Head-to-Head Model Comparison

KAT-Coder-Pro V2.5 vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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Compare KAT-Coder-Pro V2.5 by Kwaipilot against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

Comparaison Face-à-Face (VS)

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Kwaipilot

KAT-Coder-Pro V2.5

Leader en Programmation et Code

KAT-Coder-Pro V2.5 de Kwaipilot est un modèle d’IA conçu pour le génie logiciel autonome, la refactorisation multi-fichiers et le débogage complexe, offrant une fenêtre de contexte de 256K, une vitesse de 80 tokens/s, un indice d’intelligen

Intelligence Index85 / 100
Input Token:

$0.50 / 1M

Output Token:

$2.00 / 1M

Context Window:

256K tokens

SWE-bench Verified:

58%

OpenAI

GPT-6.1 Sol

Modèle Phare de Pointe

GPT-6.1 Sol de OpenAI est un modèle d’IA conçu pour le raisonnement d’entreprise de pointe, l’ingénierie logicielle et la production IA, offrant une fenêtre de contexte de 1M, une vitesse de 120 tokens/s, un indice d’intelligence de 94/100

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

Différences Clés en un Coup d’Œil

  • KAT-Coder-Pro V2.5 is 4.6x cheaper for API usage (input + output tokens combined).
  • GPT-6.1 Sol has a larger context window (1M vs 256K tokens).
  • GPT-6.1 Sol is faster at 120 tok/s vs 80 tok/s.
  • GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 58.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueKAT-Coder-Pro V2.5GPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
8594
Provider
KwaipilotOpenAI
Input Price
Cost per million input tokens
$0.50 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$2.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.34 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
256K tokens~384 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
16K128K
Throughput Speed
Tokens generated per second
80 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
58%82.3%
GPQA Diamond
Graduate-level reasoning
67%80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.05 / 1M$0.10 / 1M

Qui gagne où ?

At-a-glance decision framework
Code
GPT-6.1 Sol
+24.3% SWE-bench lead
Coût
KAT-Coder-Pro V2.5
4.6x cheaper monthly runtime
Vitesse
GPT-6.1 Sol
+50% faster generation
Contexte
GPT-6.1 Sol
1M tokens window

Scores de Benchmarks Vérifiés

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

KAT-Coder-Pro V2.5GPT-6.1 Sol
SWE-bench Verified (Autonomous Coding)

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

58%vs82.3%
GPQA Diamond (Doctorate-Level Science & Math)

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

67%vs80.8%
MMLU-Pro (Multi-task Language Understanding)

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

81%vs89.9%
LMSYS Chatbot Arena Elo

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

1360ptsvs1519pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.1%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs63.6%
SciCode (Scientific Computing)

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

N/Avs54.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.7%
AIME (Advanced Math Competition)

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

N/Avs93.5%
Long-Context Reasoning

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

N/Avs91%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs120 tok/s

Latence et Temps de Réponse

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
KAT-Coder-Pro V2.50.51s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
KAT-Coder-Pro V2.56.76s
GPT-6.1 Sol5.29s

Simulateur Interactif de Coût de Tokens

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)
KAT-Coder-Pro V2.5 Monthly API Bill
$2.63 / month
$0.5/1M in · $2/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
KAT-Coder-Pro V2.5 saves approximately $9.53 / month (78%) under this workload.4.6x Savings

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

  • Fenêtre de contexte de 256K et capacité de sortie maximale de 16K tokens (SWE-bench Verified 58% · GPQA Diamond 67%)
  • Débit rapide de 80 tokens/seconde avec une latence au premier token (TTFT) de 510 ms
  • Tarification API optimisée ($0.5/$2 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
  • Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
  • Sur les problèmes de recherche fondamentale très complexes, les modèles de raisonnement lourd restent supérieurs

GPT-6.1 Sol · Strengths & Key Advantages

  • Fenêtre de contexte de 1M et capacité de sortie maximale de 128K tokens (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
  • Débit rapide de 120 tokens/seconde avec une latence au premier token (TTFT) de 1120 ms
  • Tarification API optimisée ($2/$10 par million de tokens) avec prise en charge du cache de prompts et des schémas JSON stricts
Considerations & Trade-offs
  • Modèle cloud propriétaire ne permettant pas le téléchargement local des poids sur serveur isolé
  • Pour les charges à très fort volume, l’activation du cache de prompts et de l’API Batch est recommandée

Token Use & Task Economics

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

MetricKAT-Coder-Pro V2.5GPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$0.18$2.05
Output Tokens / Task
Tokens generated per task
22K62K
Reasoning Tokens / Task
Thinking tokens burned per task
7K36K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueKAT-Coder-Pro V2.5GPT-6.1 Sol
Official API Model ID (SDK)kat-coder-pro-v2-5gpt-6-1-sol
API ProtocolKwaipilot REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2026-072026-09
Knowledge CutoffApril 2026April 2026
ArchitectureAgentic Code-Specialized TransformerNear-Astra unified reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.5/1M in) across full 256K windowFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.05/1M tokensAutomatic prefix caching at $0.1/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)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 & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token)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 ClassificationKwaipilot Production Safety & Alignment VerifiedOpenAI Preparedness Framework Audited & System Card Published
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 Modalitiestexttext, vision
Output Modalitiestexttext

KAT-Coder-Pro V2.5 vs GPT-6.1 Sol: Réponses Rapides (FAQ)

Which is more intelligent, KAT-Coder-Pro V2.5 or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 85 on the composite Intelligence Index.

Which model is faster?

GPT-6.1 Sol is faster, generating 120 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

KAT-Coder-Pro V2.5 is cheaper at $0.34 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

KAT-Coder-Pro V2.5 has lower latency, with a time to first token of 0.51s compared with 1.12s.

Which model has a larger context window?

GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 256K tokens.

Verdict

Choose KAT-Coder-Pro V2.5 for conçu pour le génie logiciel autonome, la refactorisation multi-fichiers et le débogage complexe, l’intégration rag et les pipelines de production évolutifs., or choose GPT-6.1 Sol for conçu pour le raisonnement d’entreprise de pointe, l’ingénierie logicielle et la production ia, l’intégration rag et les pipelines de production évolutifs..

Questions fréquentes

Which is cheaper: KAT-Coder-Pro V2.5 or GPT-6.1 Sol?+

KAT-Coder-Pro V2.5 costs $0.5/1M input and $2/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: KAT-Coder-Pro V2.5 or GPT-6.1 Sol?+

KAT-Coder-Pro V2.5 supports 256K tokens whereas GPT-6.1 Sol supports 1M tokens.

AI Terminology & Guide
Explore Glossary →

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

Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: Kwaipilot & OpenAI Official Documentation