A new option has landed for developers who want agentic coding without a monthly bill. OpenCode, a free open source AI coding agent, launched in 2026 with an MIT license. The release targets teams that have watched closed tools like GitHub Copilot shift toward usage-based billing. OpenCode does not hide its model behind a paywall. The model is small enough to run on a single workstation, yet the agent layer includes file edits, terminal commands, and browser search. This is the first release from the maintainers to combine a compact coding model with a full agent loop under a permissive license.
The project lives on GitHub and Hugging Face. The maintainers published the agent runtime and model weights together. That means you can inspect the tool calling logic, modify the system prompt, or fork the entire stack. OpenCode does not require a cloud account for local use. A self-hosted server option is available for teams. The 7B parameter base model has a 128,000 token context window. It was fine-tuned from an open foundation model, although the team has not disclosed which one. The important part is that no token meter runs while you use it offline. That is a meaningful difference from hosted assistants that charge by request.
Why this matters now is simple. Free tiers have been shrinking across major AI providers in June 2026. Cursor, Windsurf, and Zed have all adjusted free access. GitHub Copilot introduced usage-based billing. OpenCode arrives as an alternative that does not extract usage fees. It is not the most powerful model on paper, but it is yours. The MIT license allows commercial use, modification, and redistribution. That is a direct contrast with hosted coding assistants that can change pricing at any time. This release matters because it gives developers a fixed cost option, which is zero.
OpenCode fits a broader movement toward self-hosted and local AI for developers. It joins recent open coding releases from Qwen, Nous Research, and JetBrains. Each of these takes a different approach to agentic coding. Some are model-first, some are agent-first. OpenCode is agent-first with a small model. The combination targets developers who care about cost, data control, and auditability more than raw benchmark leaderboard placement. In this comparison, we break down where OpenCode fits and where it falls short.
How Do the Top Options Compare?
| Tool | Best For | License | Model Size | Context Window | SWE-bench Lite |
|---|---|---|---|---|---|
| OpenCode | Local agentic coding without API fees | MIT | 7B | 128K | 38.2% |
| Qwen 3.6 Coder | High-performance open coding model | Apache 2.0 | 30B A3B | 128K | 44.1% |
| Hermes Agent | Long-horizon autonomous agent tasks | Apache 2.0 | 8B | 64K | 31.5% |
| Mellum 2 | IDE-native code completion in JetBrains | Apache 2.0 | 3B | 32K | 29.8% |
| GitHub Copilot Free | Cloud hosted coding with free monthly quota | Proprietary | Undisclosed | 64K | 46.3% |
Benchmark scores are from early vendor announcements and may shift after independent evaluation. Model sizes and context windows are as disclosed in June 2026 release notes.
1. OpenCode , Best for local coding agents with no token meter
OpenCode is the subject of this release. It is a free open source AI coding agent built for local and self-hosted use. The maintainers released it under the MIT license in 2026. The agent runs a 7B parameter model with a 128K token context window. That context holds roughly 80,000 words or several hundred files of code, which is enough for medium sized refactors. The model is not the largest open coding release this year. Qwen 3.6 Coder ships a 30B A3B mixture of experts model. But OpenCode wins on packaging, not raw size.
The agent layer handles file edits, terminal commands, and browser search. You can run it from the command line or connect it to VS Code through an extension. The maintainers published the full runtime on GitHub. Weights are available on Hugging Face. The MIT license means you can use OpenCode inside a commercial product without paying royalties. That is a different relationship from hosted tools like GitHub Copilot, which now charges based on usage.
Early benchmark results are honest. OpenCode scores around 38 percent on SWE-bench Lite, a standard test for real-world GitHub issues. That places it below the leading closed models, which often pass 45 percent. But it is close enough for many day to day tasks. The agentic loop works best for small bug fixes, test generation, and simple refactors. It struggles with large, multi-step changes that require deep architectural reasoning.
The main advantage is privacy. OpenCode does not send your code to a remote API when you run it locally. The entire stack is inspectable. You can change the system prompt, swap the model, or extend the tool set. That freedom is difficult for closed coding assistants to match.
Key strengths:
- ✅ No per-token or per-seat pricing, ever
- ✅ Runs locally or self-hosted, keeping code private
- ✅ MIT license allows commercial use and modification
- ✅ Full agent loop with file edits and terminal commands
- ✅ Small enough to run on a single GPU or Apple Silicon Mac
- ❌ 7B model trails larger open and closed models on hard benchmarks
- ❌ Requires technical setup if you want the self-hosted server
- ❌ Does not yet support all IDE features found in Cursor or Copilot
Who it’s for: Developers who want a private, free coding agent and are willing to trade some raw model power for control.
2. Qwen 3.6 Coder , Best for raw open coding benchmark performance
Qwen 3.6 Coder is an Apache 2.0 licensed open coding model from Alibaba’s Qwen team. It was released in 2026 with a 30B parameter mixture of experts architecture, activating 3B parameters per token. This sparse design keeps inference fast while retaining more total capacity than a dense 7B model. The context window is 128K tokens, matching OpenCode. It is a model release, not a full coding agent. You need to bring your own agent loop or pair it with a framework. See the full release details here.
The model scores around 44 percent on SWE-bench Lite, a significant jump over OpenCode. That score puts it closer to paid cloud models. It runs with Ollama, vLLM, and llama.cpp. The Apache 2.0 license permits commercial use. The tradeoff is hardware. The MoE model needs more memory than a dense 7B, although activation is only 3B.
Qwen 3.6 Coder is a better choice if you already have an agent framework and want the strongest open weights. But you will spend more time wiring up tool calling, file edits, and terminal integration. OpenCode bundles those pieces for you. The Qwen route is more flexible but less turnkey.
For teams that need a local coding model but do not mind building the agent layer, Qwen 3.6 Coder is likely the top open option in 2026. It also benefits from a large community that publishes quantization and fine-tunes.
Key strengths:
- ✅ 30B A3B MoE gives strong coding performance at low active parameter cost
- ✅ Apache 2.0 license is permissive and production friendly
- ✅ Scores around 44 percent on SWE-bench Lite, close to paid models
- ✅ Works with Ollama, vLLM, and llama.cpp for local inference
- ❌ Model-only release, no built-in agent loop
- ❌ Requires more VRAM than a dense 7B model even with MoE
- ❌ You must assemble your own tool calling and file editing stack
Who it’s for: Developers who want the strongest open coding model and already have an agent framework.
3. Hermes Agent , Best for long-horizon autonomous coding tasks
Hermes Agent from Nous Research is an open source agent model tuned for multi-step workflows. It uses an 8B parameter model with a 64K context window. The release focuses on tool use, planning, and reflection. It is not a standalone app like OpenCode, but a model intended to plug into agent frameworks. The Nous Research team built it for long-horizon autonomous coding tasks. It works with frameworks like Microsoft Agent Framework.
On SWE-bench Lite, Hermes Agent scores around 31.5 percent. That is below OpenCode, but its strength is planning consistency across many steps. The model is less likely to lose track of a long task than a generic completion model. The Apache 2.0 license allows commercial integration.
The main limitation is the 64K context window, which is half of OpenCode’s 128K. You also need to assemble the surrounding agent harness yourself. There is no official desktop app or editor extension from Nous Research. Teams that adopt Hermes Agent typically run it behind a custom CLI or a framework like LangGraph.
Choose Hermes Agent if you care about robust multi-step agent behavior more than raw code generation. It pairs well with OpenCode when you want a small local model for routine edits and a planning model for complex workflows.
Key strengths:
- ✅ Strong tool calling and planning for an 8B model
- ✅ Apache 2.0 license with commercial use allowed
- ✅ Tuned for multi-step developer workflows, not just single completions
- ✅ Works well with frameworks like Microsoft Agent Framework
- ❌ 64K context window is half of OpenCode’s 128K
- ❌ Requires an external agent harness to use the full loop
- ❌ Lower SWE-bench Lite score than larger models
Who it’s for: Developers who need a planning-focused open model for long autonomous tasks and already run an agent framework.
4. Mellum 2 , Best for lightweight IDE-native completion
JetBrains released Mellum 2 as an open source 3B parameter model for IDE code completion. It runs inside JetBrains IDEs and the AI Assistant plugin. The Apache 2.0 license makes it embeddable. Its 32K context window is smaller than OpenCode, but it focuses on latency-sensitive suggestions. The model is optimized for low memory and fast token generation on developer laptops.
Mellum 2 is trained on code completion tasks, not full agentic workflows. It can fill methods, suggest refactors, and complete boilerplate. It scores around 29.8 percent on SWE-bench Lite, which is low because the benchmark tests multi-step issue resolution, not inline completion. JetBrains also provides a cloud service for those who want managed inference. The open model is available for local use.
The main drawback is the narrow context and the lack of terminal or file editing tools. Mellum 2 does not try to be an agent. It is a fast completion model that sits inside the editor. For developers who spend most of their time writing code line by line, that is exactly what they need.
Mellum 2 compares poorly with OpenCode on agentic benchmarks, but it wins on latency and integration depth. If your workflow is completion driven and you use JetBrains IDEs, it is a solid open choice. For broader agentic coding, use OpenCode or Qwen 3.6 Coder.
Key strengths:
- ✅ Very low memory footprint and fast token generation
- ✅ Native integration with JetBrains IDEs
- ✅ Apache 2.0 license allows embedding in commercial products
- ✅ Good for inline completion and small refactors
- ❌ 32K context window is much smaller than OpenCode
- ❌ No agent loop, terminal commands, or file editing tools
- ❌ Low SWE-bench Lite score because it is not designed for agentic tasks
Who it’s for: JetBrains IDE users who want a fast, open completion model and do not need a full agent loop.
5. GitHub Copilot Free , Best for developers who want a managed cloud coding assistant
GitHub Copilot Free is not open source, but it is included because many developers compare it with open coding agents. In June 2026, GitHub shifted to usage-based billing for premium Copilot features. The free tier still exists, but it limits requests and uses a lighter model for some completions. Microsoft, the vendor behind GitHub Copilot, has not disclosed the exact model size. Microsoft markets Copilot Free as a cloud coding assistant with strong IDE integration.
On SWE-bench Lite, Copilot Free scores around 46.3 percent, which is higher than OpenCode. The catch is cost and control. Users cannot self-host, cannot inspect the model, and cannot modify the tool calling logic. The free tier works for light use, but heavy users face overage charges.
For developers who want the fewest setup steps and the highest benchmark scores, Copilot Free is still the easiest path. But the June 2026 pricing changes have made many teams nervous. A closed assistant can raise prices or reduce free quotas without warning. OpenCode and other open source alternatives offer an escape hatch.
Copilot Free is best for quick experiments, code reviews, and occasional pair programming. It is not a replacement for a self-hosted agent when you need privacy, auditability, or a fixed zero cost.
Key strengths:
- ✅ No local hardware required
- ✅ Strong benchmark performance and IDE integration
- ✅ Free monthly quota for light use
- ❌ Proprietary code, no self-hosting
- ❌ Usage-based billing can surprise heavy users
- ❌ Free tier limits make it hard to rely on for daily work
Who it’s for: Developers who want high benchmark scores and managed infrastructure and do not mind usage limits or closed code.
Frequently Asked Questions
Is OpenCode actually free?
Yes. OpenCode is released under the MIT license, which means you can use it for personal or commercial projects without paying a license fee. You also avoid per-token API charges when you run it locally or self-host it. The only costs are your own hardware and electricity.
What license does OpenCode use?
OpenCode uses the MIT license. This is a permissive open source license that allows commercial use, modification, redistribution, and sublicensing. It is less restrictive than AGPL or other copyleft licenses. You can embed OpenCode in proprietary products without releasing your own source code.
Can I run OpenCode locally?
Yes. OpenCode is designed to run on a single GPU or a modern Apple Silicon Mac. The 7B parameter model with 128K context fits in about 6 to 8 GB of VRAM with 4-bit quantization. You can also run on CPU, but token generation will be slower.
How does OpenCode compare to GitHub Copilot?
OpenCode is open source, free, and runs locally. GitHub Copilot Free is closed source, cloud hosted, and has usage limits. Copilot Free scores higher on SWE-bench Lite, around 46 percent versus OpenCode’s 38 percent. But Copilot pricing can change, and you cannot inspect or self-host its model.
What are the minimum hardware requirements for OpenCode?
You need about 8 GB of VRAM to run OpenCode with 4-bit quantization and a 128K context window. A 16 GB GPU or Apple Silicon Mac with 16 GB unified memory is more comfortable. CPU-only inference works but is slow for long agent loops.
Does OpenCode support VS Code or only the terminal?
OpenCode includes a command line interface and a VS Code extension. The extension supports inline edits, file diffs, and terminal command execution. You can also use the agent runtime with other editors through its HTTP API.
What Should You Remember?
- OpenCode is a free MIT-licensed coding agent that runs locally and avoids usage-based billing.
- No token meter means your cost stays zero after you set up the hardware.
- 7B parameters and 128K context balance local performance with enough context for medium refactors.
- SWE-bench Lite around 38 percent makes OpenCode useful for small to medium tasks, not all production work.
- MIT license allows commercial use, modification, and redistribution without royalty payments.
- Alternatives like Qwen 3.6 Coder, Hermes Agent, and Mellum 2 cover raw benchmarks, long-horizon planning, and IDE completion.
- Self-hosting keeps code private, gives auditability, and removes provider pricing risk.
Free AI News is an independent editorial publication. Information about AI pricing, free-tier limits, and features changes frequently and may become outdated. Always verify current details through the vendor’s official pages. Affiliate links may earn a commission at no cost to you, and never affect our reporting.
