Head-to-Head Model Comparison

Grok 4.5 vs Grok 4.7

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
Grok 4.5 vs Grok 4.7: Benchmarks, Pricing & Speed Comparison

Grok 4.5 by xAI and Grok 4.7 by xAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 500K tokens. In latency-sensitive production environments, Grok 4.5 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.

Confronto Diretto (VS)

Cambia e Confronta Qualsiasi Modello

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xAI

Grok 4.5

Modello di Ragionamento Profondo

Grok 4.5 di xAI è un modello di intelligenza artificiale con finestra di contesto da 500K token e tariffazione API di $2/1M token in input e $6/1M in output, ideale per coding, ragionamento e automazione aziendale.

Intelligence Index75 / 100
Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

500K tokens

SWE-bench Verified:

74.2%

xAI

Grok 4.7

Modello di Ragionamento Profondo

Grok 4.7 di xAI è un modello di intelligenza artificiale con finestra di contesto da 500K token e tariffazione API di $2/1M token in input e $6/1M in output, ideale per coding, ragionamento e automazione aziendale.

Intelligence Index46 / 100
Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

500K tokens

SWE-bench Verified:

77.2%

Differenze Chiave a Colpo d’Occhio

  • Both models cost the same under this workload — choose on capability, not price.
  • Grok 4.5 is faster at 130 tok/s vs 110 tok/s.
  • Grok 4.7 leads on SWE-bench coding (77.2% vs 74.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGrok 4.5Grok 4.7
Intelligence Index
Composite reasoning & knowledge score (0-100)
7546
Provider
xAIxAI
Input Price
Cost per million input tokens
$2.00 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$6.00 / 1M$6.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.35 / 1M$1.35 / 1M
Context Window
Maximum input + output tokens
500K tokens~750 A4 pages of text500K tokens~750 A4 pages of text
Max Output Tokens
Per single request
66K66K
Throughput Speed
Tokens generated per second
130 tok/s110 tok/s
SWE-bench Verified
Real-world software engineering
74.2%77.2%
GPQA Diamond
Graduate-level reasoning
75.9%78.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.50 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
Grok 4.7
+3.0% SWE-bench lead
Cost
Tied
Identical monthly runtime cost
Speed
Grok 4.5
+18% faster generation
Context
Tied
500K tokens window

Punteggi Benchmark Verificati

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

Grok 4.5Grok 4.7
SWE-bench Verified (Autonomous Coding)

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

74.2%vs77.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

75.9%vs78.5%
MMLU-Pro (Multi-task Language Understanding)

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

86.8%vs88.6%
LMSYS Chatbot Arena Elo

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

1435ptsvs1465pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

13.9%vs15.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

49.5%vs38%
SciCode (Scientific Computing)

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

47%vs52.4%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

73.5%vs76.5%
AIME (Advanced Math Competition)

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

87.8%vs91.2%
Long-Context Reasoning

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

87.2%vs89.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs110 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
Grok 4.50.50s
Grok 4.70.62s
Est. End-to-End Response (500 tokens)TTFT + generation time
Grok 4.54.35s
Grok 4.75.17s

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)
Grok 4.5 Monthly API Bill
$9.15 / month
$2/1M in · $6/1M out
Grok 4.7 Monthly API Bill
$9.15 / month
$2/1M in · $6/1M out
These models cost the same under this workload — no savings either way.Break-even

Grok 4.5 · Strengths & Key Advantages

  • Architettura Advanced Sparse MoE avanzata di xAI con supporto fino a 500K token di contesto
  • Ottimo rapporto qualità-prezzo ($2/1M input, $6/1M output) e bassa latenza
  • Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
  • L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
  • Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato

Grok 4.7 · Strengths & Key Advantages

  • Architettura 2.1T Multimodal Sparse MoE avanzata di xAI con supporto fino a 500K token di contesto
  • Ottimo rapporto qualità-prezzo ($2/1M input, $6/1M output) e bassa latenza
  • Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
  • L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
  • Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato

Token Use & Task Economics

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

MetricGrok 4.5Grok 4.7
Cost per Task
Estimated USD to finish one task
$0.62$0.68
Output Tokens / Task
Tokens generated per task
44K46K
Reasoning Tokens / Task
Thinking tokens burned per task
20K23K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureGrok 4.5Grok 4.7
Official API Model ID (SDK)grok-4-5-latestgrok-4-7-latest
API ProtocolxAI REST API (OpenAI-Compatible)xAI REST API (OpenAI-Compatible)
Release Date2026-072026-09
Knowledge CutoffMarch 2026April 2026
ArchitectureAdvanced Sparse MoE2.1T Multimodal Sparse MoE
ParametersConfidential2.1T (MoE)
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 500K windowFlat rate ($2/1M in) across full 500K window
Prompt Cache Policy & TTLCached input at $0.5/1M tokensCached input at $0.5/1M tokens
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 & SandboxReal-time Web & X (Twitter) Search Grounding + Code ExecutionReal-time Web & X (Twitter) Search Grounding + Code Execution
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationAPI Prompt Engineering & RAG Distillation
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 VRAMCloud-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 ClassificationxAI Production Safety & Alignment VerifiedxAI Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Grok 4.5 vs Grok 4.7: Risposte Rapide (FAQ)

Which is more intelligent, Grok 4.5 or Grok 4.7?

Grok 4.5 is more intelligent. It scores 75 compared with 46 on the composite Intelligence Index.

Which model is faster?

Grok 4.5 is faster, generating 130 tokens per second compared with 110 tokens per second.

Which model has lower latency?

Grok 4.5 has lower latency, with a time to first token of 0.50s compared with 0.62s.

Verdetto

Verdict & Model Selection Recommendation: Choose Grok 4.5 if your priority is Cost-sensitive production APIs, rapid coding assistants, and technical Q&A. Its performance in demanding tasks justifies its operational token cost. Choose Grok 4.7 if your workflow prioritizes Affordable second opinions in coding and legal workflows, enterprise domain tasks, and balanced agentic automation. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

Domande Frequenti (FAQ)

How does Grok 4.5 compare to Grok 4.7 in API token pricing and cost per token?+

Both Grok 4.5 and Grok 4.7 share matched API token pricing at $2.00 per 1M input tokens and $6.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.

Which model has a larger context window: Grok 4.5 or Grok 4.7?+

Both Grok 4.5 and Grok 4.7 offer an identical context window of 500K tokens (~375k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.

Which AI model is better for software engineering and coding: Grok 4.5 or Grok 4.7?+

Grok 4.5 scores 74.2% on SWE-bench Verified while Grok 4.7 scores 77.2%. For autonomous software engineering and multi-file debugging, Grok 4.7 holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between Grok 4.5 and Grok 4.7?+

Grok 4.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Grok 4.7 provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between Grok 4.5 and Grok 4.7?+

Grok 4.5 generates approximately 130 tokens per second, while Grok 4.7 delivers 110 tokens per second. In latency-sensitive production environments, Grok 4.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Grok 4.5 and Grok 4.7 to optimize costs?+

Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Grok 4.5, and route deep architectural or complex reasoning queries to Grok 4.5. This balanced routing strategy maximizes system throughput and answer quality.

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: GetInfoUs Independent AI Benchmarks & Official API Specs