OpenCode Review: Benchmarking the 60k-Star Claude Code Alternative

With OpenCode crossing 60,000 stars on GitHub, the AI coding landscape has shifted. This comprehensive benchmark review examines OpenCode and its Zen model gateway, comparing performance against Claude Code across standard benchmarks, real-world coding tasks, and multi-turn agentic workflows.

Table of Contents

1. What is OpenCode?
2. OpenCode Architecture Deep Dive
3. OpenCode Zen: The Model Gateway
4. Benchmark Methodology
5. Standard Benchmark Results
6. Real-World Coding Tests
7. Multi-Turn Agent Performance
8. GLM-4.7 Analysis
9. Big Pickle Investigation
10. Grok Code Fast Evaluation
11. OpenCode vs Claude Code Comparison
12. Performance Optimization Tips
13. Getting Started Guide
14. Future Outlook

What is OpenCode?

Repository: github.com/anomalyco/opencode Stars: 60,108 Contributors: 534+ Commits: 7,000+

OpenCode represents the most significant open-source challenge to commercial AI coding assistants. Built by neovim enthusiasts who prioritize terminal-first workflows, OpenCode has evolved from a promising project to a production-ready alternative with a thriving ecosystem.

Core Features

Terminal-First Design Unlike IDE-integrated tools, OpenCode embraces the terminal as the primary interface. This approach appeals to developers who prefer command-line workflows, vim/neovim users, and those working over SSH connections.

Provider-Agnostic Architecture OpenCode works with virtually any AI provider:

• Anthropic Claude (all model variants)
• OpenAI GPT-4 and GPT-4 Turbo
• Google Gemini
• Groq (for speed optimization)
• xAI Grok
• Local models via Ollama, LM Studio, llama.cpp

This flexibility addresses one of the primary concerns developers have with commercial tools: provider lock-in.

Dual Agent System OpenCode implements a sophisticated dual-mode architecture:

Build Mode Full capabilities enabled:

• File system read/write access
• Shell command execution
• Dependency installation
• Test execution
• Build operations

Plan Mode Read-only operations:

• Code analysis
• Architecture review
• Implementation planning
• No destructive operations

This separation enables safer AI assistance—get planning help without risking unintended modifications.

LSP Integration Language Server Protocol support provides:

• Type information awareness
• Symbol navigation
• Reference tracking
• Diagnostic understanding
• Intelligent completions

Client/Server Architecture OpenCode can run as a server while being controlled from:

• Local terminals
• Remote machines
• Web interfaces
• Mobile devices

This enables workflows like running OpenCode on a powerful server while interacting from a laptop.

OpenCode Architecture Deep Dive

Understanding OpenCode's architecture explains both its strengths and limitations.

The Agent Loop

OpenCode implements a standard tool-using agent architecture:

User Request
    ↓
Language Model (reasoning)
    ↓
Tool Selection
    ↓
Tool Execution
    ↓
Result Processing
    ↓
Response or Next Action

The language model serves as the reasoning engine, deciding which tools to use, in what order, and with what parameters. Tools provide capabilities the model lacks: file system access, code execution, and information retrieval.

Tool Implementation

OpenCode's default tool set includes:

Bash Tool Execute shell commands with:

• Working directory tracking
• Environment variable handling
• Output capture and streaming
• Timeout management
• Error handling

Read Tool Read files with:

• Line range specification
• Binary file detection
• Encoding handling
• Size limits

Write Tool Create files with:

• Directory creation
• Backup generation
• Permission handling
• Content validation

Edit Tool Modify files with:

• Search and replace
• Line insertion/deletion
• Multi-edit transactions
• Conflict detection

Glob Tool Find files with:

• Pattern matching
• Recursive search
• Exclusion patterns
• Type filtering

Grep Tool Search content with:

• Regex support
• Context lines
• File type filtering
• Output formatting

Configuration System

OpenCode uses a hierarchical configuration:

Global Configuration (~/.config/opencode/config.yaml)

• Provider API keys
• Default model settings
• Global tool configurations
• UI preferences

Project Configuration (.opencode.yaml)

• Project-specific models
• Custom tools
• MCP server definitions
• Context files

Session Configuration

• Runtime model switching
• Temporary overrides
• Debug settings

Extension System

OpenCode supports extensions through:

MCP Servers Model Context Protocol servers provide:

• Custom tools
• External data sources
• Integration bridges
• Specialized capabilities

Custom Tools Define project-specific tools:

• Shell scripts
• Python functions
• External APIs
• Database queries

Hooks Automation triggers:

• Pre-command hooks
• Post-response hooks
• Error handlers
• Logging hooks

OpenCode Zen: The Model Gateway

OpenCode Zen is what elevates OpenCode from interesting to compelling. It is a curated gateway that benchmarks specific model/provider combinations and exposes only verified configurations.

Why Zen Matters

The same model can perform differently depending on:

• Provider implementation
• API version
• Temperature settings
• System prompt handling
• Context window management

Zen solves this inconsistency by testing each combination and exposing only configurations that meet quality thresholds.

Currently Available Models

Important Notes:

• GLM-4.7 and Big Pickle include data collection during free periods
• Availability and pricing subject to change
• Free tiers may have rate limits

How Zen Works

1. Model Registration: Providers submit models for inclusion
2. Benchmark Suite: Automated testing across standardized tasks
3. Quality Gates: Minimum performance thresholds
4. Ongoing Monitoring: Continuous quality verification
5. Public Exposure: Passing models available via Zen gateway

This approach ensures developers can trust Zen recommendations.

Benchmark Methodology

Effective benchmarking requires multiple perspectives. This review employed:

Standard Benchmarks

SWE-bench Real GitHub issues from popular repositories:

• Tests understanding of existing codebases
• Requires reading, reasoning, and patching
• Includes multilingual variant

LiveCodeBench V6 Writing, executing, and debugging code:

• Multiple languages tested
• Compilation verification
• Test case execution

HumanEval Python code generation:

• Function implementation from docstrings
• Test case verification
• Clean code expectations

τ²-Bench Multi-turn reasoning:

• Extended conversations
• Reasoning preservation
• Error recovery

Real-World Testing

Benchmarks measure specific capabilities. Real-world testing measures practical utility:

Type Complexity TypeScript type narrowing, discriminated unions, generics

Async Patterns Pipelines, rate limiting, error handling, cancellation

Multi-File Refactoring Cross-file changes, import management, test updates

Extended Debugging 10+ turn debugging sessions requiring maintained context

Scoring Criteria

Each test scored on:

• Correctness: Does the code work?
• Quality: Is the code clean and idiomatic?
• Efficiency: Is performance reasonable?
• Completeness: Are edge cases handled?
• Maintainability: Is the code easy to modify?

Standard Benchmark Results

SWE-bench Performance

SWE-bench tests models on real GitHub issues. Results:

Analysis: GLM-4.7 leads on standard SWE-bench, demonstrating strong code understanding. However, Claude's multilingual advantage becomes significant for international codebases. The gap between open-source and commercial options has narrowed considerably.

LiveCodeBench V6 Results

Testing code writing, execution, and debugging:

Analysis: GLM-4.7 surpassing Claude Sonnet 4.5 represents a significant milestone. Open-source models reaching commercial performance levels changes the competitive landscape fundamentally.

HumanEval Python Results

Pure Python code generation:

Analysis: Claude maintains leadership on Python generation, but the gap has narrowed. For most practical tasks, all models perform adequately.

τ²-Bench Multi-Turn Results

Extended conversation reasoning:

Analysis: GLM-4.7's "Preserved Thinking" feature provides significant advantage in multi-turn scenarios. This matters enormously for agentic coding where conversations extend across many turns.

Real-World Coding Tests

Benchmarks measure specific capabilities. Real-world testing measures practical utility across varied tasks.

Test 1: TypeScript Type Narrowing

Task: Refactor a union type handler with discriminated unions and type guards. The existing code used repetitive instanceof checks. The goal: clean discriminated union with exhaustive handling.

Results:

Key Observations:

• GLM-4.7 produced the most TypeScript-idiomatic solution
• All models understood the refactoring goal
• Difference was in elegance, not correctness

Test 2: Python Async Pipeline

Task: Build an async data pipeline with rate limiting, retries, graceful shutdown, and proper resource cleanup. Requirements: handle backpressure, exponential backoff, cancellation support.

Results:

Key Observations:

• GLM-4.7 demonstrated strongest async patterns knowledge
• Grok's speed advantage visible (faster response time)
• All models handled core requirements well

Test 3: Multi-File Refactor

Task: Extract a service layer from a monolithic Next.js API route. Changes span 5 files: route handler, service module, types file, tests, and imports.

Results:

Key Observations:

• Multi-file operations reveal agent capabilities
• GLM-4.7's reasoning depth helped maintain consistency
• All models required some human review

Test 4: Extended Debugging Session

Task: Debug a failing test suite requiring 10+ conversation turns. Root cause: race condition in async code combined with incorrect mock setup.

Results:

Key Observations:

• GLM-4.7's Preserved Thinking maintains quality across turns
• Other models showed some degradation after turn 7-8
• This mirrors the τ²-Bench results

Aggregate Real-World Scores

Multi-Turn Agent Performance

Extended agentic workflows represent the most demanding use case. This section examines performance across sustained interactions.

The Multi-Turn Challenge

AI coding agents typically:

• Start strong with fresh context
• Degrade as conversations extend
• Lose track of earlier decisions
• Repeat mistakes or suggestions
• Become less coherent after 10+ turns

This "context degradation" problem affects all models but to varying degrees.

Preservation Techniques

Different models employ different strategies:

GLM-4.7: Preserved Thinking Maintains explicit reasoning chains across turns. The model tracks its thought process and references earlier reasoning when making new decisions.

Claude: Extended Context Uses large context windows (200k tokens) to maintain raw conversation history. Effective but computationally expensive.

Grok: Compression Summarizes earlier context to fit more information. Trades detail for coverage.

Performance Comparison at Turn Milestones

Implications:

• For quick tasks (1-5 turns), all models perform comparably
• Extended debugging or refactoring favors GLM-4.7
• Knowing model limitations enables better tool selection

GLM-4.7 Analysis

GLM-4.7 deserves detailed examination as the current open-source leader for agentic coding.

Technical Specifications

• Context Window: 128k tokens
• Provider: Z.AI
• Architecture: Transformer-based with thinking preservation
• Training Focus: Code and reasoning

Distinctive Features

Preserved Thinking The signature feature. GLM-4.7 maintains explicit reasoning chains:

• Tracks hypotheses across turns
• References earlier conclusions
• Builds on prior analysis
• Reduces redundant exploration

Think-Before-Acting Built-in planning before execution:

• Can be enabled/disabled per request
• Trades speed for accuracy
• Particularly valuable for complex tasks

Terminal Optimization Designed specifically for terminal-based agents:

• Terminal Bench 2.0: 41% (+16.5% over 4.6)
• Optimized for command-line workflows
• Better shell command generation

Speed Characteristics

Users report GLM-4.7 is significantly faster than its predecessor. Testing confirms:

• Response initiation: Competitive with Grok
• Token generation: Slightly slower than Grok
• Overall workflow: Acceptable for interactive use

Limitations

Free Tier Restrictions

• Data collection during free period
• Rate limits may apply
• Future pricing uncertain

Multilingual Performance

• 66.7% on SWE-bench Multilingual
• Behind Claude's 80%
• English-centric training visible

Big Pickle Investigation

Big Pickle presents an interesting case study in the rapidly evolving AI landscape.

Identity Mystery

Community speculation suggests Big Pickle may be GLM-4.6 under a different name:

• Same 200k context window
• Similar behavior patterns
• GitHub Issue #4276 discusses this theory

The true identity matters less than performance characteristics.

Performance Profile

Best Use Cases

Large Codebase Work 200k context enables working with substantial code volumes without truncation.

Documentation Tasks Generating comprehensive documentation benefits from large context.

Code Review Reviewing multiple files simultaneously works well with extended context.

Limitations

Data Collection Free tier includes data collection. Consider implications for sensitive code.

Speed Not optimized for rapid iteration. Better for thoughtful, comprehensive tasks.

Grok Code Fast Evaluation

Grok Code Fast represents xAI's entry into specialized coding models.

Performance Metrics

• 85.2% HumanEval Python
• 93% coding accuracy
• 75% instruction following
• 100% reliability across benchmarks

Language Support

Built from scratch with programming-rich corpus:

• TypeScript: Excellent
• Python: Excellent
• Java: Very Good
• Rust: Very Good
• C++: Good
• Go: Good

Multi-language performance: 77% (vs Claude's 80%)

Speed Advantage

The "Fast" designation is earned:

• Fastest response initiation
• High token generation rate
• Optimized for rapid iteration

Best Use Cases

Rapid Prototyping When speed matters more than perfection, Grok excels.

Quick Iterations Fast feedback loops benefit from rapid responses.

Standard Tasks Well-understood coding patterns execute quickly.

Limitations

Complex Reasoning Extended reasoning tasks favor GLM-4.7 or Claude.

Multi-Turn Degradation Performance drops after extended conversations.

Multilingual 77% vs 80% disadvantage for international codebases.

OpenCode vs Claude Code Comparison

Head-to-head comparison reveals trade-offs:

When to Choose OpenCode

Budget Constraints OpenCode + free Zen models provides capable AI coding at zero cost.

Privacy Requirements Local model support enables fully private workflows.

Provider Flexibility Switch between providers without changing tools.

Experimentation Try different models easily to find optimal fits.

When to Choose Claude Code

Maximum Capability Claude Opus/Sonnet still leads on complex tasks.

Official Support Commercial support matters for enterprise deployment.

IDE Integration Official plugins provide smoother integration.

Consistency Single-provider consistency simplifies workflows.

Performance Optimization Tips

Maximize effectiveness regardless of chosen tool:

Model Selection Strategy

Quick Tasks (1-5 turns) Any model works. Optimize for speed with Grok.

Complex Reasoning GLM-4.7 or Claude for multi-step problems.

Large Context Big Pickle for extensive code review or documentation.

Production Work Claude for highest stakes tasks.

Prompt Engineering

Be Specific Vague requests produce vague results. Specify languages, frameworks, constraints.

Provide Context Include relevant files. Models cannot infer hidden requirements.

Iterative Refinement Start broad, refine based on output. Do not expect perfection first try.

Workflow Optimization

Tool Selection Use the right model for each task. Do not use premium models for trivial tasks.

Parallel Execution OpenCode supports running multiple agents. Parallelize independent tasks.

Context Management Prune irrelevant context. Long contexts slow responses and may confuse models.

Getting Started with OpenCode

Installation

# Via npm
npm install -g opencode

# Via Homebrew
brew install opencode

# Via cargo
cargo install opencode

Basic Configuration

Create ~/.config/opencode/config.yaml:

providers:
  anthropic:
    api_key: ${ANTHROPIC_API_KEY}
  openai:
    api_key: ${OPENAI_API_KEY}

default_model: zen/glm-4.7

First Run

# Start with Zen (free models)
opencode --zen

# Or with specific model
opencode --model zen/glm-4.7

Key Commands

• Ctrl+A: Switch models
• Ctrl+F: Favorite current model
• Ctrl+P: Toggle plan mode
• Ctrl+B: Toggle build mode

Future Outlook

The AI coding tool landscape continues evolving rapidly.

Expected Developments

Model Improvements GLM-4.8 and beyond will likely close remaining gaps with Claude.

Orchestration Multi-agent workflows will become more sophisticated.

Integration Deeper IDE and infrastructure integration expected.

Memory Persistent memory will become standard across tools.

Competitive Dynamics

Open Source Momentum Community development accelerates. Commercial advantages narrow.

Provider Diversification More providers entering the market increases options and competition.

Specialization Expect models optimized for specific languages or domains.

Conclusion

OpenCode has earned its 60,000+ stars. The combination of:

• Open-source flexibility
• OpenCode Zen's free model access
• GLM-4.7's benchmark-leading performance

...creates a legitimate Claude Code alternative.

Is OpenCode better than Claude Code? For most tasks, Claude with Opus/Sonnet still edges ahead. But the gap has narrowed considerably, and the price (free for Zen models) represents compelling value.

The recommendation: Use Claude Code for highest-stakes work requiring maximum capability. Use OpenCode + GLM-4.7 for general coding tasks where the free tier provides more than adequate performance.

For developers currently locked into single-provider tools, OpenCode offers a path to flexibility without sacrificing capability. The 534+ contributors and 7,000+ commits indicate a healthy, growing ecosystem.

The future of AI-assisted coding is increasingly open—and OpenCode is leading that charge.

Sources and References

• OpenCode Repository — 60.1k stars
• OpenCode Zen — Model gateway
• GLM-4.7 Official Benchmarks — Z.AI
• GLM-4.7 HuggingFace
• Grok Code Fast Evaluation — 16x Engineer
• Grok Code Fast Official — xAI
• LLM Stats - GLM-4.7
• LLM Stats - Grok Code Fast
• SWE-bench Leaderboard
• LiveCodeBench

Benchmark data collected January 2026. Model performance and availability subject to change.