M3 measured |
| LMArena Vision: English | Not measured | 1263 | Only MiniMax M3 measured |
| LMArena Vision: Chinese | Not measured | 1311 | Only MiniMax M3 measured |
| LMArena Vision: reading text | Not measured | 1259 | Only MiniMax M3 measured |
| LMArena Vision: humor | Not measured | 1235 | Only MiniMax M3 measured |
| LMArena Vision: recognizing things | Not measured | 1197 | Only MiniMax M3 measured |
| LMArena Vision: creative writing | Not measured | 1234 | Only MiniMax M3 measured |
| LMArena Document | Not measured | 1435 | Only MiniMax M3 measured |
| EQ-Bench 4 | Not measured | 1150 | Only MiniMax M3 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | MiniMax M3 | 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 M3 costs less
Repository review
50K input + 3K output tokens
MiniMax M3 costs less
Agent loop
200K input + 10K output tokens
MiniMax M3 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-5.6 Sol Pro | MiniMax M3 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | May 31, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $4.00 | $0.30 |
| Output $/Mtok | $20.00 | $1.20 |
| Coding index | Not benchmarked | 39.2 |
| Independent measurements behind it | 0 | 6 |
| Cost per solved task | Not computable | $0.56 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol Pro better than MiniMax M3 for coding?
Only MiniMax M3 has a coding score, 39.2 on the Vibe Coding Index; GPT-5.6 Sol Pro has none, so the two cannot be ranked on coding. Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 1.05M tokens) and work measured by APEX-Agents or SimpleBench, where it leads.
Which is cheaper, GPT-5.6 Sol Pro or MiniMax M3?
GPT-5.6 Sol Pro costs $4.00 per million input tokens and $20.00 per million output tokens; MiniMax M3 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M3 only.
Which has the bigger context window, GPT-5.6 Sol Pro or MiniMax M3?
GPT-5.6 Sol Pro, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Sol Pro vs MiniMax M3 comparison based on?
5 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol Pro's coding index rests on 0 independent benchmarks and MiniMax M3's on 6. Where only one model has a result, the page says so instead of naming a winner.