GetInfoUs
Head-to-Head Model Comparison

GPT-5.3 Codex vs KAT-Coder-Air V2.5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
Condividi
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-5.3 Codex vs KAT-Coder-Air V2.5: Benchmarks, Pricing & Speed Comparison

GPT-5.3 Codex di OpenAI e KAT-Coder-Air V2.5 di Kwaipilot sono due modelli di IA di riferimento. GPT-5.3 Codex offre una finestra di contesto di 400K token a $2.00/$16.00 per milione di token, mentre KAT-Coder-Air V2.5 mette a disposizione 256K token di contesto a $0.10/$0.40 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

Cambia e Confronta Qualsiasi Modello

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

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
OpenAI

GPT-5.3 Codex

Leader in Programmazione e Codice

GPT-5.3 Codex di OpenAI è un modello di IA progettato per l’ingegneria del software autonoma, il refactoring multi-file e il debug avanzato, con finestra di contesto da 400K, velocità di 122 token/s, indice di intelligenza di 72/100 e tarif

Intelligence Index72 / 100
Input Token:

$2.00 / 1M

Output Token:

$16.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

79%

Kwaipilot

KAT-Coder-Air V2.5

Modello Leggero ed Economico

KAT-Coder-Air V2.5 di Kwaipilot è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 256K, velocità di 145 token/s, indice di intelligenza di 77/100 e ta

Intelligence Index77 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

256K tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

  • KAT-Coder-Air V2.5 is 33.7x cheaper for API usage (input + output tokens combined).
  • GPT-5.3 Codex has a larger context window (400K vs 256K tokens).
  • KAT-Coder-Air V2.5 is faster at 145 tok/s vs 122 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-5.3 CodexKAT-Coder-Air V2.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
7277
Provider
OpenAIKwaipilot
Input Price
Cost per million input tokens
$2.00 / 1M$0.10 / 1M
Output Price
Cost per million output tokens
$16.00 / 1M$0.40 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.14 / 1M$0.07 / 1M
Context Window
Maximum input + output tokens
400K tokens~600 A4 pages of text256K tokens~384 A4 pages of text
Max Output Tokens
Per single request
128K8K
Throughput Speed
Tokens generated per second
122 tok/s145 tok/s
SWE-bench Verified
Real-world software engineering
79%N/A
GPQA Diamond
Graduate-level reasoning
79.2%N/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.20 / 1M$0.01 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
KAT-Coder-Air V2.5
33.7x cheaper monthly runtime
Speed
KAT-Coder-Air V2.5
+19% faster generation
Context
GPT-5.3 Codex
400K tokens window

Punteggi Benchmark Verificati

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

GPT-5.3 CodexKAT-Coder-Air V2.5
SWE-bench Verified (Autonomous Coding)

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

79%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

79.2%vsN/A
MMLU-Pro (Multi-task Language Understanding)

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

88.4%vs74%
LMSYS Chatbot Arena Elo

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

1424ptsvs1285pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

15%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

56%vsN/A
SciCode (Scientific Computing)

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

50.5%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

78.2%vsN/A
AIME (Advanced Math Competition)

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

94.4%vsN/A
Long-Context Reasoning

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

84%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

122 tok/s vs145 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
GPT-5.3 Codex1.23s
KAT-Coder-Air V2.50.29s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5.3 Codex5.33s
KAT-Coder-Air V2.53.74s

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)
GPT-5.3 Codex Monthly API Bill
$17.70 / month
$2/1M in · $16/1M out
KAT-Coder-Air V2.5 Monthly API Bill
$0.53 / month
$0.1/1M in · $0.4/1M out
KAT-Coder-Air V2.5 saves approximately $17.18 / month (97%) under this workload.33.7x Savings

GPT-5.3 Codex · Strengths & Key Advantages

  • Finestra di contesto da 400K con capacità massima di output fino a 128K token (SWE-bench Verified 79% · GPQA Diamond 79.2%)
  • Throughput elevato di 122 token/secondo con latenza al primo token (TTFT) di soli 1230 ms
  • Economia API competitiva ($2/$16 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

KAT-Coder-Air V2.5 · Strengths & Key Advantages

  • Finestra di contesto da 256K con capacità massima di output fino a 8K token (MMLU-Pro 74% · Arena Elo 1285)
  • Throughput elevato di 145 token/secondo con latenza al primo token (TTFT) di soli 290 ms
  • Economia API competitiva ($0.1/$0.4 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

Token Use & Task Economics

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

MetricGPT-5.3 CodexKAT-Coder-Air V2.5
Cost per Task
Estimated USD to finish one task
$2.60$0.03
Output Tokens / Task
Tokens generated per task
62K14K
Reasoning Tokens / Task
Thinking tokens burned per task
36K1K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5.3 CodexKAT-Coder-Air V2.5
Official API Model ID (SDK)gpt-5-3-codexkat-coder-air-v2-5
API ProtocolOpenAI Responses API & Chat Completions v1Kwaipilot REST API (OpenAI-Compatible)
Release Date2026-022026-07
Knowledge CutoffNovember 2025April 2026
ArchitectureCode-specialised unified reasoning modelLightweight Code-Generation Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 400K windowFlat rate ($0.1/1M in) across full 256K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.2/1M (no extra cache-write fee)Cached input at $0.01/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)API Prompt Engineering & RAG Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedKwaipilot 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
Output Modalitiestexttext

GPT-5.3 Codex vs KAT-Coder-Air V2.5: Risposte Rapide (FAQ)

Which is more intelligent, GPT-5.3 Codex or KAT-Coder-Air V2.5?

KAT-Coder-Air V2.5 is more intelligent. It scores 77 compared with 72 on the composite Intelligence Index.

Which model is faster?

KAT-Coder-Air V2.5 is faster, generating 145 tokens per second compared with 122 tokens per second.

Which model is cheaper to run?

KAT-Coder-Air V2.5 is cheaper at $0.07 per 1M tokens blended, compared with $2.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

KAT-Coder-Air V2.5 has lower latency, with a time to first token of 0.29s compared with 1.23s.

Which model has a larger context window?

GPT-5.3 Codex has a larger context window, supporting 400K tokens compared with 256K tokens.

Verdetto

Sia GPT-5.3 Codex (Ideale per l’ingegneria del software autonoma, il refactoring multi-file e il debug avanzato, sistemi RAG aziendali e integrazioni API scalabili.) sia KAT-Coder-Air V2.5 (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: KAT-Coder-Air V2.5 guida nei benchmark di ragionamento e programmazione, KAT-Coder-Air V2.5 garantisce una generazione più rapida e KAT-Coder-Air V2.5 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GPT-5.3 Codex o KAT-Coder-Air V2.5?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale KAT-Coder-Air V2.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GPT-5.3 Codex e KAT-Coder-Air V2.5?+

GPT-5.3 Codex costa $2.00 / $16.00 per milione di token di input/output, mentre KAT-Coder-Air V2.5 costa $0.10 / $0.40. Nel costo combinato, KAT-Coder-Air V2.5 risulta più economico.

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

GPT-5.3 Codex supporta 400K token di contesto contro 256K token di KAT-Coder-Air V2.5, mentre KAT-Coder-Air V2.5 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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 OpenAI e Kwaipilot, schede di sistema e benchmark indipendenti (2026).