OpenCode Go Models (2026): Full List, Limits, Pricing & Best Picks
OpenCode Go is a $10/month model subscription for OpenCode. It gives you access to a rotating set of coding-focused models — DeepSeek, Kimi, GLM, Qwen, MiniMax, MiMo, LongCat — plus experimental/premium models such as Omen Alpha and Grok. Instead of a fixed number of requests, you get a spending-style allowance: $12 per 5 hours, $30 per week, and $60 per month, per the official OpenCode Go terms as of this writing. Cheap Flash-class models draw down that allowance far more slowly than premium ones.
The lineup rotates, so treat this post as a practical selection guide rather than a permanent catalog; check the official OpenCode Go page for the models currently included.
What models are available on OpenCode Go?
As of this writing, the OpenCode Go lineup includes: Kimi K3, Grok 4.6, GPT-5.6 Luna, GLM-5.3 and GLM-5.3-Flash, DeepSeek V4 Pro and V4 Flash, Qwen3.8 Max and Qwen3.8 Flash, MiniMax M3, MiMo-V2.5 and MiMo-V2.5 Pro, LongCat-2.0, Hy4 Preview/Hy3, and the experimental Omen Alpha. The exact set changes frequently, so the official page is the source of truth for what is included today.
OpenCode Go Models at a Glance
The table below separates what is known about each model from where it fits in the author’s typical workflows. “Cost tendency” describes how fast a model draws down the shared Go allowance, not the retail API price.
| Model | Go status | Practical role | Cost tendency | When I would choose it |
|---|---|---|---|---|
| DeepSeek V4 Flash | Included | Fast, high-volume routine work and agent tool calls | Relatively inexpensive | Default for mechanical edits, small fixes, and agent loops |
| MiMo-V2.5 | Included | High-volume agent and tool-call work | Relatively inexpensive | Heavy agent runs where the allowance needs to stretch |
| GLM-5.3-Flash | Included | Routine edits and tool calls | Relatively inexpensive | Cheap workhorse inside agent loops |
| LongCat-2.0 | Included | Long-running agent sessions | Relatively inexpensive | Long-horizon autonomous tasks |
| DeepSeek V4 Pro | Included | Reasoning-heavy coding | Varies | Architecture work and subtle debugging |
| GLM-5.3 | Included | Strong reasoning at moderate cost | Varies | Difficult reasoning while staying budget-aware |
| Kimi K3 | Included | Hard reasoning and long coding sessions | Relatively expensive | When a problem needs top-tier reasoning and budget allows |
| Omen Alpha | Experimental | Undisclosed model for real-world testing | Relatively expensive | Only when I explicitly want to test an unknown model |
The rest of the current lineup — Grok 4.6, GPT-5.6 Luna, Qwen3.8 Max/Flash, MiniMax M3, MiMo-V2.5 Pro, Hy4 Preview/Hy3 — is included in Go too, but these models have less impact on most daily workflows, so I don’t give each one a recommendation.
Method note: the practical roles above are starting points based on typical usage patterns, not benchmark rankings or an official OpenCode evaluation. No performance labels in this article are measured results; Omen Alpha is marked Experimental because its provider identity is not disclosed.
OpenCode Go Pricing: $10 per Month
OpenCode Go costs $10/month, per the official Go page as of this writing. That single subscription covers all models in the Go lineup, so you are not paying per model.
What the $10 buys: a shared spending allowance across the included models, plus the ability to switch models freely. For most independent developers, the appeal is a low-barrier way to test several coding models at once.
OpenCode Go Usage Limits: $12 / 5 Hours, $30 / Week, $60 / Month
OpenCode Go does not use a fixed request count. Instead, it uses spending-style limits (official terms as of this writing):
- $12 per rolling 5 hours
- $30 per rolling week
- $60 per rolling month
Meaning: cheap models like the Flash tiers can serve far more requests than premium models inside the same allowance, and heavy premium-model use usually hits the 5-hour cap first.
How OpenCode Go Usage Limits Work
The core idea is a value-based allowance: every request spends a little of your $12/$30/$60 budget, depending on the model’s price tier and the tokens actually used. There is no “500 requests per 5 hours” counter.
Why “requests per 5 hours” is not a reliable comparison unit: one request can be a one-line typo fix or a 150k-token repository analysis that edits eight files. Both count as a single request but consume very different amounts of allowance. A headline number like “30,000 requests per 5 hours” therefore reads like a concrete quota when it is really a projection that depends heavily on context size, output length, reasoning tokens, and tool-loop behavior.
What you can rely on instead is relative burn: Flash-class models (DeepSeek V4 Flash, GLM-5.3-Flash, MiMo-V2.5, LongCat-2.0) draw down the allowance far more slowly than premium models (Kimi K3, Grok 4.6, GPT-5.6 Luna). In practice, cheap models support dramatically more agent steps within the same allowance, but the exact number depends heavily on the workload. The most common “I hit the limit” reports come from heavy premium-model use.
Why Does a $10 OpenCode Go Subscription Offer So Much Usage?
The surprising part of Go is that $10 can cover a lot of agent work. Here is what is observable without relying on OpenCode’s internal economics:
- Different models consume the allowance at very different rates. A premium reasoning model burns far more per request than a Flash-class model.
- Lower-cost models make the same allowance stretch much further. That is what turns $10 into a high-volume option for agent-style usage.
- The shared allowance encourages routing: cheap models for routine steps, expensive models only for tasks that need them.
- Open-weight models (DeepSeek, GLM, Qwen, MiniMax, MiMo) keep per-request costs low across the lineup.
What OpenCode does not disclose: the exact backend, provider pricing, or any subsidy mechanism behind the flat $10. There is no public breakdown showing how the subscription is priced internally, so explanations built on “cheapest provider routing” or “subscription subsidy” are speculation. OpenCode does not publicly disclose enough information to attribute the pricing to a specific backend or subsidy mechanism; the observable behavior above is what subscribers can actually verify.
Which OpenCode Go Model Should You Use?
Don’t use the most expensive model for every step.
There is no single “best” Go model. The useful question is: which model should start on a given task? The framework below combines task complexity, context size, agent loop length, and cost of failure.
| Scenario | Start with | Escalate to | Notes |
|---|---|---|---|
| Repository exploration | DeepSeek V4 Flash or Qwen3.8 Flash | DeepSeek V4 Pro | Cheap models handle most exploration; upgrade if you keep getting stuck |
| grep / search / tool calls | MiMo-V2.5 or GLM-5.3-Flash | — | Never burn premium allowance on mechanical tool calls |
| Boilerplate generation | DeepSeek V4 Flash | Qwen3.8 Max (long templates only) | A premium model adds little here |
| Unit tests | GLM-5.3-Flash or DeepSeek V4 Flash | DeepSeek V4 Pro | Escalate on flaky or ambiguous test logic |
| Small bug fix | DeepSeek V4 Flash or GLM-5.3-Flash | DeepSeek V4 Pro | Escalate after repeated failure or unclear root cause |
| Multi-file refactor | DeepSeek V4 Pro or GLM-5.3 | Kimi K3 (plan + review) | Strong model for planning and review; cheap models for mechanical edits |
| Architecture planning | GLM-5.3 or DeepSeek V4 Pro | Kimi K3 / GPT-5.6 Luna | Premium only for high-stakes design and second opinions |
| Difficult debugging | DeepSeek V4 Pro or GLM-5.3 | Kimi K3 | Escalate when the problem needs subtle reasoning |
| Code review | DeepSeek V4 Pro or GLM-5.3 | Kimi K3 / GPT-5.6 Luna | A strong model for review; premium only for critical passes |
| Long-running autonomous agent loop | LongCat-2.0 or MiMo-V2.5 | DeepSeek V4 Pro at decision points | Keep the loop cheap; use a strong model only for checkpoints |
The general method: start cheap, escalate on uncertainty or failure, and use a stronger model for review. Do not let an expensive model carry mechanical tool calls; the allowance economics punish that immediately.
A Practical OpenCode Go Model Strategy
The biggest OpenCode Go optimization is not finding one “best model” — it is routing different tasks to different models.
Three-tier routing:
- Cheap tier (DeepSeek V4 Flash, GLM-5.3-Flash, MiMo-V2.5, LongCat-2.0) for routine work: grep/search, small edits, unit tests, docs, repetitive refactors, and agent tool calls.
- Strong tier (DeepSeek V4 Pro, GLM-5.3, Qwen3.8 Max) for difficult reasoning: architecture, subtle debugging, multi-file refactors.
- Premium tier (Kimi K3, Grok 4.6, GPT-5.6 Luna) sparingly: mostly for planning, review, and genuinely hard problems.
A working loop: plan on a strong model → implement on a cheap model → review on a strong model. When the cheap model fails or stalls, escalate to the next tier instead of retrying the same model.
How to Use OpenCode Go Models
Setup inside the OpenCode CLI:
/connect # open the connection menu# Choose OpenCode Go, paste the API key/models # list available models and select oneWhen you need a model ID for configuration, Go model IDs use the opencode-go/ prefix — for example opencode-go/deepseek-v4-flash:
opencode-go/deepseek-v4-flashThe Omen Alpha model ID is omen-alpha. Confirm the exact current IDs in the official docs, since the lineup rotates.
What Is Omen Alpha in OpenCode Go?
Omen Alpha is the most discussed model in the current lineup. It is worth knowing about, but it is not the center of the Go value proposition.
What we actually know
- Available to OpenCode Go subscribers.
- Model ID:
omen-alpha. - Exposed through an OpenAI-compatible endpoint.
- No public model card exists, and OpenCode does not disclose the provider or base model.
What we do not know
- Which provider or base model runs behind
omen-alpha. - How it compares to DeepSeek, GLM, or Kimi on real coding workloads — there are no public benchmarks from OpenCode.
- Its retention and training terms. Community reports mention zero-day retention, a no-training policy, and a temporary bonus allowance around $100, but none of this is officially confirmed.
Why developers are testing it
It is the only way to try an undisclosed model in a real agent environment without a separate contract. Go subscribers can run blind coding tests immediately, which makes it easy to judge for yourself whether the results beat the known lineup.
When it is worth trying
- You are already paying for Go and want to test a new model risk-free.
- You are curious how it handles the specific task types in your repo.
- You treat it as an experiment with clear success criteria.
When you should prefer a known model
- Production or client work where provenance matters.
- Workflows that need predictable metadata, retention terms, or documented behavior.
- Any task where a failure is costly — you cannot audit an undisclosed model the way you can a documented one.
Why self-identification and tokenizer clues are weak evidence
Some community threads try to identify Omen Alpha by asking the model “who are you” or by analyzing tokenizer behavior, with guesses pointing at a GLM-family model. This is weak evidence: a model can be prompted to claim almost any identity, and tokenizer patterns overlap heavily between model families. There is no confirmed public evidence that Omen Alpha is any specific unreleased model, so treat identity guesses as unverified community speculation.
Bottom line: Omen Alpha is interesting because it gives Go users access to an undisclosed model for real-world testing, but the lack of provenance and public benchmarks means it should not automatically replace established models for production workflows.
Can I Use OpenCode Go Outside OpenCode?
Yes, in the common case. OpenCode exposes OpenAI-compatible and Anthropic-compatible endpoints for Go, which means you can connect to OpenAI-compatible clients, Anthropic-compatible clients, custom coding agents, and orchestration tools. Compatibility is not guaranteed for every third-party client, so test your specific tool before relying on it.
Data Retention and Privacy
Model-level policies are not uniform inside Go. Some models use zero retention, some may retain data around 30 days, and Contributor variants may have different training/data-sharing terms. Grok and GPT-5.6 Luna policies must be checked individually.
| Model / category | Retention | Training use |
|---|---|---|
| Models with zero-retention policy | Zero, per official metadata | Not used for training |
| Standard models | Possibly around 30 days | Check per model |
| Contributor variants | Differs from standard | May allow data sharing |
| Grok / GPT-5.6 Luna | Check current policy | Check current policy |
Retention and training terms differ per model and change as OpenCode updates model metadata, so check the current metadata before sending proprietary code.
OpenCode Go vs Free Models vs Zen
A common confusion is that OpenCode Go is “the paid version of OpenCode.” It is not. OpenCode itself is free; Go is a model-access subscription layer on top.
| Monthly cost | Model focus | Usage | Best for | |
|---|---|---|---|---|
| Free models | $0 | Basic models | Limited | Trying OpenCode |
| OpenCode Go | $10 | Multi-model lineup | $12/5h, $30/week, $60/month | Daily coding-agent use |
| Zen | Varies | Premium/frontier focus | Premium-tier limits | Users who want one strong premium model |
OpenCode Go vs Direct APIs, OpenRouter, Cursor and Codex
Each option has different trade-offs.
| Option | Main advantages | Main downsides |
|---|---|---|
| OpenCode Go | One flat $10 for many models, simple setup | Rotating lineup, value-based caps |
| Direct APIs | Full control, exact per-token cost | Pay per token, manage multiple keys |
| OpenRouter | One key for many providers | Per-token billing like APIs |
| Cursor | Integrated IDE experience | Different pricing model, lock-in |
| Codex | Strong OpenAI models | Narrower model choice |
What Developers Are Saying About OpenCode Go
Recent community discussion (reported from X/Twitter threads, not attributed to specific users) falls into four trends:
- Price-to-performance is the biggest attraction: $10 is seen as a low-barrier way to test multiple coding models.
- Cheap models change usage behavior: fast/cheap models are cheap enough that developers let agents run large numbers of tool calls and iterations instead of rationing every request.
- Heavy premium-model use can still hit limits quickly: this is one of the most common negative reports.
- Omen Alpha turned Go into a model-testing playground: the community runs blind tests, tokenizer-based guessing, and real coding-task comparisons.
Is OpenCode Go Worth It?
Strong fit:
- Independent developers
- Coding-agent enthusiasts
- Daily OpenCode users
- Users who like switching between DeepSeek/Kimi/GLM/Qwen
- Budget-conscious agent users
- Model experimenters
Less compelling:
- Users making only a few AI calls per month
- Users who need one specific frontier proprietary model
- Users who frequently exceed premium-model allowances
- Enterprises needing strict provider-specific compliance
For developers already using OpenCode and comfortable routing tasks across multiple models, Go is unusually strong value at $10/month.
FAQ
What models are included in OpenCode Go? A rotating lineup, as of this writing: Kimi K3, Grok 4.6, GPT-5.6 Luna, GLM-5.3/Flash, DeepSeek V4 Pro/Flash, Qwen3.8 Max/Flash, MiniMax M3, MiMo-V2.5/Pro, LongCat-2.0, Hy4/Hy3, and Omen Alpha. Check the official page for the current set.
How much does OpenCode Go cost? $10/month.
What is the OpenCode Go usage limit? $12 per 5 hours, $30 per week, $60 per month — a spending allowance, not a fixed request count.
What is the best OpenCode Go model for coding? There is no single best model. For routine agent work, DeepSeek V4 Flash or MiMo-V2.5; for hard reasoning, DeepSeek V4 Pro or GLM-5.3. Route by task rather than picking one model.
Does OpenCode Go include DeepSeek / Kimi / GLM? Yes — DeepSeek V4, Kimi K3, and GLM-5.3 are part of the lineup as of this writing.
What is Omen Alpha?
An experimental premium model with model ID omen-alpha and an OpenAI-compatible endpoint. Its provider identity is not officially disclosed.
Is Omen Alpha GLM-5.4? Unconfirmed. Some community guesses point to a GLM-family model, but there is no public evidence, and self-identification/tokenizer clues are weak evidence.
Can OpenCode Go models be used outside OpenCode? Often yes, through the official OpenAI-compatible and Anthropic-compatible endpoints. Test your specific client first.
Is OpenCode Go unlimited? No. It is a value-based allowance, and premium-model usage can hit limits quickly.
What happens when I hit the limit? Per the current official policy: top up, switch to Free models, or use another provider while the window resets.
Is OpenCode Go better than OpenRouter? It depends on your usage pattern. The flat $10 suits heavy daily agent use; OpenRouter suits pay-as-you-go with a specific provider preference.
Summary
This post covered the current OpenCode Go model list, $10/month pricing, the $12/$30/$60 usage limits, Omen Alpha (facts vs speculation), and a task-based routing strategy. The key point: OpenCode Go is not about one model — it is a low-cost multi-model coding subscription, and the most effective approach is cheap models for routine agent work, stronger models for difficult tasks, and review passes on the strongest models. Since the lineup changes often, treat this article as a selection guide rather than a fixed catalog.
This post belongs to the docs.bswen.com OpenCode cluster. Related reading: “OpenCode Best Models for Programming”, “OpenCode Go Worth It”, and the older “OpenCode Go: What Is Included” overview — plus “OpenCode vs MiniMax Coding Plan” when comparing subscription plans. If you want more OpenCode and AI coding-agent tips, subscribe to the blog for updates.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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