Model comparison
GPT-5 vs MiniMax-M2.5
GPT-5 is the stronger model overall, scoring 50.9 to 38.3 on the Noometry Index. MiniMax-M2.5 costs 6.5× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
MiniMaxSummary
- They share 28 benchmarks with published results for both. GPT-5 scores higher in 9 categories and MiniMax-M2.5 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 37.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 72.7% for GPT-5 and 55.2% for MiniMax-M2.5.
- MiniMax-M2.5 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-5 accepts more context: 400K tokens versus 205K.
- MiniMax-M2.5 has downloadable open weights; the other is API-only.
Side by side
| GPT-5 | MiniMax-M2.5 | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 50.9 | 38.3 |
| Released | 2025-08-07 | 2026-02-12 |
| Weights | Proprietary | Open |
| Context window | 400K | 205K |
| Max output | 128K | 131K |
| Input $ / M tokens | $1.25 | $0.30 |
| Output $ / M tokens | $10 | $1.20 |
| Results tracked | 69 | 33 |
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Category by category
Coding GPT-5 leads
GPT-5: 50.3 (#47), MiniMax-M2.5: 48.1 (#58)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| SWE-bench Verified (bash only) | 65% | 75.8% |
| LMArena WebDev | 1418 | 1387 |
| LMArena Coding | 1436 | 1381 |
| ALE-Bench | 1,162 | 618.17 |
| SWE-bench Verified | 73.6% | — |
| Aider Polyglot | 88% | — |
| SWE-bench Multilingual | — | 68.3% |
| SciCode | 42.9% | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use GPT-5 leads
GPT-5: 33.1 (#56), MiniMax-M2.5: 30.4 (#77)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| Terminal-Bench | 49.6% | 42.7% |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
| Vending-Bench 2 | — | -23.16 |
Reasoning GPT-5 leads
GPT-5: 38.3 (#64), MiniMax-M2.5: 17.5 (#292)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| ARC-AGI-2 | 9.9% | 4.9% |
| Kagi LLM Benchmark | 72.7% | 55.2% |
| ARC-AGI-1 | 65.7% | 63.7% |
| LMArena Hard Prompts | 1416 | 1372 |
| Epoch Capabilities Index | 150 | 146.68 |
| SimpleBench | 56.7% | — |
| NYT Connections (extended) | — | 16.8% |
| CritPt | 12.6% | — |
| Chess Puzzles | 37% | — |
| EnigmaEval | 10.5% | — |
| EBR-Bench | 12.7% | — |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.7% | — |
| LMCA | 40% | — |
| ForecastBench | 61.4 | — |
Math GPT-5 leads
GPT-5: 55.0 (#44), MiniMax-M2.5: 26.9 (#253)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| ProofBench | 18% | 4% |
| LMArena Math | 1407 | 1378 |
| FrontierMath (Tiers 1-3) | 55.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 91.4% | — |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5 leads
GPT-5: 56.6 (#43), MiniMax-M2.5: 39.2 (#135)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| Vectara Hallucination Rate | 14.7% | 9.1% |
| LMArena Expert | 1419 | 1379 |
| GPQA Diamond | 86.2% | — |
| Humanity's Last Exam | 25.3% | — |
| SimpleQA Verified | 50.1% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal Not comparable
GPT-5: 46.8 (#13), MiniMax-M2.5: —
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| LMArena Vision | 1232 | — |
| GeoBench | 81% | — |
| VPCT | 66% | — |
Multilingual GPT-5 leads
GPT-5: 51.4 (#110), MiniMax-M2.5: 47.1 (#152)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| LMArena Non-English | 1397 | 1338 |
| LMArena Chinese | 1422 | 1393 |
| LMArena French | 1410 | 1362 |
| LMArena German | 1416 | 1362 |
| LMArena Japanese | 1409 | 1171 |
| LMArena Korean | 1360 | 1232 |
| LMArena Russian | 1406 | 1358 |
| LMArena Spanish | 1399 | 1354 |
Instruction Following GPT-5 leads
GPT-5: 73.8 (#113), MiniMax-M2.5: 71.5 (#148)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| LMArena Instruction Following | 1388 | 1353 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), MiniMax-M2.5: 37.5 (#216)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| LMArena Longer Query | 1399 | 1366 |
| Fiction.LiveBench | 97.2% | — |
| CL-bench | — | 11.4% |
| CL-bench Life | — | 6.3% |
Writing & Preference GPT-5 leads
GPT-5: 63.4 (#65), MiniMax-M2.5: 53.9 (#153)
| Benchmark | GPT-5 | MiniMax-M2.5 |
|---|---|---|
| LMArena Text | 1406 | 1359 |
| LMArena Creative Writing | 1365 | 1331 |
| EQ-Bench Creative Writing | 1627 | 1361 |
| LMArena Multi-Turn | 1426 | 1364 |
| Short-Story Creative Writing | 86% | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than MiniMax-M2.5?
GPT-5 is the stronger model overall, scoring 50.9 to 38.3 on the Noometry Index. MiniMax-M2.5 costs 6.5× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GPT-5 or MiniMax-M2.5?
MiniMax-M2.5 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GPT-5 or MiniMax-M2.5 better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 48.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5 does, with 400K tokens against 205K.
How many benchmarks do GPT-5 and MiniMax-M2.5 share?
28 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and MiniMax-M2.5 has 33.