Gemini 1.5 Pro vs GLM-4
| Gemini 1.5 Pro | GLM-4 | |
|---|---|---|
| Fournisseur | Zhipu | |
| Fenêtre de contexte | 2 000 000 | 128 000 |
| Date de sortie | 2024-02-15 | 2024-01-16 |
| Prix d'entrée | $1.25/ 1M tokens | $0.35/ 1M tokens |
| Prix de sortie | $5.00/ 1M tokens | $0.70/ 1M tokens |
| Idéal pour | multimodal · long-context analysis · chatbot · document processing | — |
Capacités
| Gemini 1.5 Pro | GLM-4 | |
|---|---|---|
| Vision | ✓ | ✗ |
| Raisonnement | ✓ | ✗ |
| Codage | ✓ | ✗ |
| Audio | ✓ | ✗ |
| Appel d'outils | ✓ | ✗ |
| Contexte long | ✓ | ✗ |
Comparaison de classement
| Gemini 1.5 Pro | GLM-4 | |
|---|---|---|
| Global | 76.9 | 60.1 |
| Benchmark | 70.8 | 80.8 |
| Capacité | 100 | 0 |
| Efficacité prix | 100 | 100 |
| Contexte | 100 | 64 |
Global = Benchmark 50% + Capacité 20% + Efficacité prix 20% + Contexte 10%.
Comparaison de benchmarks
| Gemini 1.5 Pro | GLM-4 | |
|---|---|---|
| Codage | — | — |
| Raisonnement | — | — |
| Mathématiques | — | — |
| Vision | — | — |
Historique des prix
Gemini 1.5 Pro
| Date | Entrée | Sortie |
|---|---|---|
| 2026-08-11 | $1.25 | $5.00 |
/ 1M tokens
GLM-4
Aucun historique de prix pour l'instant.
/ 1M tokens
Gemini 1.5 Pro
Google's long-context multimodal model supporting up to 2M tokens.
GLM-4
GLM-4 conversational model
Gemini 1.5 Pro
Data Reliability
Benchmark · VerifiedOfficial · Verified
Data confidence: 90%
GLM-4
Data Reliability
Benchmark · VerifiedOfficial · Experimental
Data confidence: 90%
Why compare these models?
These models are compared because:
- • Similar benchmark performance
Choose Gemini 1.5 Pro if:
- • Higher overall score
Choose GLM-4 if:
- • Lower input price
Frequently Asked Questions
Which model is better: Gemini 1.5 Pro or GLM-4?
Based on overall scores, Gemini 1.5 Pro ranks higher (76.9 vs 60.1). See the comparison table for a full breakdown.
Which model is cheaper: Gemini 1.5 Pro or GLM-4?
GLM-4 is cheaper: $0.35 vs $1.25 per 1M input tokens.
What are the capability differences between Gemini 1.5 Pro and GLM-4?
Gemini 1.5 Pro: Vision, Raisonnement, Codage, Audio, Appel d'outils, Contexte long