OpenCode Models Guide

Comprehensive guide to OpenCode Go and Zen models with capabilities, costs, and routing recommendations.

Sources: OpenCode Go Documentation | OpenCode Zen Documentation

Quick Cost Comparison

💰 Cost-conscious routing matters! Qwen3.5 Plus gives you 10,200 requests per $12, while GLM-5.1 gives you only 880 — that's 11.6x fewer requests for the same budget.

Model	Provider	Requests per $12 (5hr)	Cost Efficiency	Quality
Qwen3.5 Plus	Go	10,200	★★★★★	★★☆☆☆
MiniMax M2.5	Go	6,300	★★★★★	★★☆☆☆
Qwen3.7 Plus	Go	4,300	★★★★★	★★★☆☆
MiniMax M2.7	Go	3,400	★★★★☆	★★★☆☆
MiniMax M3	Go	3,200	★★★★☆	★★★☆☆
Qwen3.6 Plus	Go	3,300	★★★★☆	★★★☆☆
MiMo-V2.5	Go	2,150	★★★☆☆	★★★☆☆
MiMo-V2.5-Pro	Go	1,290	★★☆☆☆	★★★★☆
Kimi K2.5	Go	1,850	★★☆☆☆	★★★★☆
Kimi K2.6	Go	~1,150	★☆☆☆☆	★★★★★
Kimi K2.7 Code	Go	1,350	★☆☆☆☆	★★★★★
GLM-5	Go	1,150	★☆☆☆☆	★★★★☆
GLM-5.1	Go	880	☆☆☆☆☆	★★★★★
GLM-5.2	Go	880	☆☆☆☆☆	★★★★★
Qwen3.7 Max	Go	950	☆☆☆☆☆	★★★★☆

Providers

OpenCode Go (`opencode-go`)

Subscription-based ($5/month then $10/month)
OpenAI Chat Completions and Anthropic Messages endpoints
Best for: Most use cases, cost-effective models

OpenCode Zen (`opencode-zen`)

Pay-as-you-go pricing
Additional endpoint formats: Responses (GPT), Gemini
Best for: GPT models, Gemini models, premium Anthropic models

AWS Bedrock (`aws-bedrock`)

Models hosted on AWS Bedrock Mantle
Supports OpenAI Chat Completions (default) and Anthropic Messages formats
Set wire_format: "anthropic" for Claude and other Anthropic-native models
Best for: Models deployed on your own AWS infrastructure

OpenRouter Models

OpenRouter provides unified access to 100+ models from multiple providers through a single API endpoint. Instead of managing separate API keys and endpoints for each provider, you can access models from OpenAI, Anthropic, Google, Meta, Mistral, and many others through one integration.

Key Benefits

Benefit	Description
Unified API	Single OpenAI-compatible Chat Completions endpoint for all models
100+ Models	Access to models from 20+ providers without separate integrations
Dynamic Catalog	New models automatically available when the catalog is updated
Pay-as-you-go	Per-token pricing with no subscription required
Smart Routing	Automatic fallback to alternative providers if a model is unavailable
Cost Optimization	Route to the cheapest or fastest available provider

Model Naming Convention

OpenRouter uses a provider/model-name format that identifies both the original provider and the specific model:

Format	Example	Description
`openai/gpt-4o`	`openai/gpt-4o`	GPT-4o via OpenAI
`openai/gpt-4o-mini`	`openai/gpt-4o-mini`	GPT-4o Mini via OpenAI
`anthropic/claude-3.5-sonnet`	`anthropic/claude-3.5-sonnet`	Claude 3.5 Sonnet via Anthropic
`anthropic/claude-3-opus`	`anthropic/claude-3-opus`	Claude 3 Opus via Anthropic
`google/gemini-2.5-pro`	`google/gemini-2.5-pro`	Gemini 2.5 Pro via Google
`google/gemini-2.0-flash`	`google/gemini-2.0-flash`	Gemini 2.0 Flash via Google
`meta-llama/llama-3.3-70b`	`meta-llama/llama-3.3-70b`	Llama 3.3 70B via Meta
`mistral/mistral-large`	`mistral/mistral-large`	Mistral Large via Mistral AI
`x-ai/grok-2`	`x-ai/grok-2`	Grok 2 via xAI
`deepseek/deepseek-chat`	`deepseek/deepseek-chat`	DeepSeek V3 via DeepSeek

Popular OpenRouter Models

Model	Provider	Context Window	Input Cost ($/M)	Output Cost ($/M)	Best For
Claude 3.5 Sonnet	Anthropic	200K	$3.00	$15.00	Complex reasoning, coding, analysis
Claude 3 Opus	Anthropic	200K	$15.00	$75.00	Maximum quality, difficult tasks
GPT-4o	OpenAI	128K	$2.50	$10.00	General purpose, vision tasks
GPT-4o Mini	OpenAI	128K	$0.15	$0.60	Cost-effective, high volume
Gemini 2.5 Pro	Google	1M	$1.25	$10.00	Long context, coding, reasoning
Gemini 2.0 Flash	Google	1M	$0.10	$0.40	Fast responses, cost efficiency
Llama 3.3 70B	Meta	128K	$0.12	$0.30	Open source, customizable
Llama 3.1 405B	Meta	128K	$0.80	$1.60	Open source, high quality
Mistral Large	Mistral	128K	$2.00	$6.00	European provider, GDPR compliant
Grok 2	xAI	128K	$2.00	$10.00	Real-time knowledge, humor
DeepSeek V3	DeepSeek	64K	$0.07	$1.10	Cost efficiency, coding
DeepSeek R1	DeepSeek	64K	$0.55	$2.19	Reasoning, chain-of-thought

Discovering Available Models

Browse the complete model catalog at: https://openrouter.ai/models

The catalog includes detailed information for each model:

Pricing: Input and output costs per million tokens
Context window: Maximum tokens the model can process
Tool calling: Whether the model supports function calling
Vision: Whether the model supports image input
Provider routing: Available providers for each model

You can also fetch available models programmatically via the OpenRouter API:

curl https://openrouter.ai/api/v1/models \
  -H "Authorization: Bearer $OPENROUTER_API_KEY"

Configuring OpenRouter Models

Add OpenRouter models to your routatic-proxy configuration:

{
  "models": {
    "default": {
      "provider": "openrouter",
      "model_id": "anthropic/claude-3.5-sonnet",
      "temperature": 0.7,
      "max_tokens": 4096
    },
    "background": {
      "provider": "openrouter",
      "model_id": "openai/gpt-4o-mini",
      "temperature": 0.5,
      "max_tokens": 2048
    },
    "complex": {
      "provider": "openrouter",
      "model_id": "anthropic/claude-3-opus",
      "temperature": 0.7,
      "max_tokens": 8192
    },
    "long_context": {
      "provider": "openrouter",
      "model_id": "google/gemini-2.5-pro",
      "temperature": 0.7,
      "max_tokens": 8192
    }
  }
}

Cost-Based Routing Integration

When cost_routing.enabled is set, the selector automatically sorts models by cost and applies your routing preferences:

{
  "cost_routing": {
    "enabled": true,
    "prefer_providers": ["opencode-go", "openrouter"],
    "penalty_per_provider": {
      "openrouter": 0.05
    },
    "max_context_window": 200000
  }
}

How cost routing works with OpenRouter:

Models are sorted by combined input + output rates
Provider penalties adjust effective costs (e.g., openrouter: 0.05 adds 5%)
max_context_window filters out models that can't handle your request size
prefer_providers intersects with scenario preferences for final selection

Model Categories Available

Category	Providers	Example Models
OpenAI	OpenAI, Azure	GPT-4o, GPT-4o Mini, GPT-4 Turbo
Anthropic	Anthropic, AWS	Claude 3 Opus, Claude 3.5 Sonnet, Claude 3 Haiku
Google	Google	Gemini 2.5 Pro, Gemini 2.0 Flash, Gemini 1.5 Pro
Meta	Meta, Together, Fireworks	Llama 3.3 70B, Llama 3.1 405B, Llama 3.1 70B
Mistral	Mistral AI	Mistral Large, Mistral Medium, Mistral Small
xAI	xAI	Grok 2, Grok Beta
DeepSeek	DeepSeek	DeepSeek V3, DeepSeek R1
Specialized	Various	Qwen, Command R+, Perplexity, and many more

Example Configurations

Budget-Conscious Setup

Use cheaper models for most tasks, expensive ones only when needed:

{
  "models": {
    "background": {
      "provider": "openrouter",
      "model_id": "openai/gpt-4o-mini"
    },
    "default": {
      "provider": "openrouter",
      "model_id": "deepseek/deepseek-chat"
    },
    "complex": {
      "provider": "openrouter",
      "model_id": "anthropic/claude-3.5-sonnet"
    }
  }
}

Quality-First Setup

Prioritize quality for critical tasks:

{
  "models": {
    "default": {
      "provider": "openrouter",
      "model_id": "anthropic/claude-3.5-sonnet"
    },
    "complex": {
      "provider": "openrouter",
      "model_id": "anthropic/claude-3-opus"
    },
    "long_context": {
      "provider": "openrouter",
      "model_id": "google/gemini-2.5-pro"
    }
  }
}

Multi-Provider Fallback

Spread requests across providers for reliability:

{
  "models": {
    "default": {
      "provider": "openrouter",
      "model_id": "openai/gpt-4o"
    },
    "fallback": {
      "provider": "openrouter",
      "model_id": "anthropic/claude-3.5-sonnet"
    }
  },
  "fallbacks": {
    "default": [
      { "model_id": "openai/gpt-4o" },
      { "model_id": "anthropic/claude-3.5-sonnet" },
      { "model_id": "google/gemini-2.5-pro" }
    ]
  }
}

OpenRouter-Specific Features

Provider Routing Preferences

Request specific providers or enable automatic fallback:

{
  "model_overrides": {
    "claude-sonnet": {
      "provider": "openrouter",
      "model_id": "anthropic/claude-3.5-sonnet",
      "temperature": 0.7,
      "max_tokens": 4096,
      "extra_body": {
        "provider": {
          "order": ["Anthropic", "AWS"],
          "allow_fallbacks": true
        }
      }
    }
  }
}

Routing options:

order: Priority list of providers to try
allow_fallbacks: Whether to try other providers if primary is down
ignore: Providers to exclude from routing

How OpenRouter Model Catalog Works

OpenRouter models are resolved dynamically from the catalog system:

Catalog Location: ~/.config/routatic-proxy/catalog/catalog.json
Resolution: Models are keyed as provider/model-name (e.g., openai/gpt-4o, anthropic/claude-3.5-sonnet)
Dynamic Loading: New models are automatically available when the catalog is updated — no code changes required

Model Resolution from Catalog

The catalog system extracts the provider from the key prefix:

{
  "providers": {
    "openrouter": {
      "name": "OpenRouter",
      "base_url": "https://openrouter.ai/api/v1",
      "enabled": true
    }
  },
  "models": {
    "openrouter/anthropic/claude-3.5-sonnet": {
      "id": "openrouter/anthropic/claude-3.5-sonnet",
      "name": "Claude 3.5 Sonnet",
      "limit": { "context": 200000 },
      "rates": { "input": 3.0, "output": 15.0 },
      "tool_call": true,
      "modalities": { "input": ["text", "image"], "output": ["text"] },
      "reasoning": false
    }
  }
}

ResolvedModel.ModelID: The model name without provider prefix (anthropic/claude-3.5-sonnet)
ResolvedModel.CanonicalName: The full key (openrouter/anthropic/claude-3.5-sonnet)

Benefits Summary

Access to 100+ Models: Single API key for OpenAI, Anthropic, Google, Meta, Mistral, and more
Unified API: OpenAI-compatible Chat Completions format for all models
Automatic Fallbacks: Built-in fallback to alternative providers if a model is unavailable
Cost Optimization: Per-token pricing with routing preferences for cost efficiency
No Vendor Lock-in: Switch between providers by changing the model ID

Important: API Endpoints

⚠️ Critical: Not all models use the same API endpoint! routatic-proxy handles this automatically, but you should know:

OpenCode Go Endpoints

Models	Endpoint	Format
GLM-5, GLM-5.1, GLM-5.2, Kimi K2.5, Kimi K2.6, Kimi K2.7 Code, MiMo-V2.5, MiMo-V2.5-Pro, DeepSeek V4 Pro, DeepSeek V4 Flash	`https://opencode.ai/zen/go/v1/chat/completions`	OpenAI-compatible
MiniMax M2.5, MiniMax M2.7, MiniMax M3, Qwen3.5 Plus, Qwen3.6 Plus, Qwen3.7 Plus, Qwen3.7 Max	`https://opencode.ai/zen/go/v1/messages`	Anthropic-compatible

OpenCode Zen Endpoints

Models	Endpoint	Format
MiniMax M2.5, MiniMax M2.7, MiniMax M3, GLM-5, GLM-5.1, GLM-5.2, Kimi K2.5, Kimi K2.6, Kimi K2.7 Code, DeepSeek V4 Pro, DeepSeek V4 Flash, DeepSeek V4 Flash Free, Grok Build 0.1, Big Pickle, MiMo-V2.5 Free, North Mini Code Free, Nemotron 3 Ultra Free	`https://opencode.ai/zen/v1/chat/completions`	OpenAI-compatible
Claude models (claude-fable-5, claude-opus-4-8, claude-opus-4-7, claude-opus-4-6, claude-opus-4-5, claude-opus-4-1, claude-sonnet-4-6, claude-sonnet-4-5, claude-sonnet-4, claude-haiku-4-5, claude-3-5-haiku), Qwen models (qwen3.5-plus, qwen3.6-plus, qwen3.7-plus, qwen3.7-max)	`https://opencode.ai/zen/v1/messages`	Anthropic-compatible
GPT models (gpt-5.5, gpt-5.5-pro, gpt-5.4, gpt-5.4-pro, gpt-5.4-mini, gpt-5.4-nano, gpt-5.3-codex, gpt-5.3-codex-spark, gpt-5.2, gpt-5.2-codex, gpt-5.1, gpt-5.1-codex, gpt-5.1-codex-max, gpt-5.1-codex-mini, gpt-5, gpt-5-codex, gpt-5-nano)	`https://opencode.ai/zen/v1/responses`	OpenAI Responses
Gemini models (gemini-3.5-flash, gemini-3.1-pro, gemini-3-flash)	`https://opencode.ai/zen/v1/models/{id}`	Google Gemini

Why this matters: On the Go provider, MiniMax and Qwen models use Anthropic format natively. On Zen, only Claude and Qwen use the Anthropic endpoint — MiniMax uses chat completions. routatic-proxy handles all routing automatically.

Using OpenCode Zen

To use Zen models, set "provider": "opencode-zen" in your model config:

{
  "models": {
    "default": {
      "provider": "opencode-zen",
      "model_id": "kimi-k2.6",
      "temperature": 0.7,
      "max_tokens": 4096
    }
  }
}

Zen-Specific Models (50+ total)

All OpenCode Go models are also available on Zen. Zen additionally offers:

Claude Models (Anthropic endpoint): claude-fable-5, claude-opus-4-8, claude-opus-4-7, claude-opus-4-6, claude-opus-4-5, claude-opus-4-1, claude-sonnet-4-6, claude-sonnet-4-5, claude-sonnet-4, claude-haiku-4-5, claude-3-5-haiku
GPT Models (Responses endpoint): gpt-5.5, gpt-5.5-pro, gpt-5.4, gpt-5.4-pro, gpt-5.4-mini, gpt-5.4-nano, gpt-5.3-codex, gpt-5.3-codex-spark, gpt-5.2, gpt-5.2-codex, gpt-5.1, gpt-5.1-codex, gpt-5.1-codex-max, gpt-5.1-codex-mini, gpt-5, gpt-5-codex, gpt-5-nano
Gemini Models (Gemini endpoint): gemini-3.5-flash, gemini-3.1-pro, gemini-3-flash
Free Tier (chat completions): deepseek-v4-flash-free, big-pickle, mimo-v2.5-free, north-mini-code-free, nemotron-3-ultra-free

Deprecated Zen Models

The following models are deprecated and will be removed:

Model	Deprecation Date	Replacement
GPT 5.2 Codex	July 23, 2026	GPT 5.3 Codex
GPT 5.1 Codex	July 23, 2026	GPT 5.3 Codex
GPT 5.1 Codex Max	July 23, 2026	GPT 5.3 Codex
GPT 5.1 Codex Mini	July 23, 2026	GPT 5.3 Codex Spark
GPT 5 Codex	July 23, 2026	GPT 5.3 Codex
Claude Sonnet 4	June 15, 2026	Claude Sonnet 4.5/4.6
GLM 5	May 14, 2026	GLM 5.1/5.2
MiniMax M2.1	March 15, 2026	MiniMax M2.5/M2.7/M3
GLM 4.7	March 15, 2026	GLM 5/5.1/5.2
GLM 4.6	March 15, 2026	GLM 5/5.1/5.2
Gemini 3 Pro	March 9, 2026	Gemini 3.1 Pro
Kimi K2 Thinking	March 6, 2026	Kimi K2.5/K2.6/K2.7 Code
Kimi K2	March 6, 2026	Kimi K2.5/K2.6/K2.7 Code
Claude Haiku 3.5	Feb 16, 2026	Claude Haiku 4.5
Qwen3 Coder 480B	Feb 6, 2026	Qwen3.7 Plus/Max

DeepSeek V4 Pro and Flash are OpenAI-compatible on both Go and Zen providers. DeepSeek V4 Flash Free is the free Zen variant. routatic-proxy transforms Claude Code's Anthropic request into OpenAI Chat Completions format, including tools, tool results, thinking history, reasoning_effort, and thinking.

For Claude Code and OpenCode-style agent workflows, DeepSeek V4 supports max thinking mode with:

{
  "model_id": "deepseek-v4-pro",
  "reasoning_effort": "max",
  "thinking": {
    "type": "enabled"
  }
}

Use deepseek-v4-pro for default, complex, thinking, and long-context routing. Use deepseek-v4-flash for fast, background, or subagent-style workloads.

To route DeepSeek V4 Pro through Zen (free tier) instead of Go (paid), add a model_overrides entry:

{
  "model_overrides": {
    "deepseek-v4-pro": {
      "provider": "opencode-zen",
      "model_id": "deepseek-v4-pro",
      "temperature": 0.7,
      "max_tokens": 8192,
      "reasoning_effort": "max",
      "thinking": {
        "type": "enabled"
      }
    }
  }
}

Cost-Conscious Routing Strategy

Default to Cheap, Upgrade When Necessary

Most requests should use cheap models. Only upgrade to expensive models when:

Task complexity demands it (multi-step reasoning, architecture)
You've tried cheaper models and they failed
Code quality is critical (production code review)

Recommended Routing

{
  "models": {
    "background": {
      // Simple operations
      "model_id": "qwen3.5-plus",
      "max_tokens": 2048
    },
    "default": {
      // Better quality, moderate cost
      "model_id": "kimi-k2.6",
      "max_tokens": 4096
    },
    "long_context": {
      // Large files only
      "model_id": "minimax-m2.5",
      "context_threshold": 80000
    },
    "think": {
      // Reasoning tasks
      "model_id": "glm-5",
      "max_tokens": 8192
    },
    "complex": {
      // Complex architecture only
      "model_id": "glm-5.1",
      "max_tokens": 4096
    },
    "fast": {
      // Streaming requests (prioritize TTFT)
      "model_id": "qwen3.6-plus",
      "max_tokens": 4096
    }
  }
}

Decision Tree

Is context > 80K tokens?
├── YES → Use MiniMax M2.5 (1M context, 6,300 req/$12)
│
Is it a complex task (architecture, refactoring, tool operations)?
├── YES → Use GLM-5.1 (880 req/$12)
│
Is it a reasoning/planning task?
├── YES → Use GLM-5 (1,150 req/$12)
│
Is it a simple background task (read file, grep, list dir, no tools)?
├── YES → Use Qwen3.5 Plus (10,200 req/$12)
│
Default → Use Kimi K2.6 (1,850 req/$12, ★★★★★) or Qwen3.6 Plus (3,300 req/$12)

Detailed Model Profiles

Budget Champions 💰

Qwen3.5 Plus — The Workhorse

Model ID: qwen3.5-plus
Cost: 10,200 requests per $12 (best value!)
Context: ~128K tokens
Quality: ★★☆☆☆ (adequate for simple tasks)
Best For:
- File reading operations
- Directory listing
- Grep/search
- Simple questions
- Bulk operations
- Background tasks
When to Use: When you need to do lots of operations cheaply

MiniMax M2.5 — Long Context on a Budget

Model ID: minimax-m2.5
Endpoint: Anthropic-compatible (/v1/messages on Go), OpenAI-compatible (/chat/completions on Zen)
Cost: 6,300 requests per $12
Context: ~1M tokens (1 million!)
Quality: ★★☆☆☆ (acceptable)
Speed: Fast
Best For:
- Very large files
- Long conversations
- Multi-file context
When to Use: When you need 1M context but want to minimize cost
Note: Uses Anthropic endpoint on Go but chat completions on Zen - routatic-proxy handles this automatically

MiniMax M3 — Latest MiniMax, 1M Context

Model ID: minimax-m3
Endpoint: Anthropic-compatible (/v1/messages on Go), OpenAI-compatible (/chat/completions on Zen)
Context: ~1M tokens
Quality: ★★★☆☆
Best For:
- Long-context tasks requiring better quality than M2.5
- Large codebase analysis
- Document processing
When to Use: When you need 1M context and want better quality than M2.5

Balanced Models (Quality + Cost)

DeepSeek V4 Pro — Agentic Coding + Max Thinking

Model ID: deepseek-v4-pro
Endpoint: OpenAI-compatible (/chat/completions)
Context: ~1M tokens
Quality: ★★★★★
Providers: Go (paid) or Zen (free tier)
Best For:
- Claude Code agent workflows
- Complex implementation and debugging
- Architecture and refactoring
- Long-context coding tasks
- Max thinking mode

Recommended Config (Go):

{
  "provider": "opencode-go",
  "model_id": "deepseek-v4-pro",
  "temperature": 0.1,
  "max_tokens": 8192,
  "reasoning_effort": "max",
  "thinking": {
    "type": "enabled"
  }
}

Recommended Config (Zen free tier):

{
  "provider": "opencode-zen",
  "model_id": "deepseek-v4-pro",
  "temperature": 0.1,
  "max_tokens": 8192,
  "reasoning_effort": "max",
  "thinking": {
    "type": "enabled"
  }
}

DeepSeek V4 Flash — Fast Agent Workloads

Model ID: deepseek-v4-flash
Endpoint: OpenAI-compatible (/chat/completions)
Context: ~1M tokens
Quality: ★★★★☆
Best For:
- Fast routing
- Background tasks
- Subagent-style work
- Fallback for DeepSeek V4 Pro

Recommended Config:

{
  "provider": "opencode-go",
  "model_id": "deepseek-v4-flash",
  "temperature": 0.1,
  "max_tokens": 4096,
  "reasoning_effort": "max",
  "thinking": {
    "type": "enabled"
  }
}

Qwen3.6 Plus — Cost-Effective General Coding ⭐ RECOMMENDED DEFAULT

Model ID: qwen3.6-plus
Endpoint: Anthropic-compatible (/v1/messages — Go), Anthropic-compatible (/v1/messages — Zen)
Cost: 3,300 requests per $12 (3.8x more than GLM-5.1!)
Context: ~128K tokens
Quality: ★★★☆☆ (good enough for most tasks)
Speed: Fast
Best For:
- General coding (default choice)
- Feature implementation
- Bug fixes
- Refactoring
When to Use: Default for cost-conscious users

Qwen3.7 Plus — Upgraded General Coding

Model ID: qwen3.7-plus
Endpoint: Anthropic-compatible (/v1/messages)
Context: ~128K tokens
Quality: ★★★★☆
Speed: Fast
Best For:
- General coding with better quality than Qwen3.6
- Feature implementation
- Bug fixes
When to Use: When you want better quality than Qwen3.6 at similar speed

Qwen3.7 Max — Maximum Quality Qwen

Model ID: qwen3.7-max
Endpoint: Anthropic-compatible (/v1/messages)
Context: ~128K tokens
Quality: ★★★★☆
Best For:
- Complex coding tasks
- When Qwen3.7 Plus isn't enough
When to Use: When you need Qwen's best quality

Kimi K2.6 — Best Quality at Balanced Cost

Model ID: kimi-k2.6
Cost: ~1,850 requests per $12
Context: ~256K tokens (successor to K2.5 with improvements)
Quality: ★★★★★ (excellent — successor improvements)
Speed: Fast
Best For:
- Complex coding tasks
- Code review
- Architecture discussions
- General-purpose default (best quality-to-cost ratio)
When to Use: Default choice — better quality than K2.5 at similar cost

Kimi K2.5 — Quality + Reasonable Cost (Predecessor)

Model ID: kimi-k2.5
Cost: 1,850 requests per $12
Context: ~256K tokens (2x most others)
Quality: ★★★★☆ (excellent)
Speed: Fast
Best For:
- Complex coding tasks
- Code review
- Architecture discussions
- When you need better quality than budget models
When to Use: When quality matters more than maximum cost savings

Premium Models (Use Sparingly!)

GLM-5 — Reasoning Specialist

Model ID: glm-5
Cost: 1,150 requests per $12 (9x more expensive than Qwen3.5 Plus!)
Context: ~200K tokens
Quality: ★★★★☆ (excellent)
Best For:
- Multi-step reasoning
- Complex planning
- Algorithm design
- Difficult debugging
When to Use: When reasoning/planning is required and budget models fail

GLM-5.1 — Maximum Quality

Model ID: glm-5.1
Cost: 880 requests per $12 (11.6x more expensive than Qwen3.5 Plus!)
Context: ~200K tokens
Quality: ★★★★★ (best available)
Speed: Moderate
Best For:
- Critical architectural decisions
- Complex multi-file refactoring
- Production code review
- When you need the absolute best quality
When to Use: Only when cheaper models can't handle the task

GLM-5.2 — Latest Premium Model

Model ID: glm-5.2
Cost: 880 requests per $12 (same as GLM-5.1)
Context: ~200K tokens
Quality: ★★★★★ (best available)
Speed: Moderate
Best For:
- Latest GLM model with improvements over 5.1
- Critical architectural decisions
- Complex multi-file refactoring
- Production code review
When to Use: Use instead of GLM-5.1 for the latest improvements

Kimi K2.7 Code — Code Specialist

Model ID: kimi-k2.7-code
Cost: 1,350 requests per $12
Context: ~256K tokens
Quality: ★★★★★ (excellent for code tasks)
Max Output: 32K tokens (highest available!)
Speed: Fast
Best For:
- Large code generation tasks
- Complex refactoring requiring long outputs
- Code review with detailed feedback
- When you need the highest output token limit
When to Use: When you need both high quality AND very long outputs (up to 32K)

Qwen3.7 Plus — Upgraded General Coding

Model ID: qwen3.7-plus
Endpoint: Anthropic-compatible (/v1/messages)
Cost: 4,300 requests per $12 (better value than Qwen3.6!)
Context: ~128K tokens
Quality: ★★★★☆
Speed: Fast
Best For:
- General coding with better quality than Qwen3.6
- Feature implementation
- Bug fixes
When to Use: When you want better quality than Qwen3.6 at similar speed

Qwen3.7 Max — Maximum Quality Qwen

Model ID: qwen3.7-max
Endpoint: Anthropic-compatible (/v1/messages)
Cost: 950 requests per $12
Context: ~128K tokens
Quality: ★★★★☆
Best For:
- Complex coding tasks
- When Qwen3.7 Plus isn't enough
When to Use: When you need Qwen's best quality

Usage Limits

OpenCode Go limits:

5-hour limit: $12 of usage
Weekly limit: $30 of usage
Monthly limit: $60 of usage

Cost Comparison Example

Scenario: You want to make 5,000 requests this month.

Model	Cost	Can you do it?
Qwen3.5 Plus	~$6	✅ Yes, easily
MiniMax M2.5	~$10	✅ Yes
Qwen3.6 Plus	~$18	✅ Yes
Kimi K2.5	~$32	❌ Exceeds $30 weekly
GLM-5	~$52	❌ Exceeds limits
GLM-5.1	~$68	❌ Exceeds limits

Optimizing Your Usage

Strategy 1: Tiered Approach

1. Start with Qwen3.6 Plus (cheap, good quality)
2. If it fails, try Kimi K2.5 (better quality)
3. If still failing, use GLM-5 (reasoning)
4. Only for critical tasks: GLM-5.1 (premium)

Strategy 2: Task-Based Selection

Background ops (grep, ls, cat) → Qwen3.5 Plus
General coding → Qwen3.6 Plus or Kimi K2.5
Complex features → Kimi K2.5
Architecture/Planning → GLM-5
Critical review → GLM-5.1 (rarely)

Fallback Chains for Cost Efficiency

{
  "fallbacks": {
    "background": [
      { "model_id": "qwen3.6-plus" },
      { "model_id": "minimax-m2.5" }
    ],
    "long_context": [{ "model_id": "minimax-m2.7" }],
    "default": [{ "model_id": "mimo-v2.5-pro" }, { "model_id": "qwen3.6-plus" }],
    "think": [{ "model_id": "kimi-k2.6" }],
    "complex": [{ "model_id": "glm-5" }],
    "fast": [{ "model_id": "qwen3.5-plus" }, { "model_id": "minimax-m2.5" }]
  }
}

Rule of thumb: If a task succeeds with a cheap model, it doesn't need an expensive one. Only fall back to expensive models when necessary.

Quick Reference

Task Type	Recommended	Cost (req/$12)	Fallback
Read file, ls, grep	Qwen3.5 Plus	10,200	Qwen3.6 Plus
General coding	Qwen3.7 Plus	4,300	Qwen3.6 Plus
Complex features	Kimi K2.6	1,850	MiMo-V2.5-Pro
Long context (>80K)	MiniMax M2.5	6,300	MiniMax M2.7
Reasoning/planning	GLM-5	1,150	Kimi K2.6
Critical architecture	GLM-5.2	880	GLM-5.1
Code specialist	Kimi K2.7 Code	1,350	Kimi K2.6
Bulk operations	Qwen3.5 Plus	10,200	MiniMax M2.5

Cost-Saving Tips

Use Qwen3.6 Plus as default — 3,300 req/$12 is plenty for most tasks
Reserve GLM-5.1 for critical tasks only — 880 req/$12 drains budget fast
Use Qwen3.5 Plus for simple operations — 10,200 req/$12 is unbeatable
MiniMax M2.5 for long context — 6,300 req/$12 with 1M context is amazing value
Use Zen free-tier models for non-critical tasks — Nemotron 3 Ultra Free, MiMo V2.5 Free, DeepSeek V4 Flash Free, Big Pickle, and others cost $0 while their promotions remain active
Monitor your usage in the OpenCode console

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MODELS.md

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OpenCode Models Guide

Quick Cost Comparison

Providers

OpenCode Go (opencode-go)

OpenCode Zen (opencode-zen)

AWS Bedrock (aws-bedrock)

OpenRouter Models

Key Benefits

Model Naming Convention

Popular OpenRouter Models

Discovering Available Models

Configuring OpenRouter Models

Cost-Based Routing Integration

Model Categories Available

Example Configurations

Budget-Conscious Setup

Quality-First Setup

Multi-Provider Fallback

OpenRouter-Specific Features

Provider Routing Preferences

How OpenRouter Model Catalog Works

Model Resolution from Catalog

Benefits Summary

Important: API Endpoints

OpenCode Go Endpoints

OpenCode Zen Endpoints

Using OpenCode Zen

Zen-Specific Models (50+ total)

Deprecated Zen Models

Cost-Conscious Routing Strategy

Default to Cheap, Upgrade When Necessary

Recommended Routing

Decision Tree

Detailed Model Profiles

Budget Champions 💰

Qwen3.5 Plus — The Workhorse

MiniMax M2.5 — Long Context on a Budget

MiniMax M3 — Latest MiniMax, 1M Context

Balanced Models (Quality + Cost)

DeepSeek V4 Pro — Agentic Coding + Max Thinking

DeepSeek V4 Flash — Fast Agent Workloads

Qwen3.6 Plus — Cost-Effective General Coding ⭐ RECOMMENDED DEFAULT

Qwen3.7 Plus — Upgraded General Coding

Qwen3.7 Max — Maximum Quality Qwen

Kimi K2.6 — Best Quality at Balanced Cost

Kimi K2.5 — Quality + Reasonable Cost (Predecessor)

Premium Models (Use Sparingly!)

GLM-5 — Reasoning Specialist

GLM-5.1 — Maximum Quality

GLM-5.2 — Latest Premium Model

Kimi K2.7 Code — Code Specialist

Qwen3.7 Plus — Upgraded General Coding

Qwen3.7 Max — Maximum Quality Qwen

Usage Limits

Cost Comparison Example

Optimizing Your Usage

Fallback Chains for Cost Efficiency

Quick Reference

Cost-Saving Tips

See Also

OpenCode Go (`opencode-go`)

OpenCode Zen (`opencode-zen`)

AWS Bedrock (`aws-bedrock`)