Dissecting 13 models on Opencode GO: $10/month, which model should be used for what?
Table of Contents
I used to have 5 API keys for 5 different providers. DeepSeek had one key, MiniMax had one key, OpenRouter had a balance, Anthropic had a subscription… Every end of the month, I would sit and check each dashboard to see how much money was spent, which key was about to expire, which balance had only 3 cents left.
I was exhausted π
Then one day, I stumbled upon Opencode GO - $10/month, 13 models, a single key. My initial reaction was: “Is this a scam? $10/month for unlimited models?”
It turned out it wasn’t. It has a cap on usage - but $60/month is more than enough for the value of requests for a coding agent. After 2 weeks of thoroughly testing each model, this is the article I wish someone had written before I had to figure it out myself.
What is Opencode GO?
In brief: Opencode GO is a $10/month (first month $5) subscription from the Opencode team, providing you with 1 API key to access 13 models - all open-weight and thoroughly tested for coding agents.
No need to use it with the Opencode app. The endpoint is OpenAI-compatible, so you can plug it into Hermes Agent, OpenClaw, Pi Agent, Codex, or any tool that calls LLM API and it will run.
Major advantages:
- Zero-retention policy - your code is not used for training
- Servers located in the US, EU, and Singapore β stable latency for both Europe and Asia
- The Opencode team benchmarks and works with providers to optimize serving
- A single bill instead of monitoring 5 dashboards
Pricing Table for 13 Models
This is the current list of models (as of mid-June 2026), along with their prices and estimated monthly requests within the $60 cap:
| Model | Input / 1M tok | Output / 1M tok | Req / month |
|---|---|---|---|
| GLM-5.2 β | $1.40 | $4.40 | ~4,300 |
| GLM-5.1 | $1.40 | $4.40 | ~4,300 |
| Qwen3.7 Max | $2.50 | $7.50 | ~4,770 |
| Kimi K2.7 Code | $0.95 | $4.00 | ~9,250 |
| Kimi K2.6 | $0.95 | $4.00 | ~5,750 |
| MiniMax M3 π | $0.30 | $1.20 | ~16,000 |
| MiniMax M2.7 | $0.30 | $1.20 | ~17,000 |
| MiMo-V2.5-Pro | $1.74 | $3.48 | ~16,300 |
| DeepSeek V4 Pro | $1.74 | $3.48 | ~17,150 |
| Qwen3.7 Plus | $0.40 | $1.60 | ~21,600 |
| Qwen3.6 Plus | $0.50 | $3.00 | ~16,300 |
| DeepSeek V4 Flash π | $0.14 | $0.28 | ~158,150 |
| MiMo-V2.5 πΈ | $0.14 | $0.28 | ~150,400 |
These request numbers are based on average usage patterns (~700-800 input, ~150-300 output per request). In practice, using an agent (with many tool calls and large context) will result in lower request numbers.
At a glance, it’s clear that GLM-5.2 / Qwen3.7 Max are the most premium options but have the lowest request limits. DeepSeek V4 Flash / MiMo-V2.5 are very affordable, with nearly unlimited requests.
Tier Classification: Which Model Belongs to Which Tier?
I categorize them into 4 tiers based on benchmark results, community consensus, and real-world experience:
TIER S - Top-notch, for the most critical tasks π₯
GLM-5.2 - Zhipu (Z.ai) released in mid-June 2026. 744B MoE, 40B active params, 1M context, MIT license.
Benchmark scores:
- Terminal-Bench 2.1: 81.0 (GLM-5.1 only scored 62.0 - a huge jump)
- SWE-bench Pro: 62.1 (surpassed GPT-5.5)
- Code Arena Frontend: #2 worldwide (after Fable 5, above all Claude Opus)
- Design Arena: #1 globally
- Agent Arena: #1 open model
In short: this is the first open-weight model capable of replacing Claude Opus/GPT-5 for daily coding tasks. It even surpasses Opus 4.8 in frontend development.
Qwen3.7 Max - Alibaba’s ace model. Proprietary, API-only.
- SWE-bench Pro: 60.6% (the highest among proprietary models)
- GPQA Diamond: 92.4% - unparalleled STEM reasoning
- Can run autonomously for 35 hours, with 1,000+ tool calls
- Supports Anthropic API protocol β can replace Claude Code seamlessly
TIER A - Good and affordable, for daily use π
MiniMax M3 π - My new ace model. Released in June 2026, open-weight (soon to be available), 1M context.
- SWE-bench Pro: 59.0% - only 1.6 points behind Qwen Max
- BrowseComp: 83.5 (surpassed Claude Opus 4.7 in autonomous browsing)
- MSA sparse-attention architecture - 1M context that actually works, not just marketing
- Price: $1.20 output - 6.25 times cheaper than Qwen Max
Coding quality is close to Tier S, but the price is only 1/6. The current sweet spot.
DeepSeek V4 Pro - DeepSeek’s flagship model. Strong in general coding, 1M context. Priced at $3.48 output. Suitable for feature development, but avoid giving it complex codebases (see Caveat below).
TIER B - Reliable workhorses π οΈ
GLM-5.1 - The previous generation of GLM-5.2. The community calls it a “safe choice” - it gets the job done without surprises. Coding quality is around 94.6% of Claude Opus 4.6.
MiniMax M2.7 - The previous version of M3. Reddit and bitdoze users call it the “go-to for agentic tasks.” Priced at $0.30/$1.20 - an excellent value.
Kimi K2.7 Code - Moonshot’s coding-focused model. Good cache, long context. Priced at $0.95/$4.00.
Qwen3.7 Plus - Mid-tier Qwen. Priced at $0.40/$1.60 (β€256K context), with the option to expand to 1M context at a higher price. ~21K requests/month.
TIER C - Fast and cheap, for simple tasks πΈ
DeepSeek V4 Flash π - 158K requests/month, $0.28 output. Suitable for bug fixes, explaining code, or writing boilerplate. Fast and affordable.
MiMo-V2.5 - Xiaomi’s base model. Priced similarly to Flash ($0.28 output), 150K requests/month. Suitable for very simple tasks.
MiMo-V2.5-Pro - The pro version of MiMo. Has agentic capabilities, priced at $3.48 output. Falls between tier B and C.
I used to think: “Just use the best model for every task and you’re done.” Wrong. Using GLM-5.2 to debug a single
nil pointerline would waste nearly a thousand tokens - a waste. Tier classification is the key to efficient use.
Categorized by Role: Who Uses Which Model?
This is the most important part. I categorize into 4 roles that programmers often encounter when using AI coding agents:
1. Planner - Planning, Designing Architecture ποΈ
Requires deep reasoning, overall vision, and understanding of large systems.
| Level | Model | Reason |
|---|---|---|
| High-end | Qwen3.7 Max | GPQA 92.4% - Strongest STEM reasoning, 35h autonomous |
| High-end | GLM-5.2 | SWE-bench 62.1, Terminal-Bench 81.0 |
| Cost-effective | MiniMax M3 | 59% SWE-bench Pro, BrowseComp 83.5 |
| Acceptable | DeepSeek V4 Pro | Reads entire source with 1M context |
π I use: MiniMax M3 for daily planning. Qwen3.7 Max when I need architecture review for large systems.
2. Implementer - Writing Code, Implementing Features β¨οΈ
This role uses the most requests β needs a model that is both good and not too expensive.
| Level | Model | Reason |
|---|---|---|
| Best | GLM-5.2 | Frontend #2 TG, Terminal-Bench 81.0 |
| Good and affordable | MiniMax M3 | Coding β Qwen Max, 1/6 price, 1M usable context |
| Stable | DeepSeek V4 Pro | General coding, 17K req/month |
| Stable | GLM-5.1 | Safe choice, “works across use cases” |
| Fast and affordable | DeepSeek V4 Flash | Bug fix, boilerplate, small feature |
π I use: MiniMax M3 for main features (16K req/month is enough). Flash for small tasks. GLM-5.2 for important refactoring.
3. Reviewer - Code Review, Finding Bugs π
Requires attention to detail, reasoning about logic, and edge cases.
| Level | Model | Reason |
|---|---|---|
| High-end | Qwen3.7 Max | STEM reasoning #1 - Finds race conditions, logic bugs |
| High-end | MiniMax M3 | BrowseComp 83.5 - Review with research context |
| Stable | GLM-5.1 | Reliable, doesn’t miss basic bugs |
| Acceptable | DeepSeek V4 Pro | 1M context reads entire PR |
π I use: MiniMax M3 for daily review. Qwen3.7 Max for security audits or important PRs.
4. Quick Chat / Debug - Q&A, Explaining Code π¬
The simplest role, doesn’t require a high-end model. Mainly needs to be fast and affordable.
| Level | Model | Reason |
|---|---|---|
| Versatile | MiniMax M3 | Can do anything, affordable price |
| Fastest | DeepSeek V4 Flash | 158K req/month, fast response |
| Cheapest | MiMo-V2.5 | 150K req/month, $0.28 output |
π I use: Flash for 90% of chat/debug. Very affordable.
3 Strategy Setup for Programmers
There is no one-size-fits-all solution. Choose a strategy based on your budget and needs:
Strategy 1: Cost-Effective (Recommended for Most People) π°
Plan / Architecture β MiniMax M3
Implement main β MiniMax M3
Review β MiniMax M3
Chat / Quick Debug β DeepSeek V4 Flash
Important tasks β GLM-5.2 (used sparingly)
Summary: M3 handles 80% of the workload. Flash handles chat. GLM-5.2 is only enabled when maximum quality is needed (major refactoring, complex features). With ~16K requests/month for M3 and 158K for Flash, it’s unlikely to hit the cap.
Strategy 2: Maximum Savings πΈ
Main implementation β DeepSeek V4 Pro
Chat / Debug β DeepSeek V4 Flash
Plan / Review β MiniMax M3 (when intense reasoning is needed)
Summary: Pro ~17K requests + Flash ~158K requests. Comfortably use both all month without worrying. Only enable M3 for planning and review.
Strategy 3: Maximum Quality (No Budget Concerns) π₯
Plan β Qwen3.7 Max
Implementation β GLM-5.2
Review β Qwen3.7 Max
Chat / Debug β MiniMax M3
Simple tasks β DeepSeek V4 Flash
Summary: The highest quality possible in the Go plan. However, be cautious - GLM-5.2 only has ~4,300 requests/month, Qwen Max ~4,770. If you code all day, it’s easy to run out. Only use if you code infrequently but need absolute quality for each task.
Sample Configuration for OpenCode
If you are using the OpenCode CLI, here is how you can map models in your opencode.json:
{
"models": {
"go-plan": {
"provider": "opencode-go",
"model": "minimax-m3"
},
"go-implement": {
"provider": "opencode-go",
"model": "minimax-m3"
},
"go-review": {
"provider": "opencode-go",
"model": "minimax-m3"
},
"go-chat": {
"provider": "opencode-go",
"model": "deepseek-v4-flash"
},
"go-max": {
"provider": "opencode-go",
"model": "glm-5.2"
}
}
}
For use with other tools (Hermes, OpenClaw, Pi Agent), set:
export OPENAI_BASE_URL="https://opencode.ai/zen/go/v1/chat/completions"
export OPENAI_API_KEY="sk-go-xxx"
# Model ID: opencode-go/<model-id>
# For example: opencode-go/glm-5.2, opencode-go/deepseek-v4-flash
Alternatively, if your tool supports the Anthropic protocol, Qwen and MiniMax models also have their own endpoints:
# Anthropic-compatible endpoint
export ANTHROPIC_BASE_URL="https://opencode.ai/zen/go/v1/messages"
# Model: qwen3.7-max, minimax-m3, minimax-m2.7, qwen3.7-plus...
A Few Things to Keep in Mind (To Avoid Disappointment)
1. Go Plan is Not Unlimited
There is a cap of $12/5h, $30/week, $60/month. Some expensive models like GLM-5.2 can quickly exceed the limit when used extensively in long sessions. If you code 8 hours a day using GLM-5.2, you can reach the 5-hour cap in just 2-3 days.
Fix: enable “Use balance” in the console - it will fallback to Zen balance when the Go limit is exceeded.
2. DeepSeek V4 is Not Suitable for Complex Codebases
This is a consensus from the community, not just a personal opinion. Quoting a review:
“DeepSeek V4 is gonna give you headaches if working with a real complex codebase.”
Use DeepSeek for simple tasks: bug fixing a single file, boilerplate, or doc generation. Avoid using it for refactoring multiple files or architecture changes.
3. GLM-5.2 Benchmark is Impressive but Newly Released
The benchmark is impressive - Terminal-Bench 81.0, SWE-bench 62.1. However, it was released in mid-June 2026, and there are not many production reviews yet. The Z.ai team has not published a detailed paper either. Use caution and do not ship production code without reviewing it again.
4. Go Plan Pricing is Based on Actual Token Usage, Not a Flat Rate
Even if you pay a $10/month flat fee, the usage cap is calculated based on the dollar value of the tokens. This means each model “consumes” the cap differently depending on its price. Using cheaper models can provide more requests - take advantage of this.
5. You Can Self-Host Some Models
MiniMax M3 and GLM-5.2 have open weights (M3 is upcoming, GLM-5.2 is MIT licensed). If you have a GPU, you can self-host to avoid caps. However, in reality, the cost of an idle GPU can be more expensive than the $10/month Go plan. Use Go for convenience and self-host when scaling.
Conclusion
Before testing the Go plan, I used to spend an average of $15-20/month on direct API keys - and only got 2-3 models. Now, $10/month gets me 13 models, one key, and one bill.
But more importantly, it’s about strategy. Don’t be like me at first: using the most advanced model for every task. Debugging a null pointer line and using GLM-5.2 to handle it - it’s a waste, just like using a tank to transport coffee.
Bottom line: MiniMax M3 for heavy tasks, DeepSeek Flash for light tasks, GLM-5.2 for important tasks. These three handle 99% of what I need.
What model are you using on Opencode GO? Do you agree with my division? Share your setup - I’m curious to know about others’ configurations π¦
This article is based on real-world experience + benchmarks from OpenCode team, Z.ai, Alibaba, MiniMax, DeepSeek, CodingFleet, Lushbinary, Latent Space, and discussions from the r/opencodeCLI community. Prices and model lists are updated until 17/06/2026 - subject to change.
