KAT-Coder-Pro V2.5 vs GPT-5.3 Codex
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
KAT-Coder-Pro V2.5 dari Kwaipilot dan GPT-5.3 Codex dari OpenAI adalah dua model AI unggulan. KAT-Coder-Pro V2.5 menawarkan jendela konteks 256K token dengan harga $0.50/$2.00 per 1 juta token, sedangkan GPT-5.3 Codex menyediakan konteks 400K token seharga $2.00/$16.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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KAT-Coder-Pro V2.5
Pemimpin Pemrograman & KodeKAT-Coder-Pro V2.5 dari Kwaipilot adalah model AI yang dirancang untuk rekayasa perangkat lunak otonom, refactoring multi-file, dan debugging mendalam, menghadirkan jendela konteks 256K, kecepatan 80 token/detik, indeks kecerdasan 85/100, s
$0.50 / 1M
$2.00 / 1M
256K tokens
58%
GPT-5.3 Codex
Pemimpin Pemrograman & KodeGPT-5.3 Codex dari OpenAI adalah model AI yang dirancang untuk rekayasa perangkat lunak otonom, refactoring multi-file, dan debugging mendalam, menghadirkan jendela konteks 400K, kecepatan 122 token/detik, indeks kecerdasan 72/100, serta ta
$2.00 / 1M
$16.00 / 1M
400K tokens
79%
Perbedaan Utama Sekilas
- KAT-Coder-Pro V2.5 is 6.7x cheaper for API usage (input + output tokens combined).
- GPT-5.3 Codex has a larger context window (400K vs 256K tokens).
- GPT-5.3 Codex is faster at 122 tok/s vs 80 tok/s.
- GPT-5.3 Codex leads on SWE-bench coding (79.0% vs 58.0%).
| Feature | KAT-Coder-Pro V2.5 | GPT-5.3 Codex |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 85 | 72 |
Provider | Kwaipilot | OpenAI |
Input Price Cost per million input tokens | $0.50 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $16.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.34 / 1M | $2.14 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 80 tok/s | 122 tok/s |
SWE-bench Verified Real-world software engineering | 58% | 79% |
GPQA Diamond Graduate-level reasoning | 67% | 79.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.20 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
KAT-Coder-Pro V2.5 · Strengths & Key Advantages
- Jendela konteks 256K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 58% · GPQA Diamond 67%)
- Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 510 ms
- Skema harga API kompetitif (tarif $0.5/$2 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GPT-5.3 Codex · Strengths & Key Advantages
- Jendela konteks 400K dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 79% · GPQA Diamond 79.2%)
- Kecepatan generasi tinggi 122 token/detik dengan latensi token pertama (TTFT) hanya 1230 ms
- Skema harga API kompetitif (tarif $2/$16 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | KAT-Coder-Pro V2.5 | GPT-5.3 Codex |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.18 | $2.60 |
Output Tokens / Task Tokens generated per task | 22K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 7K | 36K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | KAT-Coder-Pro V2.5 | GPT-5.3 Codex |
|---|---|---|
| Official API Model ID (SDK) | kat-coder-pro-v2-5 | gpt-5-3-codex |
| API Protocol | Kwaipilot REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-07 | 2026-02 |
| Knowledge Cutoff | April 2026 | November 2025 |
| Architecture | Agentic Code-Specialized Transformer | Code-specialised unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.5/1M in) across full 256K window | Flat rate ($2/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Automatic prefix caching at $0.2/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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 | Kwaipilot Production Safety & Alignment 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 | text, vision |
| Output Modalities | text | text |
KAT-Coder-Pro V2.5 vs GPT-5.3 Codex: Jawaban Cepat (FAQ)
Which is more intelligent, KAT-Coder-Pro V2.5 or GPT-5.3 Codex?
KAT-Coder-Pro V2.5 is more intelligent. It scores 85 compared with 72 on the composite Intelligence Index.
Which model is faster?
GPT-5.3 Codex is faster, generating 122 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 $2.14 (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 1.23s.
Which model has a larger context window?
GPT-5.3 Codex has a larger context window, supporting 400K tokens compared with 256K tokens.
Kesimpulan
Baik KAT-Coder-Pro V2.5 (Sangat cocok untuk rekayasa perangkat lunak otonom, refactoring multi-file, dan debugging mendalam, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-5.3 Codex (Sangat cocok untuk rekayasa perangkat lunak otonom, refactoring multi-file, dan debugging mendalam, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: KAT-Coder-Pro V2.5 unggul dalam penalaran dan pemrograman, GPT-5.3 Codex memberikan kecepatan generasi token lebih tinggi, dan KAT-Coder-Pro V2.5 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, KAT-Coder-Pro V2.5 atau GPT-5.3 Codex?+
Dalam indeks kecerdasan gabungan dan benchmark coding, KAT-Coder-Pro V2.5 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API KAT-Coder-Pro V2.5 vs GPT-5.3 Codex?+
KAT-Coder-Pro V2.5 mengenakan biaya $0.50 / $2.00 per 1M token input/output, sedangkan GPT-5.3 Codex sebesar $2.00 / $16.00. Secara keseluruhan, KAT-Coder-Pro V2.5 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
KAT-Coder-Pro V2.5 mendukung konteks 256K token dibandingkan 400K token pada GPT-5.3 Codex, sementara GPT-5.3 Codex unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Sumber resmi: Dokumentasi API resmi Kwaipilot & OpenAI serta evaluasi benchmark independen (2026).