There are four frontier models worth comparing in mid-2026, and only one ships with open weights: GLM-5.2. Z.ai’s ~753B-parameter mixture-of-experts model enters the conversation by beating GPT-5.5 on SWE-bench Pro, landing near Claude Opus 4.8 on agentic tool use, and doing it at roughly one-sixth of the cost, per VentureBeat. GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro remain closed, metered, and strong.
The practical question is not “which model is smartest?” It is: which model should you build on for your constraints? This comparison focuses on GLM-5.2 vs GPT-5.5 vs Claude Opus 4.8, with Gemini 3.1 Pro included where it matters: coding, agentic workflows, reasoning, context, openness, and price.
For more background, see the GLM-5.1 four-way LLM comparison and the Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.5 breakdown. This article keeps GLM-5.2 as the main subject.
The contenders at a glance
| Dimension | GLM-5.2 | GPT-5.5 | Claude Opus 4.8 | Gemini 3.1 Pro |
|---|---|---|---|---|
| Weights | Open, MIT | Closed | Closed | Closed |
| Architecture | ~753B MoE, BF16 | Undisclosed | Undisclosed | Undisclosed |
| Context window | 1M tokens | Large, undisclosed | Large, undisclosed | Very large |
| API input price | $1.40 / 1M | Higher | Higher | Higher |
| API output price | $4.40 / 1M | Higher | Higher | Higher |
| SWE-bench Pro | 62.1 | 58.6 | n/a | n/a |
| MCP-Atlas, agentic | 77.0 | 75.3 | 77.8 | n/a |
| Self-host | Yes | No | No | No |
Closed-model prices change by tier and provider, so they are listed as “Higher” instead of fixed numbers. GLM-5.2’s API rates are confirmed at $1.40 per million input tokens and $4.40 per million output tokens, per OpenRouter, with cached input around $0.26 per million, attributed by VentureBeat.
Blank benchmark cells mean Z.ai did not publish every head-to-head result for every model.
Coding: where GLM-5.2 wins
GLM-5.2’s strongest case is coding.
On SWE-bench Pro, Z.ai reports:
| Model | SWE-bench Pro |
|---|---|
| GLM-5.2 | 62.1 |
| GPT-5.5 | 58.6 |
| GLM-5.1 | 58.4 |
That makes GLM-5.2 the published winner over GPT-5.5 on this benchmark.
The bigger implementation signal is Terminal-Bench 2.1:
| Model | Terminal-Bench 2.1 |
|---|---|
| GLM-5.2 | 81.0 |
| GLM-5.1 | 62.0 |
That is a ~19-point generation-over-generation jump on terminal-style agentic coding.
For coding workloads, Z.ai recommends using GLM-5.2’s Max thinking-effort level. In practice, that means you should route difficult coding tasks differently from simple autocomplete or short-answer tasks.
Example model-routing logic:
type TaskType = "quick_answer" | "bug_fix" | "multi_file_refactor" | "agentic_coding";
function selectReasoningEffort(task: TaskType) {
switch (task) {
case "quick_answer":
return "low";
case "bug_fix":
return "high";
case "multi_file_refactor":
case "agentic_coding":
return "max";
}
}
The practical takeaway:
- Use GLM-5.2 for coding agents, repository analysis, terminal workflows, and cost-sensitive code generation.
- Keep GPT-5.5 in consideration if your team already depends heavily on the OpenAI ecosystem.
- Use Claude Opus 4.8 when long-running judgment-heavy refactors matter more than benchmark score.
- Use Gemini 3.1 Pro when very large context and Google integration are the primary constraints.
For benchmark-measured coding, GLM-5.2 beats GPT-5.5 on SWE-bench Pro.
Agentic and tool use: close to Claude Opus 4.8
On MCP-Atlas, which measures Model Context Protocol tool orchestration, Z.ai reports:
| Model | MCP-Atlas |
|---|---|
| Claude Opus 4.8 | 77.8 |
| GLM-5.2 | 77.0 |
| GPT-5.5 | 75.3 |
GLM-5.2 is less than one point behind Claude Opus 4.8 and ahead of GPT-5.5.
For implementation, the important part is compatibility. GLM-5.2 supports OpenAI-compatible function and tool calling, plus an Anthropic-compatible coding endpoint. That makes it easier to test GLM-5.2 inside existing agent harnesses built around OpenAI-style or Claude-style APIs.
A typical tool-calling integration should validate:
- Tool schema generation
- Argument quality
- Retry behavior
- Long tool-call history handling
- Latency under real context size
- Cost per completed task, not just cost per token
Example pseudo-flow:
const task = {
goal: "Fix the failing test and update the implementation",
repoContext: "...",
tools: ["read_file", "write_file", "run_tests"]
};
const response = await llm.chat.completions.create({
model: "glm-5.2",
messages: [
{
role: "system",
content: "You are a coding agent. Use tools when needed."
},
{
role: "user",
content: JSON.stringify(task)
}
],
tools: [
{
type: "function",
function: {
name: "run_tests",
description: "Run the project test suite",
parameters: {
type: "object",
properties: {
command: { type: "string" }
},
required: ["command"]
}
}
}
]
});
On Humanity’s Last Exam with tools, Z.ai reports GLM-5.2 at 54.7 versus GPT-5.5 at 52.2.
GLM-5.2 also uses “IndexShare” sparse attention, which reuses one indexer across every four sparse-attention layers. For agents with long histories and many tool calls, lower long-context attention cost can matter in production.
If you want a setup guide, use the GLM-5.2 with Claude Code, Cline, and Cursor guide. For API parameters, see the GLM-5.2 API guide.
Reasoning and math: strong, but validate on your own tasks
On reasoning and math, GLM-5.2 is positioned near the frontier. Z.ai reports:
| Benchmark | GLM-5.2 |
|---|---|
| AIME 2026 | 99.2 |
| GPQA-Diamond | 91.2 |
Treat these as launch claims until they are broadly replicated by third parties.
For implementation, GLM-5.2’s useful feature is direct reasoning control. You can use high or max reasoning for hard problems and disable thinking for fast, low-cost responses.
Example request shape:
{
"model": "glm-5.2",
"messages": [
{
"role": "user",
"content": "Analyze this failing distributed transaction flow and identify the root cause."
}
],
"reasoning_effort": "max",
"thinking": {
"type": "enabled"
}
}
For simple tasks, reduce reasoning:
{
"model": "glm-5.2",
"messages": [
{
"role": "user",
"content": "Summarize this API error in one sentence."
}
],
"thinking": {
"type": "disabled"
}
}
Use this pattern in production:
| Workload | Suggested setting |
|---|---|
| Simple classification | Thinking disabled |
| Short support response | Low or disabled |
| Debugging | High |
| Multi-step coding | Max |
| Agentic tool workflow | Max |
| Long-context document reasoning | High or Max |
GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro are also excellent reasoning models. On open-ended judgment tasks, many users still find the closed frontier more polished. On scored math and science benchmarks, GLM-5.2 is competitive.
Context and openness: GLM-5.2’s main deployment advantage
GLM-5.2 ships with a 1M-token context window, specifically 1,048,576 tokens. Max output is listed as up to 128K in z.ai docs, but because that number is not echoed everywhere, verify the live limit before designing a system around it.
Gemini 3.1 Pro is the closest competitor on very large context. GPT-5.5 and Claude Opus 4.8 also provide large context windows.
The difference is deployment control.
GLM-5.2 is released under the MIT license, has no regional restrictions, and is available as:
That means you can:
- Download the weights
- Run the model air-gapped
- Fine-tune it
- Deploy it without per-token vendor fees
- Keep sensitive workloads away from third-party APIs
For teams with data residency, regulated workloads, or “no external model API” policies, this is the deciding factor. GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro cannot be self-hosted.
For local setup paths, see run GLM-5.2 locally for free and the older run GLM-5 locally guide.
Price: the practical cost argument
GLM-5.2’s API pricing is:
| Cost factor | GLM-5.2 | Closed frontier: GPT-5.5 / Opus 4.8 / Gemini 3.1 Pro |
|---|---|---|
| API input, per 1M tokens | $1.40 | Materially higher |
| API output, per 1M tokens | $4.40 | Materially higher |
| Cached input | ~$0.26 | Varies |
| Self-host option | Yes, no per-token fee | None |
| OpenRouter free tier | No | No |
VentureBeat frames GLM-5.2 as beating GPT-5.5 on long-horizon coding at roughly one-sixth the cost.
There is no free OpenRouter lane for GLM-5.2. If you see one advertised, it is not the official model.
For production cost planning, calculate cost per completed workflow, not only cost per token:
type Usage = {
inputTokens: number;
outputTokens: number;
};
const GLM_52_INPUT_PER_1M = 1.40;
const GLM_52_OUTPUT_PER_1M = 4.40;
function estimateGlm52Cost(usage: Usage) {
const inputCost = (usage.inputTokens / 1_000_000) * GLM_52_INPUT_PER_1M;
const outputCost = (usage.outputTokens / 1_000_000) * GLM_52_OUTPUT_PER_1M;
return {
inputCost,
outputCost,
total: inputCost + outputCost
};
}
console.log(
estimateGlm52Cost({
inputTokens: 500_000,
outputTokens: 80_000
})
);
For current pricing and GLM Coding Plan tiers, verify the latest numbers at z.ai. Secondary sources still disagree on some tier details as of June 2026.
You can also read the dedicated GLM-5.2 pricing breakdown, or route the model through OpenRouter as z-ai/glm-5.2.
For broader cost comparisons, the GLM-5 vs DeepSeek vs GPT-5 speed and cost piece is still useful, although it predates GLM-5.2.
How to choose the right model
Pick by constraint, not hype.
| If your constraint is... | Choose... |
|---|---|
| Best coding-per-dollar | GLM-5.2 |
| Open weights and self-hosting | GLM-5.2 |
| Air-gapped or regulated deployment | GLM-5.2 |
| OpenAI-native ecosystem | GPT-5.5 |
| Long agentic refactors and judgment-heavy coding | Claude Opus 4.8 |
| Very large context plus Google integration | Gemini 3.1 Pro |
| Lowest vendor lock-in | GLM-5.2 |
| Most polished closed generalist experience | GPT-5.5 or Claude Opus 4.8 |
A practical evaluation plan:
- Select 20 to 50 real tasks from your backlog.
- Include easy, medium, and hard examples.
- Measure success rate, not only answer quality.
- Track total input and output tokens.
- Record latency per step and per completed task.
- Test tool-call reliability.
- Run the same harness across GLM-5.2, GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro.
- Choose based on cost per successful task.
For API-heavy workloads, test the model against your own endpoints before committing. Apidog helps you design, debug, mock, and test the API calls behind model workflows in one place. You can use it to compare real latency and tool-call behavior on your own traffic instead of relying only on benchmark charts. Download Apidog and point it at the z.ai endpoint to start.
How GLM-5.2 compares to GLM-5.1
The generational jump matters because GLM-5.2 is not just a minor refresh.
For the full comparison, read the GLM-5.2 vs GLM-5.1 comparison. The GLM-5.2 benchmarks deep dive covers every scored test.
If you are new to the model family, start with what GLM-5.2 is. For the prior generation’s API surface, the GLM-5.1 reference and how to use the GLM-5.1 API still apply with minor changes.
Official release notes are on Z.ai’s blog and in the GLM-5.2 docs. VentureBeat also provides independent context in its GLM-5.2 coverage.
FAQ
Is GLM-5.2 really better than GPT-5.5 at coding?
On SWE-bench Pro, yes: GLM-5.2 scores 62.1 versus GPT-5.5 at 58.6, per Z.ai’s published results. GPT-5.5 still wins some tasks and has deeper ecosystem support, so validate against your workload.
How close is GLM-5.2 to Claude Opus 4.8 on agentic tasks?
Very close. On MCP-Atlas, GLM-5.2 scores 77.0 against Claude Opus 4.8’s 77.8. That is a sub-one-point gap. GLM-5.2 also leads GPT-5.5’s 75.3 on the same benchmark.
Why does GLM-5.2 cost less?
It is open-weights and priced aggressively: $1.40 per million input tokens and $4.40 per million output tokens. VentureBeat describes it as roughly one-sixth the cost of GPT-5.5 on long-horizon coding. You can also self-host it and avoid per-token vendor fees.
Does GLM-5.2 have a vision model?
No confirmed vision variant exists as of June 2026. Per the API docs, GLM-5.2 is text-in, text-out. Do not assume a “GLM-5.2V” until Z.ai ships one.
Can I run GLM-5.2 with Claude Code?
Yes. GLM-5.2 exposes an Anthropic-compatible coding endpoint. You can set ANTHROPIC_BASE_URL, use a GLM Coding Plan key, and point Claude Code at the glm-5.2[1m] variant for the 1M-context model. The GLM-5.2 with Claude Code, Cline, and Cursor guide has the full environment setup.
Final verdict
The frontier is no longer a single leaderboard. It is a set of tradeoffs.
GLM-5.2 does not beat GPT-5.5, Claude Opus 4.8, and Gemini 3.1 Pro on everything. It does not need to. It wins on openness, self-hosting, coding-per-dollar, and competitive agentic tool use. For many engineering teams in 2026, that is enough to make it the default model to test first.
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