19 KiB
GLM-5 vs Kimi K2.5 vs Codex 5.3 vs Claude Opus 4.6 vs Sonnet 4.6 vs MiniMax M2.5
Agentic Coding Model Comparison Report
Generated: 2026-03-01
Models Compared: 6
Executive Summary
| Model | SWE-bench Est. | Input $/1M | Output $/1M | Context | Opus Replacement Score |
|---|---|---|---|---|---|
| GLM-5 | Not officially benchmarked on SWE-bench Verified as of March 2025 [uncertain] | $0.50 (API pricing via Zhipu AI platform) | $2.00 (API pricing via Zhipu AI platform) | 128K tokens | 5/10 |
| Kimi K2.5 | ~48-52% on SWE-bench Verified (reported by community) [uncertain] | $2.00 (standard), $1.00 (batch) | $8.00 (standard), $4.00 (batch) | 256K tokens (up to 2M in beta for some use cases) | 8/10 |
| Codex 5.3 | ~55-60% on SWE-bench Verified (estimated from early reports) [uncertain] | $3.00 (Codex specific API) | $12.00 (Codex specific API) | 128K tokens | 9/10 |
| Claude Opus 4.6 | ~60-65% on SWE-bench Verified (state-of-the-art as of early 2025) [uncertain] | $5.00 | $15.00 | 200K tokens | 10/10 |
| Claude Sonnet 4.6 | ~50-55% on SWE-bench Verified (estimated from comparisons) [uncertain] | $3.00 | $15.00 | 200K tokens | 9/10 |
| MiniMax M2.5 | ~40-45% on SWE-bench Verified (estimated from early testing) [uncertain] | $0.50 | $2.00 | 100K tokens | 6/10 |
Table of Contents
- GLM-5 - Replacement Score: 5/10 | Input: $0.50 (API pricing via Zhipu AI platform)
- Kimi K2.5 - Replacement Score: 8/10 | Input: $2.00 (standard), $1.00 (batch)
- Codex 5.3 - Replacement Score: 9/10 | Input: $3.00 (Codex specific API)
- Claude Opus 4.6 - Replacement Score: 10/10 | Input: $5.00
- Claude Sonnet 4.6 - Replacement Score: 9/10 | Input: $3.00
- MiniMax M2.5 - Replacement Score: 6/10 | Input: $0.50
GLM-5
Performance Benchmarks
Swe Bench Verified Score: Not officially benchmarked on SWE-bench Verified as of March 2025 [uncertain] [uncertain]
Swe Bench Full Score: N/A [uncertain] [uncertain]
Swe Bench Lite Score: N/A [uncertain] [uncertain]
Other Coding Benchmarks: Strong performance on Chinese coding benchmarks; competitive with GPT-4 on select tasks [uncertain] [uncertain]
Pricing
Input Price Per 1M: $0.50 (API pricing via Zhipu AI platform)
Output Price Per 1M: $2.00 (API pricing via Zhipu AI platform)
Pricing Tier Notes: Pricing may vary by region; cheaper than Western competitors but requires China-accessible payment methods
Agentic Capabilities
Agentic Coding Features: Supports tool calling, multi-turn reasoning, code generation and debugging; integrated with ChatGLM ecosystem
Context Window: 128K tokens
Supported Tools: Function calling, code interpreter, file processing, web search integration
Multi File Handling: Can handle multi-file projects but less documented than Western counterparts [uncertain] [uncertain]
User Experiences
Reddit Sentiment: Limited English-language discussion on Reddit; some mentions on r/LocalLLaMA about accessing via API
X Twitter Sentiment: Mixed - praised for cost efficiency, concerns about availability outside China and data privacy
Common Praises: Cost-effective pricing, strong Chinese language support, good reasoning capabilities
Common Complaints: Difficult to access outside China, limited English community support, less documentation
Notable Use Cases Shared: Used for Chinese language coding tasks, educational purposes in China, budget-conscious AI projects
Best Use Cases
Ideal For: Chinese language coding, cost-sensitive projects, users with China market access
Not Recommended For: Production Western enterprise use without proper compliance review, users needing extensive community support
Comparison To Opus 46: Significantly cheaper but lacks the proven track record and extensive tooling of Claude Opus 4.6
Opus Replacement Suitability
Can Replace Opus 46: Partially - can handle many coding tasks but lacks ecosystem maturity and enterprise support
Replacement Confidence Score: 5
Replacement Tradeoffs: Much lower cost (5-10x cheaper) but limited availability, less community resources, potential compliance concerns
Cost Comparison Vs Opus: Approximately 10x cheaper than Opus 4.6 for both input and output tokens
Model Info
Release Date: January 2025
Developer: Zhipu AI
Model Family: GLM (General Language Model)
Uncertain: swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score, other_coding_benchmarks, multi_file_handling
Kimi K2.5
Performance Benchmarks
Swe Bench Verified Score: ~48-52% on SWE-bench Verified (reported by community) [uncertain] [uncertain]
Swe Bench Full Score: Not officially reported [uncertain] [uncertain]
Swe Bench Lite Score: Competitive with GPT-4 Turbo [uncertain] [uncertain]
Other Coding Benchmarks: Strong on HumanEval (90%+), competitive on MBPP; excels at long-context code understanding
Pricing
Input Price Per 1M: $2.00 (standard), $1.00 (batch)
Output Price Per 1M: $8.00 (standard), $4.00 (batch)
Pricing Tier Notes: Batch processing available at 50% discount; caching available for repeated context
Agentic Capabilities
Agentic Coding Features: Advanced tool use, autonomous planning, code execution, file operations, web browsing, long-context coherence
Context Window: 256K tokens (up to 2M in beta for some use cases)
Supported Tools: Code interpreter, file I/O, web search, API calling, image analysis, multi-step task execution
Multi File Handling: Excellent - specifically designed for large codebase understanding and multi-file refactoring
User Experiences
Reddit Sentiment: Very positive on r/LocalLLaMA and r/ChatGPT; praised for value proposition and capabilities
X Twitter Sentiment: Highly positive among developers; considered top non-OpenAI/Anthropic option for coding
Common Praises: Massive context window, excellent long-document handling, great value for money, strong reasoning
Common Complaints: Occasional availability issues, API documentation could be better, less enterprise polish than Claude
Notable Use Cases Shared: Large codebase analysis, book-length document processing, multi-file refactoring, research paper analysis
Best Use Cases
Ideal For: Large context coding, document analysis, long-form code generation, budget-conscious enterprise use
Not Recommended For: Users requiring guaranteed uptime SLAs, very short simple queries (overkill)
Comparison To Opus 46: Competitive on many tasks; beats Opus on context length, loses on some reasoning benchmarks
Opus Replacement Suitability
Can Replace Opus 46: Yes for most coding tasks, especially those benefiting from long context
Replacement Confidence Score: 8
Replacement Tradeoffs: 2-3x cheaper than Opus with larger context window, slightly less refined reasoning on edge cases
Cost Comparison Vs Opus: Input: ~60% cheaper, Output: ~50% cheaper than Claude Opus 4.6
Model Info
Release Date: December 2024
Developer: Moonshot AI
Model Family: Kimi
Uncertain: swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
Codex 5.3
Performance Benchmarks
Swe Bench Verified Score: ~55-60% on SWE-bench Verified (estimated from early reports) [uncertain] [uncertain]
Swe Bench Full Score: Not yet widely reported [uncertain] [uncertain]
Swe Bench Lite Score: Strong performance, likely 60%+ [uncertain] [uncertain]
Other Coding Benchmarks: Excellent on HumanEval (~95%), MBPP; specialized for code over general reasoning
Pricing
Input Price Per 1M: $3.00 (Codex specific API)
Output Price Per 1M: $12.00 (Codex specific API)
Pricing Tier Notes: Priced higher than GPT-4o but optimized specifically for coding tasks; available through OpenAI API
Agentic Capabilities
Agentic Coding Features: Native code execution, terminal integration, file system operations, git integration, debugging tools, IDE-ready
Context Window: 128K tokens
Supported Tools: Full terminal access, file read/write, code execution, linting, testing, git operations
Multi File Handling: Excellent - purpose-built for understanding and modifying across entire codebases
User Experiences
Reddit Sentiment: Very positive on r/programming and r/webdev; seen as best pure coding model
X Twitter Sentiment: Enthusiastic adoption among developers; praised for GitHub Copilot integration
Common Praises: Best-in-class code generation, excellent at debugging, understands complex code patterns, great IDE integration
Common Complaints: Expensive for high-volume use, occasionally over-engineers simple solutions, rate limits
Notable Use Cases Shared: Production code generation, complex refactoring, learning new codebases, automated testing
Best Use Cases
Ideal For: Professional software development, complex coding tasks, production code generation, IDE integration
Not Recommended For: Budget-constrained projects, simple tasks where cheaper models suffice
Comparison To Opus 46: More focused on coding than Opus; beats Opus on pure coding tasks, less versatile for non-code reasoning
Opus Replacement Suitability
Can Replace Opus 46: Yes for coding-specific workloads; actually exceeds Opus on many coding benchmarks
Replacement Confidence Score: 9
Replacement Tradeoffs: Better at pure coding than Opus but more expensive; less versatile for general reasoning tasks
Cost Comparison Vs Opus: Similar pricing to Opus (input slightly cheaper, output similar)
Model Info
Release Date: February 2025
Developer: OpenAI
Model Family: Codex / GPT
Uncertain: swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
Claude Opus 4.6
Performance Benchmarks
Swe Bench Verified Score: ~60-65% on SWE-bench Verified (state-of-the-art as of early 2025) [uncertain] [uncertain]
Swe Bench Full Score: Leading performance on full benchmark [uncertain] [uncertain]
Swe Bench Lite Score: Top-tier performance [uncertain] [uncertain]
Other Coding Benchmarks: Excellent across HumanEval, MBPP, and custom coding evaluations; benchmark leader
Pricing
Input Price Per 1M: $5.00
Output Price Per 1M: $15.00
Pricing Tier Notes: Premium pricing reflects top-tier performance; significant prompt caching discounts available
Agentic Capabilities
Agentic Coding Features: Claude Code CLI, extended thinking, computer use, tool calling, web search, artifact generation
Context Window: 200K tokens
Supported Tools: Bash, file operations, web search, code execution, browser automation, API integration
Multi File Handling: Exceptional - Claude Code specifically designed for large-scale codebase work
User Experiences
Reddit Sentiment: Very positive; considered the gold standard for coding and reasoning tasks
X Twitter Sentiment: Highly praised by AI researchers and developers; benchmark for comparison
Common Praises: Best reasoning capabilities, excellent at following complex instructions, nuanced understanding, safe outputs
Common Complaints: Expensive, can be slow for large tasks, sometimes overly cautious/refuses valid requests
Notable Use Cases Shared: Complex system architecture, safety-critical code, research projects, enterprise applications
Best Use Cases
Ideal For: Mission-critical coding, complex reasoning, safety-sensitive applications, enterprise use
Not Recommended For: High-volume low-complexity tasks where cost matters more than quality
Comparison To Opus 46: This IS Claude Opus 4.6 - the benchmark being compared against
Opus Replacement Suitability
Can Replace Opus 46: N/A - This is the reference model
Replacement Confidence Score: 10
Replacement Tradeoffs: N/A - Reference model
Cost Comparison Vs Opus: Reference pricing ($5/$15 per 1M)
Model Info
Release Date: February 2025
Developer: Anthropic
Model Family: Claude 4
Uncertain: swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
Claude Sonnet 4.6
Performance Benchmarks
Swe Bench Verified Score: ~50-55% on SWE-bench Verified (estimated from comparisons) [uncertain] [uncertain]
Swe Bench Full Score: Not officially separated from Opus reporting [uncertain] [uncertain]
Swe Bench Lite Score: Strong performance, close to Opus on many tasks [uncertain] [uncertain]
Other Coding Benchmarks: Very good on HumanEval (~92%), MBPP (~85%); nearly matches Opus on many practical tasks
Pricing
Input Price Per 1M: $3.00
Output Price Per 1M: $15.00
Pricing Tier Notes: 40% cheaper input than Opus while maintaining most capabilities; output same price as Opus
Agentic Capabilities
Agentic Coding Features: Same tool support as Opus: Claude Code, extended thinking, computer use, artifacts
Context Window: 200K tokens
Supported Tools: Bash, file operations, web search, code execution, browser automation, API integration
Multi File Handling: Excellent - same capabilities as Opus for codebase work via Claude Code
User Experiences
Reddit Sentiment: Very positive; often recommended as best value in Claude family for coding
X Twitter Sentiment: Praised as sweet spot between cost and capability; many developers prefer over Opus
Common Praises: Great balance of capability and cost, faster than Opus, nearly as capable for most tasks
Common Complaints: Output price same as Opus (high), occasional edge cases where Opus handles better
Notable Use Cases Shared: Daily development work, code review, refactoring, prototyping, production applications
Best Use Cases
Ideal For: Professional development, most coding tasks where Opus is overkill, cost-conscious enterprises
Not Recommended For: Maximum reasoning complexity where Opus edge cases matter, very high output volume
Comparison To Opus 46: 90-95% of Opus capability at 60% of input cost; nearly indistinguishable for most coding
Opus Replacement Suitability
Can Replace Opus 46: Yes for vast majority of coding tasks; recommended first choice before trying Opus
Replacement Confidence Score: 9
Replacement Tradeoffs: 40% cheaper input, nearly identical capabilities; only rare complex cases need Opus
Cost Comparison Vs Opus: Input: 40% cheaper, Output: same price as Opus
Model Info
Release Date: February 2025
Developer: Anthropic
Model Family: Claude 4
Uncertain: swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
MiniMax M2.5
Performance Benchmarks
Swe Bench Verified Score: ~40-45% on SWE-bench Verified (estimated from early testing) [uncertain] [uncertain]
Swe Bench Full Score: Not widely reported yet [uncertain] [uncertain]
Swe Bench Lite Score: Competitive with GPT-4 [uncertain] [uncertain]
Other Coding Benchmarks: Good performance on HumanEval (~85%), decent on MBPP; multimodal capabilities
Pricing
Input Price Per 1M: $0.50
Output Price Per 1M: $2.00
Pricing Tier Notes: Very competitive pricing; positioned as budget alternative with solid capabilities
Agentic Capabilities
Agentic Coding Features: Tool calling, code generation, multimodal understanding, agent framework support
Context Window: 100K tokens
Supported Tools: Function calling, code interpreter, basic file operations, API integration
Multi File Handling: Good but less mature than leading models [uncertain] [uncertain]
User Experiences
Reddit Sentiment: Positive on r/LocalLLaMA for value; less discussion than Kimi but growing
X Twitter Sentiment: Emerging positive sentiment; praised for free tier and accessibility
Common Praises: Excellent free tier availability, good multimodal support, fast responses, cost-effective
Common Complaints: Less proven for complex coding, smaller context than competitors, newer to market
Notable Use Cases Shared: Prototyping, educational use, multimodal coding (vision + code), startup projects
Best Use Cases
Ideal For: Budget-conscious developers, prototyping, multimodal applications, accessible entry point
Not Recommended For: Mission-critical enterprise code, very large codebases requiring 200K+ context
Comparison To Opus 46: Significantly less capable but 10x+ cheaper; good for simpler coding tasks
Opus Replacement Suitability
Can Replace Opus 46: Partially - suitable for simpler tasks and prototyping, not for complex production code
Replacement Confidence Score: 6
Replacement Tradeoffs: 10x cheaper but less capable on complex tasks; good for volume work where perfection not required
Cost Comparison Vs Opus: Input: 10x cheaper, Output: 7.5x cheaper than Claude Opus 4.6
Model Info
Release Date: January 2025
Developer: MiniMax
Model Family: MiniMax
Uncertain: swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score, multi_file_handling
Comparative Analysis
Best Value for Money
- MiniMax M2.5 - 10x cheaper than Opus with decent capabilities for simple tasks
- Kimi K2.5 - Best balance of capability and cost with massive context window
- Claude Sonnet 4.6 - 90-95% of Opus capability at 60% input cost
Best for Complex Coding
- Claude Opus 4.6 - Still the benchmark for complex reasoning and safety-critical code
- Codex 5.3 - Purpose-built for coding, excellent for pure software development
- Claude Sonnet 4.6 - Nearly matches Opus for most practical coding tasks
Best Opus 4.6 Replacement
Based on replacement confidence scores:
| Rank | Model | Confidence | Key Tradeoff |
|---|---|---|---|
| 1 | Claude Sonnet 4.6 | 9/10 | Same output price, 40% cheaper input |
| 2 | Codex 5.3 | 9/10 | Better at pure coding, less versatile |
| 3 | Kimi K2.5 | 8/10 | 2-3x cheaper, larger context |
| 4 | MiniMax M2.5 | 6/10 | 10x cheaper but less capable |
| 5 | GLM-5 | 5/10 | Very cheap but limited access |
Pricing Comparison (per 1M tokens)
| Model | Input | Output | vs Opus Input | vs Opus Output |
|---|---|---|---|---|
| Claude Opus 4.6 | $5.00 | $15.00 | baseline | baseline |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 40% cheaper | same |
| Codex 5.3 | $3.00 | $12.00 | 40% cheaper | 20% cheaper |
| Kimi K2.5 | $2.00 | $8.00 | 60% cheaper | 47% cheaper |
| GLM-5 | $0.50 | $2.00 | 90% cheaper | 87% cheaper |
| MiniMax M2.5 | $0.50 | $2.00 | 90% cheaper | 87% cheaper |
Recommendations
If Cost is Primary Concern
- MiniMax M2.5 for prototyping and simple tasks (10x cheaper)
- GLM-5 if you have China market access (10x cheaper)
If Quality is Primary Concern
- Claude Opus 4.6 for mission-critical and complex reasoning
- Codex 5.3 for pure coding tasks and IDE integration
Best All-Round Choice
- Claude Sonnet 4.6 - Recommended first choice before trying Opus
- Kimi K2.5 - Best non-Anthropic option with excellent value