Gemma 4 31B — Specifications
Compilato da Mustafa Ihsan dalla documentazione pubblicata del fornitore · Ultimo aggiornamento
| Sviluppatore | |
|---|---|
| Tipo | LLM (multimodale) |
| Modalità | Testo, immagine → testo |
| Parametri | 30,7B |
| Finestra contestuale | 256K |
| Licenza | Apache 2.0 |
| Pesi aperti | Sì |
| Pubblicato | 2026-03 |
| Prezzo dell’input | $0.20 /1M |
| Prezzo dell’output | $0.40 /1M |
| Provider API | DeepInfra, OpenRouter (hosted); Gemini API (free tier only); Ollama (local) |
Esegui localmente
| VRAM (4-bit) | ~17.5 GB |
|---|
Benchmark
| Google-reported | MMLU Pro 85.2%; GPQA Diamond 84.3%; LiveCodeBench v6 80.0%; AIME 2026 (no tools) 89.2%; MMMU Pro 76.9%. |
|---|
What is Gemma 4 31B?
Gemma 4 31B is the largest dense model in Google DeepMind’s open Gemma 4 family, released on 31 March 2026 under the Apache 2.0 licence. Its model card lists 30.7 billion parameters, text and image input, a 256K-token context window, configurable thinking modes and support for over 140 languages. Google reports 85.2% on MMLU Pro, 84.3% on GPQA Diamond and 80.0% on LiveCodeBench v6, against 67.6%, 42.4% and 29.1% for Gemma 3 27B without thinking.
Google estimates about 17.5 GB of memory to load it at 4-bit (Q4_0). That covers the weights only: the context window (KV cache) and supporting software need more on top. Ollama’s gemma4:31b-it-q4_K_M build is a 20 GB download, which will not fit in a 16 GB card’s memory but fits a 24 GB GPU with some room for context. Check a setup with the Calcolatore VRAM e la nostra guida a running Gemma 4 in Ollama.
Google’s Gemini API serves gemma-4-31b-it on its free tier only, with no paid tier. Hosted, DeepInfra charges $0.20 per million input tokens and $0.40 output, the price stored in this entry. It succeeds Gemma 3 27B. If speed matters more than peak quality, the mixture-of-experts Gemma 4 26B A4B needs less memory and, according to Google, runs almost as fast as a 4B model.
Gemma 4 31B pricing: API cost per 1M tokens
| Input (per ogni milione di token) | $0.200 |
|---|---|
| Output (per ogni milione di token) | $0.400 |
| Rapporto output/input | 2× |
| Combinato (4:1 in:out) | $0.240 per 1 milione di token |
What Gemma 4 31B costs per month
Spesa mensile reale con un mix input-to-output 4:1 — il rapporto effettivamente prodotto da un tipico carico di lavoro basato su chat o RAG.
| Carico di lavoro | Token/mese | Costo/mese |
|---|---|---|
| Progetto secondario | 1 milione in ingresso / 0,25 milioni in uscita | $0.30 |
| Piccolo team | 20 milioni in ingresso / 5 milioni in uscita | $6.00 |
| Produzione | 200 milioni in ingresso / 50 milioni in uscita | $60 |
Calcola i tuoi numeri personalizzati nel Calcolatore dei costi delle API per l'IA.
Cheaper alternatives to Gemma 4 31B
| Modello | Costo combinato ($/1 milione) | Risparmi |
|---|---|---|
| Llama 3.1 8B aperta | $0.0240 | 90% cheaper |
| Mistral NeMo 12B aperta | $0.0292 | 88% più economico |
| Gemma 3 4B aperta | $0.0600 | il 75% più economica |
Eseguirlo in autonomia o pagare l’API?
Gemma 4 31B is open-weight, so you can run it yourself. It needs ~17.5 GB of VRAM at 4-bit. Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calcolatore self-hosting vs API calcola il punto di pareggio in base al tuo volume di token.
Domande frequenti
How much does Gemma 4 31B cost per 1M tokens?
Gemma 4 31B costs $0.200 per 1M input tokens and $0.400 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.240 per 1M tokens.
How much does Gemma 4 31B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $6.00 on Gemma 4 31B. A side project (1M in / 0.25M out) costs roughly $0.30.
What is a cheaper alternative to Gemma 4 31B?
Llama 3.1 8B is the strongest cheaper option in our database at $0.0240 per 1M blended — about 90% less than Gemma 4 31B. It is also open-weight, so self-hosting is an option.
Can I run Gemma 4 31B locally?
Yes. Gemma 4 31B is open-weight and needs about ~17.5 GB of VRAM at 4-bit quantisation.
Why does Gemma 4 31B charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Gemma 4 31B charges 2× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
I prezzi indicati sono le tariffe ufficiali pubblicate per l'API principale del modello e vengono aggiornati man mano che i fornitori li modificano. Sconti per volumi elevati, elaborazione batch e input memorizzati nella cache non sono inclusi. Confronta tutti i modelli fianco a fianco nel database dei modelli AI o il Classifica LLM.