GLM-5.2 Benchmarks and Specs: SWE-bench Pro, Terminal-Bench, and What the Numbers Mean

GLM-5.2 from Z.ai (Zhipu AI) ships with several benchmark claims worth checking before you put it into a coding or agent workflow. The headline number is SWE-bench Pro at 62.1, slightly ahead of GPT-5.5. The more implementation-relevant jump is Terminal-Bench 2.1, where GLM-5.2 moves from 62.0 to 81.0 versus GLM-5.1. This guide breaks down each score, what the benchmark actually tests, and how to validate the model against your own engineering workload.

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All launch numbers below are Z.ai’s published results unless stated otherwise. Treat vendor benchmark tables as a starting point, not a deployment decision. The useful question is not “did the model win a row?” but “does this score map to the kind of coding, terminal, tool-use, and API work I need it to perform?”

💡If you build or test APIs while evaluating models like this, Apidog is the all-in-one platform we use to design, debug, mock, and document the endpoints these models call. This matters because many GLM-5.2 gains show up in agentic and tool-use workloads, where every tool call is effectively an API request.

GLM-5.2 benchmark scores at a glance

Here is the benchmark table reported by Z.ai, with nearby models included for context.

Benchmark	What it measures	GLM-5.2	GLM-5.1	GPT-5.5	Claude Opus 4.8
SWE-bench Pro	Real-world repo bug fixes	62.1	58.4	58.6	n/a
Terminal-Bench 2.1	Multi-step shell/agent tasks	81.0	62.0	n/a	n/a
MCP-Atlas	Tool-use over MCP servers	77.0	n/a	75.3	77.8
Humanity’s Last Exam (w/ tools)	Hard expert reasoning	54.7	n/a	52.2	n/a
AIME 2026	Competition math	99.2	n/a	n/a	n/a
GPQA-Diamond	Graduate-level science	91.2	n/a	n/a	n/a

Z.ai also reports GLM-5.2 as the highest-scoring open-source model on FrontierSWE, PostTrainBench, and SWE-Marathon. That qualifier matters: “highest open-source” is not the same as “highest overall.”

For a broader model overview, see the GLM-5.2 overview. For a direct comparison against proprietary models, see GLM-5.2 vs GPT-5.5, Opus, and Gemini.

SWE-bench Pro: what 62.1 means for coding tasks

SWE-bench Pro evaluates whether a model can fix real GitHub issues inside real repositories. The model receives the issue and repository context, then must produce a patch that passes hidden tests.

That makes it one of the more practical coding benchmarks because it tests skills developers actually care about:

• reading unfamiliar code
• locating the relevant files
• understanding project structure
• editing multiple files safely
• avoiding regressions
• producing a patch that passes tests

GLM-5.2 scores 62.1. Z.ai reports GPT-5.5 at 58.6 and GLM-5.1 at 58.4.

The implementation takeaway:

• The lead over GPT-5.5 is real but modest. A 3.5-point difference can be affected by prompts, retries, test harness configuration, and evaluation setup.
• The gain over GLM-5.1 is more useful as a signal because it compares two generations under the same reporting source.
• If your use case is repository maintenance, bug fixing, or coding agents, SWE-bench Pro is relevant enough to justify testing GLM-5.2 directly.

A practical eval for your own repo could look like this:

# 1. Pick real closed issues or bugs from your repo
git checkout -b glm-eval/issue-123

# 2. Give the model the issue, failing test, and repo context

# 3. Ask for a minimal patch

# 4. Run the project test suite
npm test
# or
pytest
# or
go test ./...

Track more than “did it pass?” Record:

• number of attempts
• files modified
• whether the model found the right failure
• whether it introduced unrelated changes
• whether the patch is maintainable

Terminal-Bench 2.1: the most important GLM-5.2 number

Terminal-Bench evaluates a model as an agent operating in a shell. It must run commands, install dependencies, inspect output, recover from errors, and complete multi-step tasks.

This maps closely to real developer-agent work:

npm install
npm test
cat package.json
grep -R "failingFunction" src/
npm run build

GLM-5.1 scored 62.0. GLM-5.2 scores 81.0.

That 19-point jump is the strongest result in the table. It suggests a major improvement in long-horizon execution, not just answer quality. For developers, this is the difference between a model that can suggest commands and a model that may reliably operate inside an iterative terminal loop.

Z.ai attributes part of this improvement to GLM-5.2’s “IndexShare” sparse attention, which reuses one indexer across every four sparse-attention layers to reduce long-context attention costs. That matters because terminal agents create long transcripts:

command -> output -> error -> diagnosis -> command -> output -> next step

If the model loses earlier context, it repeats work or breaks the task. A 1M-token context window and better long-context handling are directly relevant to this kind of workflow.

For the generational comparison, see GLM-5.2 vs GLM-5.1.

Caveat: Terminal-Bench results are sensitive to agent scaffolding. Timeouts, retries, system prompts, allowed tools, and execution harness design all matter. Before using GLM-5.2 in CI, deployment automation, or repo-level agents, run your own shell-task benchmark.

MCP-Atlas: tool-use performance is effectively tied at the top

MCP-Atlas measures tool use through the Model Context Protocol. In practical terms, it tests whether a model can:

• choose the correct tool
• format the tool call correctly
• pass valid parameters
• parse the response
• continue the task after receiving the result
• handle tool errors

Z.ai reports:

• GLM-5.2: 77.0
• GPT-5.5: 75.3
• Claude Opus 4.8: 77.8

This is not a decisive win for any model. The scores are close enough to treat the top models as roughly tied for MCP-style tool use.

For developers, the more important point is that GLM-5.2 appears competitive for API-backed agents. Tool use is API work: structured input, structured output, validation, error handling, retries, and state management.

A simple MCP-style tool call might look like this:

{
  "tool": "getCustomerById",
  "arguments": {
    "customerId": "cus_123"
  }
}

Things can still fail in production-like workflows:

{
  "tool": "getCustomerById",
  "arguments": {
    "id": "cus_123"
  }
}

That payload may be semantically close but invalid if the API expects customerId.

This is where Apidog fits into the evaluation loop. Define and mock the endpoints your agent will call, then inspect the actual requests and responses the model generates before connecting it to production services. You can also download Apidog to test model-generated API calls like normal integration traffic.

Reasoning and math: HLE, AIME, and GPQA-Diamond

GLM-5.2 is not only positioned as a coding model. Z.ai also reports strong reasoning and technical QA numbers.

Humanity’s Last Exam with tools: 54.7

Humanity’s Last Exam is designed to be difficult across expert-level domains. The “with tools” setting allows the model to use search or computation rather than answer from memory only.

Z.ai reports:

• GLM-5.2: 54.7
• GPT-5.5: 52.2

The margin is small, but a score in the 50s on this benchmark is still notable.

AIME 2026: 99.2

AIME is competition math. A score of 99.2 is effectively near the ceiling. This is useful as a “no obvious math weakness” signal, but it may not separate frontier models well if many are already saturated.

GPQA-Diamond: 91.2

GPQA-Diamond focuses on graduate-level science questions that are hard for non-experts. A 91.2 score puts GLM-5.2 in frontier-model territory for technical reasoning.

The practical takeaway: GLM-5.2 is not narrowly optimized for code at the expense of math and science reasoning. Z.ai also describes two thinking-effort levels, High and Max, with Max recommended for coding. That gives you a latency/depth tradeoff to test depending on the task.

For a broader benchmark comparison, see GLM-5.2 benchmarks vs the field.

What “highest open-source” actually means

Z.ai reports GLM-5.2 as the top open-source model on FrontierSWE, PostTrainBench, and SWE-Marathon.

Read that carefully. “Highest open-source” means the comparison is against open-weights models, not necessarily every closed proprietary model.

That distinction matters for implementation decisions. If your constraint is:

• self-hosting
• open weights
• MIT license
• no regional restrictions
• avoiding a closed API dependency

then GLM-5.2’s open-source position is highly relevant.

If your only goal is maximum benchmark score and you are comfortable using closed APIs, then you should compare it against proprietary frontier models directly.

Z.ai makes direct claims where it reports GLM-5.2 ahead of GPT-5.5, such as SWE-bench Pro and HLE. For FrontierSWE, PostTrainBench, and SWE-Marathon, the open-source qualifier defines the comparison set.

VentureBeat characterized GLM-5.2 as beating GPT-5.5 on long-horizon coding at roughly one-sixth the cost. That is VentureBeat’s framing and should be treated as attributed analysis, not an independently verified universal cost claim.

GLM-5.2 specs developers should check before testing

Benchmarks only matter if the model fits your deployment and runtime constraints.

Spec	Value
Parameters	~753B total, mixture-of-experts (MoE)
Precision	BF16
Attention	IndexShare sparse attention (one indexer shared per 4 sparse layers)
Context window	1M tokens (1,048,576)
Max output	Up to 128K per z.ai docs (verify live; OpenRouter does not list a figure)
Modality	Text in, text out (no confirmed vision variant)
Thinking effort	High and Max; can be disabled
License	MIT, open weights, no regional restrictions
Model ids	HF zai-org/GLM-5.2, API glm-5.2, Ollama glm-5.2, OpenRouter z-ai/glm-5.2

A few implementation notes:

• The ~753B parameter count is the total MoE size, not necessarily dense active compute per token.
• The 1M-token context window is relevant for long repo analysis, terminal agents, and multi-turn tool use.
• Z.ai docs cite up to 128K max output as of June 2026, but provider limits may differ.
• There is no confirmed GLM-5.2 vision model. If you see “GLM-5.2V,” verify it against Z.ai’s official releases.

Pricing follows the open-weights logic. OpenRouter lists $1.40 per 1M input tokens and $4.40 per 1M output tokens, with cached input around $0.26 per 1M according to VentureBeat’s figure. For more detail, see the GLM-5.2 pricing page. If you want to avoid per-token API pricing, how to use GLM-5.2 for free covers the self-host route.

How to verify GLM-5.2 on your own workload

Do not ship based only on benchmark tables. Build a small eval that mirrors your actual tasks.

1. Read the primary sources

Start with the official sources:

• Z.ai GLM-5.2 blog
• Z.ai docs
• Hugging Face model card

Use these to confirm model IDs, context limits, licensing, and deployment options.

2. Check provider listings

Then verify runtime availability and pricing:

• OpenRouter page
• Ollama library entry
• VentureBeat’s coverage

Provider details can differ from model-card details, especially around max output, pricing, and serving constraints.

3. Run a repo-level coding eval

Pick a representative set of tasks:

• bug fixes
• failing tests
• dependency upgrades
• refactors
• feature requests
• documentation updates
• CI failures

For each task, record:

Task ID:
Repo:
Prompt:
Model:
Thinking mode:
Pass/fail:
Number of attempts:
Files changed:
Tests run:
Human review result:
Notes:

Use prior-generation context if helpful:

• GLM-5.1
• GLM-5 vs DeepSeek vs GPT-5 speed and cost

4. Test tool calls separately from final answers

Tool-use failures are often hidden inside otherwise good responses. Watch for:

• malformed JSON
• wrong parameter names
• missing required fields
• incorrect tool choice
• weak error handling
• repeated failed calls
• unsafe calls to production endpoints

A mock endpoint is enough to catch many of these issues. For example, define a customer lookup endpoint and test whether the model consistently sends the right schema:

{
  "customerId": "cus_123"
}

instead of:

{
  "id": "cus_123"
}

Mocking those endpoints in Apidog lets you inspect the model’s actual requests and responses without hitting live services. That is often the fastest way to tell whether a model that performs well on MCP-Atlas will behave correctly in your own stack.

Takeaway

GLM-5.2’s benchmark sheet is worth taking seriously, especially for developer-agent workloads. The strongest result is the Terminal-Bench jump from 62.0 to 81.0. The SWE-bench Pro lead over GPT-5.5 is meaningful but modest. MCP-Atlas shows GLM-5.2 competing in the same range as GPT-5.5 and Claude Opus 4.8 for tool use.

The combination of open weights, MIT licensing, 1M-token context, strong coding-agent scores, and competitive reported pricing makes GLM-5.2 a model worth evaluating.

Use the benchmarks to decide whether it belongs in your shortlist. Use your own repo, terminal tasks, and API/tool-call tests to decide whether it belongs in production. When that evaluation involves real endpoints, set them up in Apidog so you can inspect exactly what the model sends and receives before it touches live systems.