Head-to-Head Model Comparison

Gemini 3.7 Flash vs GPT-5.6 Terra

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Gemini 3.7 FlashProfile

GPT-5.6 TerraProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 3.7 Flash

Modello Ammiraglia di Frontiera

Gemini 3.7 Flash di Google è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1.0M, velocità di 180 token/s, indice di intelligenza di

Intelligence Index85 / 100

Input Token:

$0.75 / 1M

Output Token:

$3.75 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

73.4%

OpenAI

GPT-5.6 Terra

Modello Ammiraglia di Frontiera

GPT-5.6 Terra di OpenAI è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1.1M, velocità di 124 token/s, indice di intelligenza di 85/

Intelligence Index85 / 100

Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

81.4%

Differenze Chiave a Colpo d’Occhio

Gemini 3.7 Flash is 2.6x cheaper for API usage (input + output tokens combined).
GPT-5.6 Terra has a larger context window (1M vs 1M tokens).
Gemini 3.7 Flash is faster at 180 tok/s vs 124 tok/s.
GPT-5.6 Terra leads on SWE-bench coding (81.4% vs 73.4%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Gemini 3.7 Flash	GPT-5.6 Terra
Intelligence Index Composite reasoning & knowledge score (0-100)	85	85
Provider	Google	OpenAI
Input Price Cost per million input tokens	$0.75 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$3.75 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.66 / 1M	$1.54 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	1.05M tokens~1,575 A4 pages of text
Max Output Tokens Per single request	66K	128K
Throughput Speed Tokens generated per second	180 tok/s	124 tok/s
SWE-bench Verified Real-world software engineering	73.4%	81.4%
GPQA Diamond Graduate-level reasoning	83.1%	80.4%
Cached Input Price Prompt caching discount per 1M tokens	$0.19 / 1M	$0.20 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GPT-5.6 Terra

+8.0% SWE-bench lead

Cost

Gemini 3.7 Flash

2.6x cheaper monthly runtime

Speed

Gemini 3.7 Flash

+45% faster generation

Context

GPT-5.6 Terra

1.05M tokens window

Punteggi Benchmark Verificati

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

Gemini 3.7 FlashGPT-5.6 Terra

SWE-bench Verified (Autonomous Coding)

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

73.4%vs81.4%

GPQA Diamond (Doctorate-Level Science & Math)

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

83.1%vs80.4%

MMLU-Pro (Multi-task Language Understanding)

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

88.2%vs89.8%

LMSYS Chatbot Arena Elo

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

1470ptsvs1470pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs16.4%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs57%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs52.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs78.8%

AIME (Advanced Math Competition)

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

N/Avs94.6%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs87.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

180 tok/s vs124 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

Gemini 3.7 Flash0.38s

GPT-5.6 Terra1.09s

Est. End-to-End Response (500 tokens)TTFT + generation time

Gemini 3.7 Flash3.16s

GPT-5.6 Terra5.12s

Simulatore Interattivo dei Costi Token

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Gemini 3.7 Flash Monthly API Bill

$4.78 / month

$0.75/1M in · $3.75/1M out

GPT-5.6 Terra Monthly API Bill

$12.30 / month

$2/1M in · $10/1M out

Gemini 3.7 Flash saves approximately $7.52 / month (61%) under this workload.2.6x Savings

Gemini 3.7 Flash · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 66K token (SWE-bench Verified 73.4% · GPQA Diamond 83.1%)
Throughput elevato di 180 token/secondo con latenza al primo token (TTFT) di soli 380 ms
Economia API competitiva ($0.75/$3.75 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

GPT-5.6 Terra · Strengths & Key Advantages

Finestra di contesto da 1.1M con capacità massima di output fino a 128K token (SWE-bench Verified 81.4% · GPQA Diamond 80.4%)
Throughput elevato di 124 token/secondo con latenza al primo token (TTFT) di soli 1090 ms
Economia API competitiva ($2/$10 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

Token Use & Task Economics

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

Metric	Gemini 3.7 Flash	GPT-5.6 Terra
Cost per Task Estimated USD to finish one task	$1.15	$1.95
Output Tokens / Task Tokens generated per task	42K	58K
Reasoning Tokens / Task Thinking tokens burned per task	20K	32K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini 3.7 Flash	GPT-5.6 Terra
Official API Model ID (SDK)	models/gemini-3-7-flash	gpt-5-6-terra
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI Responses API & Chat Completions v1
Release Date	2026-08	2026-07
Knowledge Cutoff	May 2026	February 2026
Architecture	Agent-first adaptive multimodal transformer	Balanced unified reasoning model
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Flat rate ($2/1M in) across full 1050K window
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.1875/1M with configurable TTL	Automatic prefix caching at $0.2/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Google DeepMind Frontier Safety Framework Verified	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision, audio, video	text, vision
Output Modalities	text	text

Gemini 3.7 Flash vs GPT-5.6 Terra: Risposte Rapide (FAQ)

Which model is faster?

Gemini 3.7 Flash is faster, generating 180 tokens per second compared with 124 tokens per second.

Which model is cheaper to run?

Gemini 3.7 Flash is cheaper at $0.66 per 1M tokens blended, compared with $1.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.7 Flash has lower latency, with a time to first token of 0.38s compared with 1.09s.

Which model has a larger context window?

GPT-5.6 Terra has a larger context window, supporting 1.05M tokens compared with 1.05M tokens.

Verdetto

Sia Gemini 3.7 Flash (Ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi RAG aziendali e integrazioni API scalabili.) sia GPT-5.6 Terra (Ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemini 3.7 Flash guida nei benchmark di ragionamento e programmazione, Gemini 3.7 Flash garantisce una generazione più rapida e Gemini 3.7 Flash offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Gemini 3.7 Flash o GPT-5.6 Terra?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Gemini 3.7 Flash. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Gemini 3.7 Flash e GPT-5.6 Terra?+

Gemini 3.7 Flash costa $0.75 / $3.75 per milione di token di input/output, mentre GPT-5.6 Terra costa $2.00 / $10.00. Nel costo combinato, Gemini 3.7 Flash risulta più economico.

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

Gemini 3.7 Flash supporta 1.05M token di contesto contro 1.05M token di GPT-5.6 Terra, mentre Gemini 3.7 Flash 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.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Gemini 3.7 Flash vs GPT-5.6 Terra

Cambia e Confronta Qualsiasi Modello

Gemini 3.7 Flash

GPT-5.6 Terra

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Gemini 3.7 Flash · Strengths & Key Advantages

Considerations & Trade-offs

GPT-5.6 Terra · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Gemini 3.7 Flash vs GPT-5.6 Terra: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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