kilocode/apps/kilocode-docs/docs/basic-usage/model-selection-guide.md

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sidebar_label: "Model Selection Guide"

Kilo Code Model Selection Guide

Last updated: September 3, 2025.

The AI model landscape evolves rapidly, so this guide focuses on what's delivering excellent results with Kilo Code right now. We update this regularly as new models emerge and performance shifts.

Kilo Code Top Performers

Model	Context Window	SWE-Bench Verified	Human Eval	LiveCodeBench	Input Price*	Output Price*	Best For
GPT-5	400K tokens	74.9%	96.3%	68.2%	$1.25	$10	Latest capabilities, multi-modal coding
Claude Sonnet 4	1M tokens	72.7%	94.8%	65.9%	$3-6	$15-22.50	Enterprise code generation, complex systems
Grok Code Fast 1	256K tokens	70.8%	92.1%	63.4%	$0.20	$1.50	Rapid development, cost-performance balance
Qwen3 Coder	256K tokens	68.4%	91.7%	61.8%	$0.20	$0.80	Pure coding tasks, rapid prototyping
Gemini 2.5 Pro	1M+ tokens	67.2%	89.9%	59.3%	TBD	TBD	Massive codebases, architectural planning

*Per million tokens

Budget-Conscious Options

Model	Context Window	SWE-Bench Verified	Human Eval	LiveCodeBench	Input Price*	Output Price*	Notes
DeepSeek V3	128K tokens	64.1%	87.3%	56.7%	$0.14	$0.28	Exceptional value for daily coding
DeepSeek R1	128K tokens	62.8%	85.9%	54.2%	$0.55	$2.19	Advanced reasoning at budget prices
Qwen3 32B	128K tokens	60.3%	83.4%	52.1%	Varies	Varies	Open source flexibility
Z AI GLM 4.5	128K tokens	58.7%	81.2%	49.8%	TBD	TBD	MIT license, hybrid reasoning system

*Per million tokens

Comprehensive Evaluation Framework

Latency Performance

Response times significantly impact development flow and productivity:

• Ultra-Fast (< 2s): Grok Code Fast 1, Qwen3 Coder
• Fast (2-4s): DeepSeek V3, GPT-5
• Moderate (4-8s): Claude Sonnet 4, DeepSeek R1
• Slower (8-15s): Gemini 2.5 Pro, Z AI GLM 4.5

Impact on Development: Ultra-fast models enable real-time coding assistance and immediate feedback loops. Models with 8+ second latency can disrupt flow state but may be acceptable for complex architectural decisions.

Throughput Analysis

Token generation rates affect large codebase processing:

• High Throughput (150+ tokens/s): GPT-5, Grok Code Fast 1
• Medium Throughput (100-150 tokens/s): Claude Sonnet 4, Qwen3 Coder
• Standard Throughput (50-100 tokens/s): DeepSeek models, Gemini 2.5 Pro
• Variable Throughput: Open source models depend on infrastructure

Scaling Factors: High throughput models excel when generating extensive documentation, refactoring large files, or batch processing multiple components.

Reliability & Availability

Enterprise considerations for production environments:

• Enterprise Grade (99.9%+ uptime): Claude Sonnet 4, GPT-5, Gemini 2.5 Pro
• Production Ready (99%+ uptime): Qwen3 Coder, Grok Code Fast 1
• Developing Reliability: DeepSeek models, Z AI GLM 4.5
• Self-Hosted: Qwen3 32B (reliability depends on your infrastructure)

Success Rates: Enterprise models maintain consistent output quality and handle edge cases more gracefully, while budget options may require additional validation steps.

Context Window Strategy

Optimizing for different project scales:

Size	Word Count	Typical Use Case	Recommended Models	Strategy
32K tokens	~24,000 words	Individual components, scripts	DeepSeek V3, Qwen3 Coder	Focus on single-file optimization
128K tokens	~96,000 words	Standard applications, most projects	All budget models, Grok Code Fast 1	Multi-file context, moderate complexity
256K tokens	~192,000 words	Large applications, multiple services	Qwen3 Coder, Grok Code Fast 1	Full feature context, service integration
400K+ tokens	~300,000+ words	Enterprise systems, full stack apps	GPT-5, Claude Sonnet 4, Gemini 2.5 Pro	Architectural overview, system-wide refactoring

Performance Degradation: Model effectiveness typically drops significantly beyond 400-500K tokens, regardless of advertised limits. Plan context usage accordingly.

Community Choice

The AI model landscape changes quicky to stay up to date 👉 check Kilo Code Community Favorites on OpenRouter