Tagline: Six terminal coding agents, three architectures, one question — which one should you actually use in 2026?
Purpose
What problem does this solve? The terminal AI coding agent space exploded in 2025-2026. Claude Code, Codex CLI, OpenCode, Aider, Pi, and Gemini CLI all claim to be the best — but they take fundamentally different approaches to model support, cost, architecture, and privacy. This guide cuts through the noise.
Who is it for? Developers who want to pick the right coding agent for their workflow, especially those interested in running local models or avoiding vendor lock-in.
Key Findings
- Claude Code leads on raw coding ability (Sonnet 4.6/Opus 4.7) but is Claude-only and $100-200/mo
- OpenCode (MIT, 156K stars) offers the most model flexibility — 75+ providers, BYOK, free tool
- Codex CLI (Apache 2.0) now supports Ollama locally alongside GPT-5.3-Codex-Spark
- Qwen3-Coder-Next (80B/3B MoE) hits 70.6% SWE-Bench and can run on 2x consumer GPUs
- The 2026 trend: multi-agent architectures with sub-agents, parallel execution, and model routing
Key Results / Achievements / Techniques / Concepts
- SWE-Bench Verified scores across all harnesses and models
- Total cost of ownership: 20/mo vs $100-200/mo
- Local model viability: Qwen3.6-27B fits single GPU, Qwen3-Coder-Next fits 2x GPU
Tools & Technologies
| Tool / Technology | Description | Link / Port |
|---|---|---|
| Claude Code | Anthropic’s terminal-first coding agent. Claude-only, sub-agent architecture | claude CLI |
| OpenAI Codex CLI | OpenAI’s Rust-based coding agent. Apache 2.0, cloud+local, Ollama support | codex CLI |
| OpenCode | MIT open-source, TypeScript+Bun+Go TUI. 75+ providers, client/server | opencode CLI |
| Aider | OG open-source coding agent. Apache 2.0, 100+ models via LiteLLM | aider CLI |
| Pi | MIT open-source, 324 models across 15+ providers | pi CLI |
| Gemini CLI | Google’s Apache 2.0 agent, 1,000 free req/day | gemini CLI |
| Qwen3-Coder-Next | 80B/3B MoE, 70.6% SWE-Bench, Apache 2.0, 256K context | Ollama / vLLM |
| Qwen3.6-27B | Dense 27B, 68.9% SWE-Bench, fits single GPU at Q4 | Ollama / llama.cpp |
| Obsidian | Knowledge base & documentation | Local Vault |
Architecture & Workflow
graph TD subgraph "Architecture Comparison" CC[Claude Code] -->|Local only, Claude models| CC1[Sub-agents: Explore/Plan/General] CX[Codex CLI] -->|Local + Cloud sandbox| CX1[GPT-5.3-Codex-Spark on Cerebras] OC[OpenCode] -->|Client/Server, BYOK| OC1[75+ providers, any model] AI[Aider] -->|Local, LiteLLM| AI1[100+ models via API/Ollama] end subgraph "Local Model Layer" Q3[Qwen3-Coder-Next 80B/3B] -->|Via Ollama/vLLM| CC Q3 --> CX Q3 --> OC Q3 --> AI Q36[Qwen3.6-27B] --> CC Q36 --> CX Q36 --> OC Q36 --> AI end
Agent Capability Matrix
| Feature | Claude Code | Codex CLI | OpenCode | Aider | Pi |
|---|---|---|---|---|---|
| License | Proprietary | Apache 2.0 | MIT | Apache 2.0 | MIT |
| Model flexibility | Claude-only | OpenAI + Ollama | 75+ providers | 100+ via LiteLLM | 324 models |
| Local models | No | Yes (Ollama) | Yes (Ollama) | Yes (Ollama) | Yes (Ollama) |
| Sub-agents | Yes (3 types) | No | No | No | No |
| Cloud execution | No | Yes (sandbox) | No | No | No |
| SWE-Bench (best model) | ~72.7% (Opus 4.7) | ~69.1% (GPT-5.3) | Varies by model | Varies by model | Varies by model |
| Cost | $20-200/mo | $20/mo + API | Free (BYOK) | Free (BYOK) | Free (BYOK) |
| GitHub Stars | ~30K | ~80K | ~156K | ~44K | ~45K |
Cost Analysis
Cost Caveat
Subscription tiers and agent metering are especially unstable in 2026. Claude Code docs describe wide per-developer cost variance, while Gemini CLI publishes request-based quotas that differ by auth method.
| Agent | Entry Cost | High-Usage Cost | Model Cost (per 1M tokens) |
|---|---|---|---|
| Claude Code | $20/mo Pro | $200/mo Max | 15.00 (Sonnet) |
| Codex CLI | $20/mo Plus | $200/mo Pro | 15.00 (Codex) |
| OpenCode | Free | Free (BYOK) | Provider-dependent |
| Aider | Free | Free (BYOK) | Provider-dependent |
| Pi | Free | Free (BYOK) | Provider-dependent |
| Gemini CLI | Free | Free (1K req/day) | Free tier / pay after |
Local Model Recommendations
Local-Agent Caveat
“Supports Ollama” does not always mean “works well as a coding agent.” Tool-calling reliability, context handling, edit format, and model-specific prompts matter as much as raw benchmark scores.
| Model | Params | Active | SWE-Bench | VRAM (Q4) | Hardware |
|---|---|---|---|---|---|
| Qwen3-Coder-Next | 80B | 3B (MoE) | 70.6% | ~52 GB | 2x RTX 5090 / 1x H100 |
| Qwen3.6-27B | 27B | 27B (dense) | 68.9% | ~17 GB | 1x RTX 5090 / 4090 |
| DeepSeek V4-Flash | ~300B MoE | ~20B | 78.4% | ~150 GB | 2x H100 |
| Gemma 2 27B | 27B | 27B | ~55% | ~17 GB | 1x RTX 5090 |
| Phi-4 | 14B | 14B | ~52% | ~9 GB | 1x RTX 4090/7800 XT |
Implementation Steps
Step 1: Define Your Constraints
- Budget: 20/mo → Codex/Claude Pro; $200/mo → Claude Max
- Privacy: Local-only → OpenCode + Ollama; Cloud ok → Codex/Claude
- Model preference: Claude → Claude Code; OpenAI → Codex; Any → OpenCode/Aider/Pi
Step 2: Install Your Agent
# Claude Code
npm install -g @anthropic-ai/claude-code
# Codex CLI
npm install -g @openai/codex
# OpenCode
npm install -g opencode
# Aider
pip install aider-chat
# Pi
npm install -g @earendil/piStep 3: Configure Local Models
# Install Ollama
ollama pull qwen3.6-27b
ollama pull qwen3-coder-next
# Point your agent at local model
# OpenCode: opencode --model ollama/qwen3.6-27b
# Aider: aider --model ollama/qwen3.6-27b
# Codex: codex --model ollama --ollama-model qwen3.6-27bDecision Heuristics
| Constraint | Best Starting Point | Why |
|---|---|---|
| Best raw coding quality | Claude Code or Codex with frontier cloud model | Strong model + mature agent harness |
| Lowest vendor lock-in | OpenCode or Aider | Provider flexibility and BYOK/local options |
| Local-first privacy | OpenCode/Aider/Codex via Ollama-compatible endpoint | Keeps code and inference local when configured correctly |
| Big refactors | Agent with planning, approvals, and strong context management | Harness quality matters more than model leaderboard deltas |
| Learning/debugging | Aider or OpenCode | Transparent CLI workflows make failure modes easier to inspect |
Reflections & Lessons Learned
What the Data Shows
- No single best agent — the right choice depends entirely on your model preference, budget, and privacy needs
- Local models are viable in 2026 — Qwen3.6-27B (68.9% SWE-Bench) and Qwen3-Coder-Next (70.6%) are good enough for ~75-80% of routine coding tasks
- Cost arbitrage is massive — Qwen3 Coder Plus at 3.00/M is a 10x difference
- Multi-agent is the next frontier — Claude Code’s sub-agent architecture (Explore/Plan/General) is the most sophisticated, but OpenCode’s snapshot undo system is a unique safety innovation
Caveats
- SWE-Bench scores don’t tell the whole story — real-world coding quality depends on context length, tool call accuracy, and subtle reasoning
- Claude Code’s Claude-only lock-in is a growing concern as GPT-5.5, Gemini 3 Pro, and DeepSeek all become competitive
- Local model setup still requires technical comfort with ROCm/CUDA/Ollama configuration
Future Roadmap - Potential Applications
- Real hardware benchmarks on RX 7800 XT (16GB) — compare Qwen3.6-27B, Qwen3-Coder-Next, DeepSeek V4-Flash across agents
- Test Claude Code sub-agent workflow against OpenCode parallel agent teams
- Evaluate Codex CLI’s cloud sandbox for CI/CD integration vs local-only approaches
- Monitor OpenCode’s ACP protocol adoption as a standard for agent-client communication
- Track Qwen3-Coder-Next improvements — it’s the most rapidly improving local coding model