Model comparison
GPT-5.6 Sol vs MiniMax M1
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 39.7 on the Noometry Index. MiniMax M1 costs 9.4× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MiniMaxSummary
- They share 17 benchmarks with published results for both. GPT-5.6 Sol scores higher in 8 categories and MiniMax M1 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.5 to 34.8.
- MiniMax M1 is cheaper at $0.40 / $2.20 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 1M.
- MiniMax M1 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | MiniMax M1 | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 64.4 | 39.7 |
| Released | 2026-07-09 | 2025-06-13 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 1M |
| Max output | 128K | 40K |
| Input $ / M tokens | $4 | $0.40 |
| Output $ / M tokens | $20 | $2.20 |
| Results tracked | 64 | 18 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 61.1 (#7), MiniMax M1: 39.2 (#149)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Coding | 1493 | 1359 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1619 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| WeirdML | 89.4% | — |
| MirrorCode | 20% | — |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Sol: 50.2 (#7), MiniMax M1: —
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.7 (#6), MiniMax M1: 25.8 (#125)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Hard Prompts | 1481 | 1340 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| DTBench | 96% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 161.99 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.5 (#7), MiniMax M1: 34.8 (#152)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Math | 1472 | 1360 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#16), MiniMax M1: 35.7 (#168)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Expert | 1517 | 1316 |
| GPQA Diamond | 93.5% | — |
| SimpleQA Verified | 69.7% | — |
| Vectara Hallucination Rate | 12.4% | — |
Multimodal Not comparable
GPT-5.6 Sol: 48.9 (#8), MiniMax M1: —
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.4 (#32), MiniMax M1: 46.0 (#157)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Non-English | 1450 | 1320 |
| LMArena Chinese | 1523 | 1361 |
| LMArena French | 1475 | 1369 |
| LMArena German | 1476 | 1350 |
| LMArena Japanese | 1475 | 1220 |
| LMArena Korean | 1434 | 1263 |
| LMArena Russian | 1466 | 1330 |
| LMArena Spanish | 1449 | 1354 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.8 (#16), MiniMax M1: 69.5 (#169)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Instruction Following | 1480 | 1312 |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.3 (#42), MiniMax M1: 41.4 (#136)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Longer Query | 1479 | 1326 |
| Fiction.LiveBench | — | 69.4% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.5 (#12), MiniMax M1: 53.3 (#155)
| Benchmark | GPT-5.6 Sol | MiniMax M1 |
|---|---|---|
| LMArena Text | 1456 | 1343 |
| LMArena Creative Writing | 1450 | 1298 |
| LMArena Multi-Turn | 1456 | 1335 |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than MiniMax M1?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 39.7 on the Noometry Index. MiniMax M1 costs 9.4× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Sol or MiniMax M1?
MiniMax M1 is cheaper. It lists at $0.40 per million input tokens and $2.20 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or MiniMax M1 better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 39.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 1M.
How many benchmarks do GPT-5.6 Sol and MiniMax M1 share?
17 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and MiniMax M1 has 18.