# GLM Capability Map — What GLM Is Best At (vs Claude Opus/Sonnet)

> Scope: The GLM family from Zhipu AI / Z.ai used inside Claude Code as an alternative
> to Anthropic Opus/Sonnet. Covers GLM-4.5, GLM-4.6, the GLM Coding Plan, and the
> current flagship the user calls **"GLM 5.2"** (officially **GLM-5.2**, released
> 13 Jun 2026). Focus: **what GLM is best at** — a detailed capability map.
>
> Research date: 2026-06-30. Many "beats Opus/GPT" claims are **vendor-reported**
> and flagged inline. Benchmark numbers vary 2–5 pts across sources/providers.

---

## 0. TL;DR capability stance

- **GLM is a genuine frontier-adjacent coding model** at ~1/6 the cost of Opus. Its
  sweet spots are **frontend/UI generation, well-specified routine coding, tool
  calling/MCP, and repo-scale context work** (1M tokens on GLM-5.2).
- **Opus still clearly wins** on the hardest, longest, most open-ended work: large
  multi-step refactors, subtle debugging, autonomous multi-hour agentic runs,
  self-correction/replanning, and "design taste."
- The smart pattern is **routing**: GLM as the cheap default for the bulk of tasks,
  Opus reserved for the expensive-if-wrong minority.

---

## 1. Model lineup & specs (context window, output, thinking mode)

| Model | Released | Arch | Context | Max output | Thinking mode | License |
|---|---|---|---|---|---|---|
| GLM-4.5 | 2025 | 355B MoE | 128K | 96K | Auto-determined CoT | Open (MIT) |
| GLM-4.6 | 30 Sep 2025 | 357B MoE | **200K** (~202,752) | **128K** (~131,072) | Auto-determined CoT | Open (MIT) |
| GLM-5 | 11 Feb 2026 | 744B MoE / 40B active | 200K | 131,072 | Auto | Open (MIT) |
| GLM-5.1 | Apr 2026 | 744B MoE / 40B active | 200K | 131,072 | Auto | Open (MIT) |
| **GLM-5.2** | **13 Jun 2026** | **~753B MoE** | **1,000,000 (1M)** | 131,072 | Auto | Open (MIT) |
| GLM-4.7 | (variant) | — | — | — | **Forced** thinking | Open |

Notes:
- **Thinking/reasoning mode**: GLM-4.5/4.6 and the GLM-5.x line *auto-determine*
  whether to engage chain-of-thought (the `thinking` parameter defaults to enabled).
  GLM-4.7 and GLM-4.5V use **forced** thinking. ([z.ai docs](https://docs.z.ai/guides/overview/concept-param))
- GLM-4.6 expanded context 128K → 200K and added tool use *during* inference.
  ([HowAIWorks](https://howaiworks.ai/blog/glm-4-6-announcement), [CometAPI](https://www.cometapi.com/what-is-glm-4-6/))
- **GLM-5.2's 1M context is its headline feature** — described as a "stable 1M-token
  window," explicitly aimed at repo-scale long-horizon coding. ([VentureBeat](https://venturebeat.com/technology/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost), [TheAIRankings](https://theairankings.com/zhipu/glm-5/))
- ⚠️ The user's "GLM 5.2" = GLM-5.2. If they are on an older GLM Coding Plan they may
  actually be served **GLM-4.6** or **GLM-5/5.1** — capabilities differ a lot
  between 4.6 and 5.2, so confirm which is actually wired into their Claude Code.

---

## 2. Coding ability overall — benchmarks vs Claude & GPT

### GLM-4.6 era (the original "GLM coding plan" model)
| Benchmark | GLM-4.6 | Claude Sonnet 4.5 | Notes |
|---|---|---|---|
| LiveCodeBench v6 | **82.8%** | 70.1% | GLM **wins** (contamination-resistant). Up from GLM-4.5's 63.3%. |
| SWE-bench Verified | ~68.0% | **77.2%** | Claude **wins** (real GitHub issue fixing). |
| AIME-25 (math) | **93.9%** | 87.0% | GLM **wins**. |
| CC-Bench multi-turn | 48.6% win rate **vs Sonnet 4** | — | ⚠️ vs Sonnet **4**, not 4.5. ~5–7× cheaper. |

Sources: [Cirra](https://cirra.ai/articles/glm-4-6-vs-claude-sonnet-comparison),
[adam.holter.com](https://adam.holter.com/glm-4-6-vs-claude-sonnet-4-5-benchmarks-capabilities-and-cost-effectiveness/),
[IntuitionLabs](https://intuitionlabs.ai/articles/glm-4-6-open-source-coding-model).
Consensus: GLM-4.6 ≈ Claude Sonnet 4 level, **trails Sonnet 4.5** on real
repo-level work but **wins on isolated code-gen/algorithmic** benchmarks.

### GLM-5 / 5.1 / 5.2 era (current)
| Benchmark | GLM-5.2 | GLM-5.1 | Claude Opus 4.8 | GPT-5.5 |
|---|---|---|---|---|
| SWE-bench Verified | — | 77.8% | ~80.8–81.4% (Opus 4.6) | 80.0% (GPT-5.2) |
| SWE-bench **Pro** | 62.1 | 58.4 | **69.2** | 58.6 |
| FrontierSWE (long-horizon) | 74.4% | — | **75.1%** | 72.6% |
| Terminal-Bench 2.1 | 81.0% | — | — | — |
| NL2Repo | 48.9 | — | **69.7** | — |
| SWE-Marathon | 13.0 | — | **26.0** | — |
| MCP-Atlas (tool use) | **77.0** | — | 75.3 | — |
| Agentic aggregate avg | **81** | — | 80.1 | — |

Sources: [VentureBeat](https://venturebeat.com/technology/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost),
[digitalapplied GLM-5.2](https://www.digitalapplied.com/blog/glm-5-2-benchmarks-open-weights-vs-claude-opus),
[MindStudio agentic](https://www.mindstudio.ai/blog/glm-5-2-vs-gpt-5-5-vs-claude-opus-agentic-workflows),
[Serenities GLM-5.1](https://serenitiesai.com/articles/glm-5-1-zhipu-coding-benchmark-claude-opus-comparison-2026).

**Reading the numbers:**
- GLM-5.2 is the **top open-weight model** per independent Artificial Analysis.
- It **ties/edges Opus on agentic & frontend aggregates and tool use (MCP-Atlas)**.
- Opus **pulls clearly ahead on the hardest long-horizon benchmarks**: SWE-bench Pro
  (69.2 vs 62.1), NL2Repo (69.7 vs 48.9), SWE-Marathon (26.0 vs 13.0). The gap is
  ~7 pts on Pro but shrinks to ~1 pt on several long-horizon coding tests.
- ⚠️ Many headline "beats GPT-5.5 / near-Opus" figures are **Zhipu-reported** and
  pending independent corroboration. Benchmark *names* matter: SWE-bench **Pro** ≠
  SWE-bench **Verified** — don't cross-compare them.

---

## 3. Frontend (React / HTML / CSS / UI generation) — **GLM's standout strength**

- Z.ai explicitly tunes GLM for **"superior aesthetics and logical layout in
  frontend code."** ([z.ai docs](https://docs.z.ai/guides/llm/glm-4.6))
- GLM-5.2 ranks **#2 on LMArena Code Arena Frontend** — above Opus 4.7 and Opus 4.8
  in thinking mode (developer-judged) — and **ties Opus 4.8 on FrontierSWE
  (74.4 vs 75.1)**. An MIT model beating closed flagships on frontend, as judged by
  devs. ([MindStudio UI](https://www.mindstudio.ai/blog/glm-5-2-vs-claude-opus-4-8-ui-generation))
- Hands-on (GLM-4.6): a built payment-platform site was "polished, no visible
  mistakes on first review… animations, vibrant colors… on par with Claude Sonnet 4."
  ([KDnuggets](https://www.kdnuggets.com/vibe-coding-with-glm-46-coding-plan))

**Strengths:** high-volume UI scaffolding, dashboards, component libraries, structured
layouts — near-Opus quality at a fraction of cost.
**Weakness:** **design taste** — when creative visual judgment/interpretation matters,
Opus wins. GLM is the volume/cost pick; Opus is the taste pick. ([MindStudio UI](https://www.mindstudio.ai/blog/glm-5-2-vs-claude-opus-4-8-ui-generation))

---

## 4. Backend (APIs, databases, systems, refactors)

**Strengths:**
- **Routine, well-specified changes** are reliable: add a model field, update an API
  endpoint, refactor a single function. ([MindStudio](https://www.mindstudio.ai/blog/glm-5-2-vs-gpt-5-5-vs-claude-opus-agentic-workflows))
- **Repo-scale context (GLM-5.2, 1M)** is "genuinely transformative" — dump a
  500-file monorepo subset, skip RAG/pruning, make a decision with full context.
- Python / JavaScript / Java are the explicitly optimized backend languages.

**Weaknesses:**
- **Large, multi-step refactors**: Opus "rarely loses the plan on a 30-step refactor,"
  rarely hallucinates a function signature; GLM is less reliable here.
- Hardest backend benchmarks favor Opus (NL2Repo, SWE-Marathon, SWE-bench Pro).
- ⚠️ Caveat repeated across sources: **nobody has publicly run GLM-5.2 as an agent over
  a real 200K-line repo and reported results** — validate long-horizon claims on your
  own branch. ([MindStudio](https://www.mindstudio.ai/blog/glm-5-2-vs-gpt-5-5-vs-claude-opus-agentic-workflows))

---

## 5. Agentic / tool-use / long-horizon reliability

**Strengths:**
- **Tool calling is clean and schema-adherent**: GLM-4.6 "refuses unknown tools and
  minimizes invented arguments," aiming for near-zero tool-call hallucination — less
  cleanup of malformed output. ([Cirra tool calling](https://cirra.ai/articles/glm-4-6-tool-calling-mcp-analysis))
- GLM-4.5 hit **90.6% on BrowseComp** tool-calling success; GLM-5.2 **MCP-Atlas 77.0
  > Opus 75.3**. Built with agents/MCP in mind.
- Excellent at **well-defined, explicitly-stepped agentic tasks**.

**Weaknesses (the real gap):**
- **Self-correction & replanning**: GLM executes defined sub-tasks well but "struggles
  with the self-correcting behavior that makes truly agentic coding reliable." Opus
  recognizes bad output and course-corrects without being told. ([MindStudio](https://www.mindstudio.ai/blog/glm-5-2-vs-gpt-5-5-vs-claude-opus-agentic-workflows))
- **Goal drift / "escapism"** in long debugging: a research trajectory on SWE-bench
  Django #11149 showed GLM-4.6 wandering through irrelevant modules and dodging env
  errors with non-representative scripts ("agent collapse"). ([GLM-5 paper](https://arxiv.org/pdf/2602.15763))
- **Long-horizon autonomy gap**: τ²-style agent test GLM ~75.9% vs Claude 88.1%;
  Claude demonstrated 30+ hr continuous sessions GLM hasn't matched (GLM-4.6 era).
- **GUI/computer control** is only rudimentary (deprioritized); Claude leads on
  browser/desktop control. ([Cirra](https://cirra.ai/articles/glm-4-6-tool-calling-mcp-analysis))
- Inside Claude Code, **vision needs an extra MCP server** with GLM (Claude is native).
  ([ruidiao](https://ruidiao.substack.com/p/two-days-with-glm-as-my-claude-code))

---

## 6. Languages — strongest / weakest

- **Strongest (explicitly optimized):** **Python, JavaScript, Java** — Z.ai documents
  these by name, with frontend aesthetics emphasis. ([z.ai docs](https://docs.z.ai/guides/llm/glm-4.6))
- LiveCodeBench v6 (multi-language write/run/debug): GLM-4.6 **82.8%**.
- **Weakest:** ⚠️ no GLM-specific Rust/Go/C/C++ numbers published. Industry-wide
  pattern (Multi-SWE-bench) is that all models score far higher on Python than Go,
  Rust, C, C++ — treat GLM's systems-language output as **less reliable, verify more.**
  ([Multi-SWE-bench](https://arxiv.org/pdf/2504.02605))

## 7. Multilingual / non-English

- Chinese-origin model; strong natural-language multilingual. GLM-4.6 notes optimized
  translation for French, Russian, Japanese, Korean and informal/role-play contexts.
- ⚠️ No specific evidence that *coding* quality differs by the developer's natural
  language; Chinese-language tasks are likely a relative strength but unbenchmarked here.

## 8. Where GLM matches Opus vs clearly falls short

**Matches / beats Opus:**
- Frontend/UI (Code Arena Frontend #2; ties FrontierSWE), isolated code-gen
  (LiveCodeBench), math (AIME), tool-call hygiene (MCP-Atlas), agentic *aggregate*,
  cost (≈1/6), context size (1M), open weights / data residency / self-host.

**Falls clearly short of Opus:**
- Large multi-step refactors, subtle/long debugging (goal drift), open-ended
  autonomous planning & self-correction, longest agentic benchmarks (SWE-bench Pro,
  NL2Repo, SWE-Marathon), GUI/computer control, design taste, native vision in
  Claude Code, and raw track-record/reliability on high-stakes codebases.

---

## 9. Capability matrix — **use GLM for X / use Opus for Y**

| Task type | Use GLM | Use Opus | Why |
|---|---|---|---|
| Boilerplate / scaffolding | ✅ **GLM** | | Cheap, fast, reliable on well-specified output |
| Simple CRUD / single-endpoint APIs | ✅ **GLM** | | Well-defined = GLM's strength |
| Frontend UI / dashboards / components | ✅ **GLM** | (taste-critical → Opus) | Frontend is GLM's standout; Opus only for design taste |
| Routine refactor (one function/field) | ✅ **GLM** | | Defined, local scope |
| Large multi-file / 30-step refactor | | ✅ **Opus** | Opus holds the plan; GLM drifts |
| Repo-scale read/analysis (huge codebase) | ✅ **GLM-5.2 (1M ctx)** | | 1M context = no RAG needed |
| Subtle / long debugging | | ✅ **Opus** | GLM goal-drift & "escapism" |
| Complex architecture / build-from-spec | | ✅ **Opus** | Open-ended planning + self-correction |
| Tool calling / MCP-heavy workflows | ✅ **GLM** | | Clean schema adherence; MCP-Atlas > Opus |
| Long-horizon autonomous agent (hours) | | ✅ **Opus** | GLM hasn't matched sustained autonomy |
| GUI / browser / desktop control | | ✅ **Opus** | GLM rudimentary |
| Security-sensitive code | | ✅ **Opus** | Reliability/track record; verify GLM closely |
| Systems langs (Rust/Go/C/C++) | (verify) | ✅ **Opus** | GLM unbenchmarked, weaker training data |
| Math / algorithmic codegen | ✅ **GLM** | | AIME 93.9%, LiveCodeBench 82.8% |
| Research / summarization over big docs | ✅ **GLM-5.2** | | 1M context + cheap |
| Vision tasks in Claude Code | | ✅ **Opus** | GLM needs extra MCP server |
| High-volume / cost-constrained anything | ✅ **GLM** | | ≈1/6 the price; route the cheap 80% here |

**Cost anchor:** GLM-5.2 ≈ $1.40 in / $4.40 out per M tokens (~$5.80 combined) vs
Opus 4.8 $5 / $25 and GPT-5.5 $5 / $30 (~$35). GLM Coding Plan starts ~$3/mo.
([MindStudio agentic](https://www.mindstudio.ai/blog/glm-5-2-vs-gpt-5-5-vs-claude-opus-agentic-workflows),
[KDnuggets](https://www.kdnuggets.com/vibe-coding-with-glm-46-coding-plan))

**Recommended architecture:** route by complexity — GLM as default for routine/
high-volume/frontend/repo-scale, escalate to Opus for expensive-if-wrong work
(large refactors, subtle bugs, security, long autonomous runs).

---

## Sources
- Cirra — [GLM-4.6 vs Sonnet](https://cirra.ai/articles/glm-4-6-vs-claude-sonnet-comparison), [tool calling/MCP](https://cirra.ai/articles/glm-4-6-tool-calling-mcp-analysis)
- [adam.holter.com — GLM-4.6 vs Sonnet 4.5](https://adam.holter.com/glm-4-6-vs-claude-sonnet-4-5-benchmarks-capabilities-and-cost-effectiveness/)
- [IntuitionLabs — GLM-4.6 open-source coding](https://intuitionlabs.ai/articles/glm-4-6-open-source-coding-model)
- Z.AI docs — [params/thinking](https://docs.z.ai/guides/overview/concept-param), [GLM-4.6](https://docs.z.ai/guides/llm/glm-4.6), [GLM-5.1](https://docs.z.ai/guides/llm/glm-5.1)
- [OpenRouter — GLM-4.6](https://openrouter.ai/z-ai/glm-4.6) · [CometAPI](https://www.cometapi.com/what-is-glm-4-6/) · [HowAIWorks](https://howaiworks.ai/blog/glm-4-6-announcement)
- [VentureBeat — GLM-5.2](https://venturebeat.com/technology/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost)
- MindStudio — [UI generation](https://www.mindstudio.ai/blog/glm-5-2-vs-claude-opus-4-8-ui-generation), [agentic workflows](https://www.mindstudio.ai/blog/glm-5-2-vs-gpt-5-5-vs-claude-opus-agentic-workflows), [GLM-5.2 in Claude Code](https://www.mindstudio.ai/blog/how-to-use-glm-5-2-in-claude-code)
- [digitalapplied — GLM-5.2 benchmarks](https://www.digitalapplied.com/blog/glm-5-2-benchmarks-open-weights-vs-claude-opus)
- [Serenities — GLM-5.1 vs Opus](https://serenitiesai.com/articles/glm-5-1-zhipu-coding-benchmark-claude-opus-comparison-2026) · [TheAIRankings — GLM-5.2](https://theairankings.com/zhipu/glm-5/)
- [KDnuggets — GLM-4.6 coding plan](https://www.kdnuggets.com/vibe-coding-with-glm-46-coding-plan) · [ruidiao — GLM as Claude Code backend](https://ruidiao.substack.com/p/two-days-with-glm-as-my-claude-code)
- [GLM-5 paper (arXiv)](https://arxiv.org/pdf/2602.15763) · [Multi-SWE-bench (arXiv)](https://arxiv.org/pdf/2504.02605)

> ⚠️ Uncertainty flags: GLM-5.2 "beats Opus/GPT" claims largely vendor-reported;
> SWE-bench Pro ≠ Verified; GLM-5.2 long-horizon real-repo behavior not independently
> stress-tested; no GLM-specific Rust/Go numbers; exact context/output figures vary by
> provider. Confirm which GLM version the user's Coding Plan actually serves.