Head-to-Head Model Comparison

Gemma 4 31B vs Gemma 4 26B A4B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemma 4 31B vs Gemma 4 26B A4B: Benchmarks, Pricing & Speed Comparison

Gemma 4 31B di Google e Gemma 4 26B A4B di Google sono due modelli di IA di riferimento. Gemma 4 31B offre una finestra di contesto di 262.1K token a $0.00/$0.00 per milione di token, mentre Gemma 4 26B A4B mette a disposizione 262.1K token di contesto a $0.00/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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Google

Gemma 4 31B

Ragionamento a Pesi Aperti

Gemma 4 31B di Google è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 262K, velocità di 80 token/s, indice di intelligenza di 81/100 e tariffa di a

Intelligence Index81 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

68.5%

Google

Gemma 4 26B A4B

Modello Base a Pesi Aperti

Gemma 4 26B A4B di Google è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 262K, velocità di 160 token/s, indice di intelligenza di 74/100

Intelligence Index74 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

61%

Differenze Chiave a Colpo d’Occhio

  • Both models cost the same under this workload — choose on capability, not price.
  • Gemma 4 26B A4B is faster at 160 tok/s vs 80 tok/s.
  • Gemma 4 31B leads on SWE-bench coding (68.5% vs 61.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemma 4 31BGemma 4 26B A4B
Intelligence Index
Composite reasoning & knowledge score (0-100)
8174
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$0.00 / 1M$0.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.00 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text262.1K tokens~393 A4 pages of text
Max Output Tokens
Per single request
33K33K
Throughput Speed
Tokens generated per second
80 tok/s160 tok/s
SWE-bench Verified
Real-world software engineering
68.5%61%
GPQA Diamond
Graduate-level reasoning
77%71.5%

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
Gemma 4 31B
+7.5% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Gemma 4 26B A4B
+100% faster generation
Context
Tied
262.1K tokens window

Punteggi Benchmark Verificati

Official evaluation metrics verified by independent third-party research labs.

Gemma 4 31BGemma 4 26B A4B
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

68.5%vs61%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

77%vs71.5%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84.8%vs80.5%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1410ptsvs1365pts
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

86%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs160 tok/s

Latenza e Tempo di Risposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Gemma 4 31B0.48s
Gemma 4 26B A4B0.29s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemma 4 31B6.73s
Gemma 4 26B A4B3.42s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemma 4 31B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Gemma 4 26B A4B Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
These models cost the same under this workload — no savings either way.Break-even

Gemma 4 31B · Strengths & Key Advantages

  • Finestra di contesto da 262K con capacità massima di output fino a 33K token (SWE-bench Verified 68.5% · GPQA Diamond 77%)
  • Throughput elevato di 80 token/secondo con latenza al primo token (TTFT) di soli 480 ms
  • Pesi aperti (31B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Gemma 4 26B A4B · Strengths & Key Advantages

  • Finestra di contesto da 262K con capacità massima di output fino a 33K token (SWE-bench Verified 61% · GPQA Diamond 71.5%)
  • Throughput elevato di 160 token/secondo con latenza al primo token (TTFT) di soli 290 ms
  • Pesi aperti (26B (4B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGemma 4 31BGemma 4 26B A4B
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
12K8K
Reasoning Tokens / Task
Thinking tokens burned per task
6K3K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureGemma 4 31BGemma 4 26B A4B
Official API Model ID (SDK)models/gemma-4-31bmodels/gemma-4-26b-a4b
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2026-042026-04
Knowledge CutoffJanuary 2026January 2026
ArchitectureDense open reasoning transformer with native thinkingSparse Mixture-of-Experts open transformer (26B total, 4B active)
Parameters31B26B (4B active MoE)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Free across full context window (only KV-cache VRAM scales)Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 67 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 16 GB VRAM (4-bit Quantized) · 56 GB VRAM (Native BF16)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Gemma 4 31B vs Gemma 4 26B A4B: Risposte Rapide (FAQ)

Which is more intelligent, Gemma 4 31B or Gemma 4 26B A4B?

Gemma 4 31B is more intelligent. It scores 81 compared with 74 on the composite Intelligence Index.

Which model is faster?

Gemma 4 26B A4B is faster, generating 160 tokens per second compared with 80 tokens per second.

Which model has lower latency?

Gemma 4 26B A4B has lower latency, with a time to first token of 0.29s compared with 0.48s.

Verdetto

Sia Gemma 4 31B (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) sia Gemma 4 26B A4B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemma 4 31B guida nei benchmark di ragionamento e programmazione, Gemma 4 26B A4B garantisce una generazione più rapida e Gemma 4 31B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Gemma 4 31B o Gemma 4 26B A4B?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Gemma 4 31B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Gemma 4 31B e Gemma 4 26B A4B?+

Gemma 4 31B costa $0.00 / $0.00 per milione di token di input/output, mentre Gemma 4 26B A4B costa $0.00 / $0.00. Nel costo combinato, Gemma 4 31B risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Gemma 4 31B supporta 262.1K token di contesto contro 262.1K token di Gemma 4 26B A4B, mentre Gemma 4 26B A4B offre una velocità di generazione superiore.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Fonti ufficiali: Documentazione API ufficiale di Google e Google, schede di sistema e benchmark indipendenti (2026).