Best AI Coding Models 2026: Comparing Free Options via OpenCode CLI

In this post, we’ll dive deep into how these models perform—with special attention to frontend development—and when it actually makes sense to use the free models available through the OpenCode CLI instead of paying for a premium API directly. If you’re looking to optimize your workflow, check out our guide on leveraging agent skills in web development.

Table of Contents

• TL;DR for Frontend Developers
• Quick Start: OpenCode Commands
• The Contenders At A Glance
• Pricing Comparison
• Coding Performance Comparison
• Frontend Development Deep Dive
• When NOT to Use Each Model
• FAQ

TL;DR for Frontend Developers

If you only read one thing: For frontend work, Kimi K2.5 is the standout free choice. It matches or beats paid models on UI generation, handles React/Vue components beautifully, and can convert Figma screenshots into working code. For complex design systems or CSS architecture, Claude Opus 4.5 remains the premium option, but Kimi gets you 90% of the way there for $0.

Benchmark data current as of February 2026. These models improve rapidly—check the SWE-bench leaderboard for latest scores.

Quick Start: OpenCode Commands

Here’s how to actually use these models in OpenCode:

# Start OpenCode with a specific model
opencode --model kimi-k2.5

# Or switch models within a session
/model kimi-k2.5

# Start multiple parallel sessions (each in separate terminals)
opencode --model minimax-m2.1 --session "css-fixes"
opencode --model trinity-large-preview --session "repo-audit"
opencode --model gemini-3-pro --session "architecture"

# Check your token usage across all sessions
/usage

# Export a session to share with teammates
/share

Pro Tip: Model Chaining

Use one model for planning, another for execution:

# Terminal 1: Plan with Gemini
opencode --model gemini-3-pro
> Design a state management strategy for this React app

# Terminal 2: Implement with Kimi (in parallel)
opencode --model kimi-k2.5
> Implement the Redux store based on this architecture: [paste plan]

To get the most out of these models, mastering prompt engineering is essential. Small changes in how you frame your requests can significantly impact the quality of the generated code.

The Contenders At A Glance

Free models available via OpenCode

• MiniMax M2.1 (via OpenCode)

  • Strong general intelligence for an open model, competitive with mid-tier closed models on coding and reasoning benchmarks
  • Known for solid reasoning and good price-performance, often close to or matching older Gemini/Claude tiers in tests

• Big Pickle Kimi K2.5

  • Open-weight model built on Moonshot’s Kimi K2 line, tuned heavily for code and tool use
  • Frequently showcased beating Gemini 3 Pro on complex coding tasks like building dashboards, multi-file apps, and browser-based tools in independent tests

• Trinity-Large-Preview (Arcee)

  • Sparse Mixture-of-Experts architecture with 400B parameters and about 13B active per token, optimized for long context and efficiency
  • Preview API supports up to 128k tokens, which is ideal for large codebases, refactors, and long debugging sessions

Paid “frontier” models

• Gemini 3 Pro

  • Very strong at structured coding workflows and logic; in one benchmark it was rated “best for speed & logic” when building full apps. It’s a key part of the Google AI Tools for Developers ecosystem.
  • Good at iterating quickly and keeping output clean and minimal, which matters a lot in production code.

• Claude Opus 4.5

  • Top-tier language and UI generation, great for front-end, documentation, and complex interface work
  • In the same benchmark, it was called “best for UI/design” but sometimes more verbose and slower than Gemini 3

Pricing Comparison: What Each Model Actually Costs

When evaluating “free” vs “paid,” here’s the real cost breakdown (per million tokens):

* Free through OpenCode’s free tier or OpenRouter. Paid tiers available for higher rate limits.

Real-World Monthly Costs

Free Tier User (OpenCode + free models):

• 100k input tokens/day × 30 days = 3M tokens
• Cost: $0
• Suitable for: Side projects, learning, prototypes

Paid Tier User (mixed usage):

• 80% free models (2.4M tokens): $0
• 20% Gemini 3 Pro (600k input + 200k output): ~$5
• Monthly total: ~$5-10

Heavy Paid User:

• 5M tokens on Opus 4.5: ~$150
• Better suited for: Enterprise teams, mission-critical code

The takeaway: You can realistically ship production code for under $10/month by strategically using free models for 80% of tasks.

Coding Performance: Where Free Models Shine (And Don’t)

Everyday coding and small projects

For normal day-to-day dev work—writing functions, small components, quick bug fixes—all of these models are usable, but the experience differs:

Kimi K2.5

• Very strong on multi-step coding tasks and can produce full-stack scaffolds, dashboards, and landing pages in one go
• Often comparable to Gemini 3 Pro in independent coding demos, sometimes beating it on responsiveness and structure for app-level tasks

MiniMax M2.1

• Solid “generalist coder,” especially attractive when you want good reasoning plus low cost
• Competitive with several commercial models on intelligence indices and speed, though still a slight tier below the very best closed models in quality

Trinity-Large-Preview

• Less hyped for pure coding speed, but very useful when your main pain point is context length: big monorepos, large config trees, or long logs
• The 128k context preview makes it ideal for large-scale refactors, cross-file analysis, and “tell me what’s going on in this entire service” prompts

Gemini 3 Pro

• Excels at producing working MVPs quickly: clean logic, decent architecture, and fewer hallucinated dependencies
• In benchmarks building real apps, it repeatedly started quickly and iterated to something workable faster than peers

Opus 4.5

• Great when you need beautifully structured output: complex UIs, rich error messages, documentation, and product copy around your code
• More likely to over-design or be verbose, which is nice for ideation but can add refactoring overhead for production work

Long Context, Repos, And “Big Picture” Tasks

If you’re using OpenCode inside a repo-aware environment, context window and reasoning about large codebases becomes crucial.

Best for massive context (free tier):

• Trinity-Large-Preview stands out with its Mixture-of-Experts design and 128k token context window in the preview API
• This makes it particularly strong for:
  • Auditing entire services or microservice boundaries in one prompt
  • Large-scale refactors where you paste or reference numerous files
  • Multi-file debugging with long logs and traces

Best for structured multi-step coding (free tier):

• Kimi K2.5 is often used to orchestrate multiple tools and even agent swarms, with demos showing up to 100 sub-agents working in parallel
• This translates well into OpenCode workflows where the model coordinates tests, file edits, and code generation across a project

Paid models for large context:

• Gemini 3 Pro and Opus 4.5 also support large contexts and perform very well on SWE-bench-style tasks, hitting strong scores but still not always producing a complete, fully correct MVP without iteration
• For context-heavy debugging, they’re extremely capable, but you’re paying for that reliability and polish

What OpenCode Actually Changes

Most IDE-integrated AI tools still push you into “one task, one terminal, one conversation” workflows. OpenCode flips this by acting as a task orchestration layer for coding: you spin up multiple sessions, each using a different model, all running in parallel on the same project or across projects.

A pragmatic model strategy within OpenCode:

• Planning: Gemini 3 Pro Preview with high “thinking effort” for hard architectural decisions
• Execution: GPT-5.2 / GPT-5.2 Codex for implementation and complex features
• Bug fixes & small changes: Lower-tier or free models to save tokens
• Token awareness: OpenCode surfaces per-provider usage so you can stay within 20–100 USD monthly budgets instead of blindly burning credits

The same interface also exposes a large catalog of free models (including GLM 4.7 and MiniMax) that can beat mid-tier closed models like GPT-5.2 and Gemini 3 Flash in many benchmarks while staying much cheaper to run.

Frontend Development: Where These Models Truly Shine

Frontend work is where the gap between free and paid models narrows fastest. Modern UI development involves repetitive patterns, visual reasoning, and rapid iteration—all things these AI models excel at.

What Makes a Great Frontend AI Model?

Vision-to-Code capabilities: The ability to look at a screenshot or mockup and generate matching HTML/CSS/React code. This is where Kimi K2.5 particularly excels with its 92.3 OCR Bench score.

Component architecture: Understanding how to break down a page into reusable components, props, and state management.

Responsive design intuition: Knowing when to use flexbox vs grid, media queries, and modern CSS features.

Animation and interaction: Generating smooth transitions, hover states, and scroll-triggered effects—not just static layouts.

Real Frontend Tasks These Models Handle

1. Landing Page Generation Given a simple prompt like “Create a SaaS landing page with hero section, feature grid, pricing cards, and CTA”, Kimi K2.5 produces production-ready React code with Tailwind CSS in one shot. The component structure, responsive breakpoints, and styling are coherent and follow modern conventions.

2. Design-to-Code Workflow Feed a Figma export or screenshot into a vision-capable model like Kimi K2.5, and it generates matching code. This works surprisingly well for:

• Marketing sites
• Dashboard layouts
• E-commerce product pages
• Portfolio sites

3. Component Library Creation Need a consistent set of form inputs, buttons, or cards? These models can generate an entire design system with:

• TypeScript interfaces
• Storybook stories
• CSS variables for theming
• Accessibility attributes

4. CSS Debugging and Refinement Stuck on a layout issue? Describe the problem (or share a screenshot), and models like MiniMax M2.1 excel at suggesting CSS fixes, often identifying issues with z-index, overflow, or flexbox alignment that you’d spend 20 minutes debugging manually.

5. Animation and Micro-interactions Kimi K2.5’s demos show it creating scroll-triggered animations, hover effects, and page transitions using Framer Motion or pure CSS. This is typically tedious hand-written work that AI accelerates dramatically.

Kimi K2.5: The Open Source “Front-End Monster”

A dedicated benchmarking video shows Kimi K2.5 building a point-of-sale dashboard and a SaaS landing page, directly against Gemini 3 Pro. In that test:

• Kimi K2.5 produces fully working UI layouts with feature sections, pricing tables, testimonials, and interactive behaviors like scroll-triggered animation in about 40 seconds of “thinking mode”
• It’s particularly strong at combining vision and code: given UI screenshots, it understands not just layout but dynamic behavior and can wire up animations and interactions correctly

On coding benchmarks such as SWE-bench Verified, Kimi K2.5 scores 76.8%, which is close to Claude Opus 4.5 at 80.9% and GPT-5.2 at 80%. For an open-weight model that you can run or access cheaply via OpenCode, that’s exceptionally strong performance.

Kimi K2.5 also posts impressive numbers for perception and document tasks (e.g., 92.3 on OCR Bench and 88.8 on OmniDoc), which translates into powerful screenshot-based debugging and “read this API doc + codebase and fix things for me” workflows in OpenCode.

Frontend-Specific Strengths

React/Vue/Angular fluency: Kimi generates idiomatic component code with proper hooks, props typing, and state management patterns.

Modern CSS: Excellent with Tailwind, CSS-in-JS (styled-components, emotion), and modern features like container queries and CSS grid.

Accessibility awareness: Automatically includes ARIA labels, semantic HTML, and keyboard navigation considerations.

Design system integration: Understands Material UI, Ant Design, Chakra UI, and can work with your existing component libraries.

MiniMax M2.1: Reasonable, cheap, and underestimated

MiniMax M2.1 is often described as a conversational and reasoning-oriented model that holds up surprisingly well on moderately challenging coding tasks and function calling. Community tests highlight:

• Very good performance on “medium-hard” problems, with struggles mainly on long-horizon planning and extremely complex pipelines
• Excellent price-performance when wired into a tool-calling or agentic environment

In the open-weights ecosystem, MiniMax frequently lands just behind Kimi and top Qwen models on raw coding benchmarks, but it shines when you care about interaction quality and user-facing experiences as much as code quality.

Trinity-Large-Preview: MoE long-context specialist

Trinity-Large-Preview uses a Mixture-of-Experts design (400B total, roughly 13B active per token) and exposes a 128k-token context window via API. That makes it ideal when your main bottleneck is context size, not raw reasoning:

• Reviewing large monorepos, config forests, or deeply nested folders in a single session
• Long-log debugging for production systems where you want to paste in a giant trace and have it reason across it

In combination with OpenCode’s agents.md initialization and MCP integrations like Context7, you can drastically reduce token waste by pre-indexing a project and delegating long documentation reads to cheaper retrieval layers. Trinity then gets a cleaner, compressed view of the repo.

When NOT to Use Each Model (Limitations)

Every model has weaknesses. Here’s when to avoid each one:

MiniMax M2.1: Don’t use when…

• You need complex multi-step planning: MiniMax excels at single tasks but struggles with long-horizon planning across 10+ steps
• Working on large-scale architecture: It can handle components but not system design
• You need the absolute fastest response: While fast, it’s not the speed champion

Red flag: If your prompt starts with “Design a microservices architecture…” reach for Gemini 3 Pro instead.

Kimi K2.5: Don’t use when…

• You need perfect consistency: Kimi is creative and fast, but occasionally changes its approach mid-stream
• Working on safety-critical code: Great for UI, not for medical devices or financial transaction logic
• You need extensive documentation: It generates working code but lighter on inline docs

Red flag: If your code requires extensive comments for compliance, Opus 4.5’s verbosity is actually a feature.

Trinity-Large-Preview: Don’t use when…

• You need quick answers: The MoE architecture has overhead; it’s slower than smaller models
• Working on simple tasks: Overkill for a single CSS fix or small function
• You have tight latency requirements: Not suitable for real-time chat or interactive apps

Red flag: Don’t use Trinity for tasks under 1k tokens—you’re paying the overhead without benefiting from the large context.

Gemini 3 Pro: Don’t use when…

• You’re on a tight budget: At $15/M output tokens, costs add up fast
• You need niche framework knowledge: Sometimes lacks depth in less common libraries
• You want creative exploration: Tends to converge on “safe” solutions quickly

Red flag: If you’re prototyping and want wild ideas, Kimi’s creativity beats Gemini’s pragmatism.

Opus 4.5: Don’t use when…

• Speed matters: It’s the slowest of the bunch
• You’re iterating rapidly: The verbosity slows down rapid prototyping
• Cost is a concern: At $25/M output tokens, it’s premium pricing
• You need to use it in OpenCode directly: Anthropic restricts third-party usage

Red flag: For quick experiments, Opus is overkill. Use it for final polish, not initial exploration.

Real-World Usage Tips: Maximizing OpenCode

Running Parallel Sessions

The killer feature of OpenCode is running multiple models simultaneously. Here’s a practical setup:

# Terminal 1: Kimi for active development
opencode --model kimi-k2.5 --session "feature-dev"

# Terminal 2: Trinity for codebase analysis (running in parallel)
opencode --model trinity-large-preview --session "codebase-audit"

# Terminal 3: MiniMax for quick fixes
opencode --model minimax-m2.1 --session "hotfixes"

Pro workflow: Start all three at the beginning of your day. Switch between them based on task type without losing context.

Token Budgeting Strategies

The 80/20 Rule: Use free models for 80% of tasks, paid for 20%:

• Free tier: Prototyping, UI components, CSS fixes, documentation drafts
• Paid tier: Architecture decisions, security-critical code, final code review

Daily Budget Management:

Day goal: Stay under $2
- First 6 hours: Free models only (Kimi, MiniMax)
- Complex task encountered: Switch to Gemini ($0.50)
- End of day polish: Opus 4.5 if needed ($1.50)

Monitor Usage:

# In OpenCode
/usage

# Shows per-model breakdown
Kimi K2.5:     45k tokens (free)
MiniMax M2.1:  12k tokens (free)
Gemini 3 Pro:  8k tokens ($0.12)

Model Chaining Workflows

The “Plan → Execute → Review” Chain:

1. Planning Phase (Gemini 3 Pro)

   "Design the data flow for a real-time collaborative editor"

   → Get architecture diagram and approach

2. Execution Phase (Kimi K2.5, in parallel)

   "Implement the WebSocket manager based on this design: [paste]"

   → Get working code

3. Review Phase (Opus 4.5, optional)

   "Review this code for edge cases and add error handling"

   → Get production-ready version

The “Screenshot → Code → Refine” Frontend Chain:

1. Screenshot → Code (Kimi K2.5 with vision)

   • Upload Figma export
   • Get initial React implementation

2. Refine & Optimize (MiniMax M2.1)

   • “Optimize the CSS and reduce bundle size”
   • Quick, cheap iteration

3. Accessibility Audit (Trinity-Large-Preview)

   • “Check for WCAG compliance across all components”
   • Large context handles entire component library

Token-Saving Tricks

1. Use Context7 MCP: Pre-index your codebase so models don’t waste tokens reading the same files
2. Start with smaller context: Begin prompts focused, expand only when needed
3. Chain short prompts: Three 500-token prompts often beat one 1500-token prompt
4. Cache common patterns: Save successful prompts in your agents.md file

When to Stop and Switch Models

Switch signs:

• Model gives contradictory answers → Switch to more capable model
• Stuck on same bug for 3+ prompts → Fresh perspective with different model
• Response quality degrading → Context window filling up, start new session
• “I apologize, but I cannot…” → Try different model with different framing

Cost, Reliability, And When Paid Models Still Win

When you compare free OpenCode models to paid APIs, you’re trading a bit of raw reliability and polish for cost savings and openness.

Where free models are “good enough”:

• Solo devs, indie hackers, and hobby projects (This is me! It let’s me use this stuff for my side projects)
• Internal tools, prototypes, and experiments where occasional missteps are acceptable
• Learning and teaching environments where open weights and transparent behavior matter more than shaving off the last 10% of error rate

Where paid models still justify their cost:

• Enterprise workflows where every regression has a cost and you want the highest success rate per prompt
• Complex, multi-service production systems where hallucinated APIs or slightly wrong refactors can be expensive
• Teams that care deeply about latency, uptime SLAs, and stable behavior across months

In benchmarks that look at full-app delivery, Gemini 3 Pro and Opus 4.5 still tend to deliver more consistent, production-ready code than any individual open model, though the gap is narrowing quickly as MiniMax and Kimi continue to improve.

Which Model Should You Use For Coding?

If your main question is “what’s the best free model to use via the OpenCode CLI compared to Gemini 3 and Opus 4.5?”, the practical answer looks like this:

Frontend Development Comparison

If you’re already inside OpenCode, a pragmatic setup is:

• Use Kimi K2.5 as your default free coding workhorse for anything beyond trivial snippets
• Swap to Trinity-Large-Preview when the main challenge is “too much context” rather than raw logic
• Keep MiniMax M2.1 in your rotation for general reasoning and cheaper runs when you don’t need top-end coding quality
• Reach for Gemini 3 Pro or Opus 4.5 only when you’re building something that absolutely needs maximum reliability or polish, and you’re willing to pay for it

Example: Frontend Project Workflow

Here’s how you might use multiple models in parallel on a real frontend project:

Session 1 (Kimi K2.5): “Create a responsive pricing page with three tiers, toggle for monthly/yearly, and FAQ section below” → Generates complete React + Tailwind component

Session 2 (Trinity-Large-Preview): “Review our entire component library for accessibility issues and suggest fixes” → Audits 50+ components, identifies missing ARIA labels, color contrast issues

Session 3 (MiniMax M2.1): “Fix the CSS bug where the mobile menu doesn’t close when clicking outside” → Quick, focused fix with minimal token usage

Session 4 (Gemini 3 Pro - optional): “Design the state management architecture for the checkout flow” → High-level plan handed off to Session 1 for implementation

This workflow leverages each model’s strengths while controlling costs.

Conclusion

Inside OpenCode, free models like MiniMax M2.1, Kimi K2.5, and Trinity-Large-Preview are now strong enough that you can realistically ship real products without paying for Gemini 3 Pro or Claude Opus 4.5 on every task. The trick is combining the right model with OpenCode’s parallel workflow so your bottleneck is no longer GPU time, but your own attention.

The gap between free open-weight models and paid frontier models is narrowing rapidly. Kimi K2.5’s 76.8% on SWE-bench Verified is approaching Claude Opus 4.5’s 80.9%. For many developers—especially indie hackers, solo devs, and those building prototypes—using these models via the OpenCode CLI is now more than capable enough.

The real advantage of OpenCode isn’t just the models it supports; it’s the workflow. If you haven’t tried OpenCode yet, it’s worth exploring. Start with Kimi K2.5 for your day-to-day coding, bring in Trinity-Large-Preview when you need to understand a large codebase, and keep MiniMax M2.1 handy for quick tasks. You might find that the free tier takes you further than you expected.

FAQ: Frequently Asked Questions

Is OpenCode really free for coding?

Yes, OpenCode provides a free tier that grants access to powerful models like Kimi K2.5 and MiniMax M2.1. While there are rate limits, they are generous enough for most individual developers and side projects.

Which model is best for React and Tailwind CSS?

Kimi K2.5 is currently the top contender for frontend work. It has excellent vision-to-code capabilities and generates idiomatic React code with Tailwind utilities.

How does Gemini 3 Pro compare to free models?

Gemini 3 Pro generally offers more consistent logic and better multi-step planning. While models like Kimi K2.5 are close in raw coding benchmarks, Gemini remains more reliable for complex architectural decisions.

Can I use multiple models at once in OpenCode?

Yes, one of OpenCode’s most powerful features is the ability to run parallel sessions with different models, allowing you to use a “frontier” model for planning and a “cheaper” model for execution.

What’s your favorite AI model for coding right now? Let me know in the comments or reach out on Twitter/X. If you enjoyed this post, don’t forget to share it with your fellow developers!

Learn More

OpenCode & Tools:

• OpenCode - The open source AI coding agent
• OpenCode on GitHub - Source code and documentation

Free Models:

• MiniMax M2.1 - Official website and API docs
• MiniMax on Hugging Face - Model weights and technical details
• Kimi K2.5 - Moonshot AI documentation
• Trinity-Large-Preview - Arcee AI blog post
• Trinity on OpenRouter - Free API access

Paid Models:

• Gemini 3 Pro - Google DeepMind model card
• Gemini 3 Pro Documentation - Google Cloud docs
• Claude Opus 4.5 - Anthropic announcement
• Claude Opus 4.5 System Card - Technical details and safety evaluations

Benchmarks:

• SWE-bench - Official website and leaderboard
• SWE-bench on GitHub - Benchmark framework and datasets
• SWE-bench Results Viewer - Interactive model comparison tool