Model comparison
GPT-5 Mini vs MiniMax-M2.7
GPT-5 Mini is the stronger model overall, scoring 41.7 to 37.4 on the Noometry Index.
Last verified . 25 shared benchmarks.
MiniMaxSummary
- They share 25 benchmarks with published results for both. GPT-5 Mini scores higher in 5 categories and MiniMax-M2.7 in 4 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.3 to 24.9.
- The biggest single-benchmark swing is WeirdML: 52.7% for GPT-5 Mini and 37% for MiniMax-M2.7.
- MiniMax-M2.7 is cheaper at $0.30 / $1.20 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- GPT-5 Mini accepts more context: 400K tokens versus 205K.
- MiniMax-M2.7 has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Mini | MiniMax-M2.7 | |
|---|---|---|
| Provider | OpenAI | MiniMax |
| Noometry Index | 41.7 | 37.4 |
| Released | 2025-08-07 | 2026-03-18 |
| Weights | Proprietary | Open |
| Context window | 400K | 205K |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.25 | $0.30 |
| Output $ / M tokens | $2 | $1.20 |
| Results tracked | 60 | 30 |
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Category by category
Coding MiniMax-M2.7 leads
GPT-5 Mini: 40.0 (#134), MiniMax-M2.7: 41.4 (#115)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| SciCode | 39.2% | 47% |
| WeirdML | 52.7% | 37% |
| LMArena Coding | 1406 | 1454 |
| ALE-Bench | 799.77 | 599.25 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| LMArena WebDev | — | 1397 |
| SWE-bench Multilingual | 39.7% | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use GPT-5 Mini leads
GPT-5 Mini: 31.0 (#64), MiniMax-M2.7: 24.9 (#103)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| Terminal-Bench | 34.8% | 45.1% |
| Berkeley Function Calling Leaderboard | 55.5% | — |
| ExploitBench | — | 13.3% |
| GBAEval | — | 0% |
| Vending-Bench 2 | -31.18 | — |
Reasoning GPT-5 Mini leads
GPT-5 Mini: 24.1 (#153), MiniMax-M2.7: 19.0 (#248)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| CritPt | 0% | 0.6% |
| LMArena Hard Prompts | 1380 | 1422 |
| Epoch Capabilities Index | 145.51 | 145.84 |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| NYT Connections (extended) | — | 24.7% |
| ARC-AGI-1 | 54.3% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| Thematic Generalization | — | 39.3% |
| Mystery Game Puzzles | 10% | — |
| DTBench | 80.5% | — |
| LMCA | 34.2% | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.3 (#59), MiniMax-M2.7: 24.9 (#254)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| ProofBench | 9% | 3% |
| LMArena Math | 1377 | 1420 |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| Omni-MATH | 72.2% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.5 (#80), MiniMax-M2.7: 37.1 (#151)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| Vectara Hallucination Rate | 12.9% | 12.9% |
| LMArena Expert | 1377 | 1443 |
| GPQA Diamond | 75% | — |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| GPQA (HELM) | 75.6% | — |
Multimodal Not comparable
GPT-5 Mini: 35.0 (#80), MiniMax-M2.7: —
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual MiniMax-M2.7 leads
GPT-5 Mini: 49.1 (#131), MiniMax-M2.7: 50.5 (#120)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | 1363 | 1382 |
| LMArena Chinese | 1387 | 1439 |
| LMArena French | 1385 | 1422 |
| LMArena German | 1366 | 1398 |
| LMArena Japanese | 1342 | 1261 |
| LMArena Korean | 1306 | 1313 |
| LMArena Russian | 1362 | 1383 |
| LMArena Spanish | 1355 | 1403 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.4 (#43), MiniMax-M2.7: 74.2 (#101)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | 1357 | 1404 |
| IFEval | 92.7% | — |
Long Context MiniMax-M2.7 leads
GPT-5 Mini: 41.9 (#127), MiniMax-M2.7: 43.3 (#94)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | 1355 | 1419 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference MiniMax-M2.7 leads
GPT-5 Mini: 55.4 (#143), MiniMax-M2.7: 59.1 (#107)
| Benchmark | GPT-5 Mini | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | 1373 | 1404 |
| LMArena Creative Writing | 1325 | 1354 |
| LMArena Multi-Turn | 1363 | 1413 |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
Frequently asked questions
Is GPT-5 Mini better than MiniMax-M2.7?
GPT-5 Mini is the stronger model overall, scoring 41.7 to 37.4 on the Noometry Index.
Which is cheaper, GPT-5 Mini or MiniMax-M2.7?
MiniMax-M2.7 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GPT-5 Mini lists at $0.25 and $2.
Is GPT-5 Mini or MiniMax-M2.7 better for coding?
MiniMax-M2.7 scores higher on coding benchmarks: 41.4 versus 40.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 205K.
How many benchmarks do GPT-5 Mini and MiniMax-M2.7 share?
25 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and MiniMax-M2.7 has 30.