Gemini 1.5 Pro vs MiniMax M2
| Gemini 1.5 Pro | MiniMax M2 | |
|---|---|---|
| Anbieter | MiniMax | |
| Kontextfenster | 2.000.000 | 200.000 |
| Erscheinungsdatum | 2024-02-15 | 2025-09-22 |
| Eingabepreis | $1.25/ 1M Tokens | $0.50/ 1M Tokens |
| Ausgabepreis | $5.00/ 1M Tokens | $2.00/ 1M Tokens |
| Geeignet für | multimodal · long-context analysis · chatbot · document processing | — |
Funktionen
| Gemini 1.5 Pro | MiniMax M2 | |
|---|---|---|
| Vision | ✓ | ✗ |
| Schlussfolgerung | ✓ | ✗ |
| Programmierung | ✓ | ✗ |
| Audio | ✓ | ✗ |
| Tool-Aufruf | ✓ | ✗ |
| Langer Kontext | ✓ | ✗ |
Ranking-Vergleich
| Gemini 1.5 Pro | MiniMax M2 | |
|---|---|---|
| Gesamt | 76.9 | 18 |
| Benchmark | 70.8 | 0 |
| Fähigkeit | 100 | 0 |
| Preiseffizienz | 100 | 100 |
| Kontext | 100 | 100 |
Gesamt = Benchmark 50% + Fähigkeit 20% + Preiseffizienz 20% + Kontext 10%.
Benchmark-Vergleich
| Gemini 1.5 Pro | MiniMax M2 | |
|---|---|---|
| Programmierung | — | — |
| Schlussfolgerung | — | — |
| Mathematik | — | — |
| Vision | — | — |
Preisverlauf
Gemini 1.5 Pro
| Datum | Eingabe | Ausgabe |
|---|---|---|
| 2026-08-11 | $1.25 | $5.00 |
/ 1M Tokens
MiniMax M2
Noch kein Preisverlauf vorhanden.
/ 1M Tokens
Gemini 1.5 Pro
Google's long-context multimodal model supporting up to 2M tokens.
MiniMax M2
MiniMax M2 reasoning model
Gemini 1.5 Pro
Data Reliability
Benchmark · VerifiedOfficial · Verified
Data confidence: 90%
MiniMax M2
Data Reliability
Benchmark · ExperimentalOfficial · Experimental
Data confidence: 50%
Why compare these models?
Choose Gemini 1.5 Pro if:
- • Higher overall score
Choose MiniMax M2 if:
- • Lower input price
Frequently Asked Questions
Which model is better: Gemini 1.5 Pro or MiniMax M2?
Based on overall scores, Gemini 1.5 Pro ranks higher (76.9 vs 18). See the comparison table for a full breakdown.
Which model is cheaper: Gemini 1.5 Pro or MiniMax M2?
MiniMax M2 is cheaper: $0.5 vs $1.25 per 1M input tokens.
What are the capability differences between Gemini 1.5 Pro and MiniMax M2?
Gemini 1.5 Pro: Vision, Schlussfolgerung, Programmierung, Audio, Tool-Aufruf, Langer Kontext