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# GLM vs Claude: Which Should You Use in 2026?
- URL: https://www.edgewisely.com/glm-vs-claude/
- Published: 2026-11-06T05:18:43.000Z
- Updated: 2026-11-06T05:18:43.000Z
- Description: GLM-4.7 vs Claude compared on price, benchmarks, license and Claude Code. GLM-4.7 is MIT-licensed and roughly 5-10x cheaper; Claude leads on agentic reliability and frontier reasoning. A clear, sourced verdict by use case.
- Author: John Karpentar
- Tags: AI, Opinion

Pick **Claude** when you need top-tier reasoning and the most reliable agentic coding: long autonomous runs, fewer wrong turns, enterprise support. Pick **GLM-4.7** when you want roughly **80-90%** of that quality at a fraction of the price, with an MIT license you can self-host. For most everyday coding, GLM is now good enough.

That is the short answer to **GLM vs Claude**. The rest is detail: current models, real prices, a benchmark table, and where each one actually wins.

## TL;DR

- **Verdict:** Claude for frontier capability and reliable agents; **GLM-4.7** for cost-sensitive, high-volume coding and on-prem.
- **Price gap is the story:** GLM-4.7 runs about **$0.60 in / $2.20 out** per 1M tokens on Z.ai, versus Claude Opus 5.5 at **$4 / $20** and Sonnet 5.5 at **$2 / $10**. That is roughly **9x cheaper** than Opus on output.
- **License:** GLM-4.7 is **MIT, open weights**. Claude is proprietary, API-only, no weights.
- **The benchmark that matters:** on coding, GLM-4.7 self-reports **73.8%** on SWE-bench Verified; Claude 5.5 leads on Anthropic's own Terminal-Bench 4.0 (**66.4%**) and SWE-bench Pro, but the two labs don't publish the same test, so treat single numbers with care.
- **GLM runs in Claude Code.** Z.ai ships an Anthropic-compatible endpoint, so you can point Claude Code at GLM by changing one environment variable.

## GLM vs Claude: which is better?

**Claude is the stronger model. GLM-4.7 is the better deal.** Both statements are true at once, and which matters depends on your workload.

**GLM** is made by **Z.ai (Zhipu AI)** in Beijing, sometimes called "China's OpenAI." Its current open flagship is **GLM-4.7**, released [December 22, 2025](https://openrouter.ai/z-ai/glm-4.7-20251222?ref=edgewisely.com), a **\~358B-parameter** Mixture-of-Experts model shipped under the **MIT license** on [Hugging Face](https://huggingface.co/zai-org/GLM-4.7?ref=edgewisely.com). It is tuned for coding and agent work.

**Claude** is made by **Anthropic**. The current line is **Claude Opus 5.5** (released September 2026) and **Claude Sonnet 5.5**, both with up to **1M-token** context on the API. Claude is closed: you get an API and apps, never the weights.

So "better" splits cleanly. For the hardest reasoning and the most autonomous agent runs, Claude wins. For getting a large volume of ordinary coding done cheaply, or for running a model on your own hardware, GLM wins.

One moving part to flag: Z.ai has kept shipping. **GLM-5.1** landed in April 2026, also open-weight and MIT. GLM-4.7 remains the model with the fullest public benchmark record, so it anchors this comparison, but the open line is a moving target and the price-and-license argument below carries forward to each new release.

## The comparison table

| Model                 | Maker        | License               | Context      | Price in / out (per 1M) | Headline benchmark (self-reported) | Best for                                         |
| --------------------- | ------------ | --------------------- | ------------ | ----------------------- | ---------------------------------- | ------------------------------------------------ |
| **GLM-4.7**           | Z.ai (Zhipu) | **MIT, open weights** | \~**200K**   | **$0.60 / $2.20**       | SWE-bench Verified **73.8%**       | Cost-sensitive, high-volume coding; self-hosting |
| **Claude Sonnet 5.5** | Anthropic    | Proprietary           | up to **1M** | **$2 / $10**            | AA Intelligence Index **55**       | The default workhorse; balanced price/quality    |
| **Claude Opus 5.5**   | Anthropic    | Proprietary           | up to **1M** | **$4 / $20**            | Terminal-Bench 4.0 **66.4%**       | Hardest reasoning; long autonomous agents        |

Prices: [Z.ai via OpenRouter](https://openrouter.ai/z-ai/glm-4.7-20251222?ref=edgewisely.com) and [Anthropic pricing](https://claude.com/pricing?ref=edgewisely.com). Context and license: the [GLM-4.7 model card](https://huggingface.co/zai-org/GLM-4.7?ref=edgewisely.com). The headline benchmarks are **not the same test** across rows; see the next section.

## Is GLM as good as Claude?

**Close on paper, not identical in practice.** On its own benchmark sheet, GLM-4.7 posts **73.8%** on SWE-bench Verified and **84.9%** on LiveCodeBench-v6, per the [GLM-4.7 model card](https://huggingface.co/zai-org/GLM-4.7?ref=edgewisely.com). Those are frontier-adjacent numbers, and on several reasoning tests GLM-4.7 beats the older Claude Sonnet 4.5 that Z.ai charted against.

The honest catch: **the two labs don't share a scoreboard.** Anthropic doesn't publish SWE-bench Verified for Opus 5.5\. It reports **66.4%** on [Terminal-Bench 4.0](https://www.anthropic.com/news/claude-opus-5-5?ref=edgewisely.com) (self-run, xhigh effort) and strong SWE-bench Pro results. GLM reports Terminal-Bench 2.0, a different, easier version. Comparing a GLM "2.0" number to a Claude "4.0" number tells you nothing.

What benchmarks miss matters more here than the numbers. A single SWE-bench score doesn't capture **agentic reliability** over a 40-step task, how well a model recovers from a bad tool call, or how fast quality degrades at 150K tokens of context. That is where Claude's lead is real and where GLM is merely "good enough."

So: GLM-4.7 is as good as Claude on many discrete tasks, and noticeably behind on the long, messy, autonomous runs.

## How much cheaper is GLM than Claude?

**Roughly 5x to 10x cheaper, depending on which Claude.** This is the clearest, most verifiable gap in the whole comparison.

Per-token, first-party:

- **GLM-4.7 (Z.ai):** $0.60 input / $2.20 output per 1M tokens
- **Claude Sonnet 5.5:** $2 / $10
- **Claude Opus 5.5:** $4 / $20

![Grouped bar chart of first-party API price per 1M tokens: GLM-4.7 $0.60 in / $2.20 out, Claude Sonnet 5.5 $2 / $10, Claude Opus 5.5 $4 / $20](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/11/chart2.png)

Chart: Edgewisely, data from [Z.ai / OpenRouter](https://openrouter.ai/z-ai/glm-4.7-20251222?ref=edgewisely.com) and [Anthropic pricing](https://claude.com/pricing?ref=edgewisely.com) (as of November 2026)

Put real volume through it. Say an agent burns **10M input + 10M output tokens** in a month:

- **GLM-4.7:** \~**$28**
- **Sonnet 5.5:** \~**$120**
- **Opus 5.5:** \~**$240**

That is an **\~8.6x** spread between GLM-4.7 and Opus. Third-party hosts go lower still: DeepInfra serves GLM-4.7 at [$0.40 / $1.75](https://openrouter.ai/z-ai/glm-4.7-20251222?ref=edgewisely.com). For teams running agents around the clock, that difference is a headcount, not a rounding error. If raw API cost is your main axis, see our [cheapest AI API](https://www.edgewisely.com/cheapest-ai-api/) breakdown.

There is also a flat-rate path. Z.ai's **GLM Coding Plan** starts at about **$18/month**, per the [Z.ai developer docs](https://docs.z.ai/devpack?ref=edgewisely.com), with Pro and Max tiers above it and generous per-window quotas. That competes directly with a Claude Pro or Max subscription on price while giving you far more coding throughput.

## Can GLM replace Claude for coding (and in Claude Code)?

**For a lot of work, yes, and you can run it inside Claude Code today.** This is the phenomenon driving the whole "GLM vs Claude for coding" search surge.

Z.ai exposes an **Anthropic-compatible endpoint**. Point Claude Code's base URL and key at Z.ai, and the GLM Coding Plan "can be applied to agent tools such as ZCode, Claude Code, Codex" and others, per the [Z.ai docs](https://docs.z.ai/devpack?ref=edgewisely.com). You keep the Claude Code UX and swap the brain underneath for a model that costs a fraction as much.

What you give up is subtle but real:

- **Long-run reliability.** On a 20-file refactor with 50 tool calls, Opus 5.5 drifts off-task less often. GLM needs more babysitting.
- **Polish.** Claude tends to produce cleaner diffs and better-reasoned PR descriptions out of the box.
- **Ecosystem edge cases.** Some Claude-Code features assume Anthropic-specific behavior and degrade on a swapped backend.

For scaffolding, boilerplate, test generation, bug triage, and routine feature work, GLM-4.7 in Claude Code is a genuine substitute. For the gnarliest autonomous sessions, you'll still reach for Opus. Many teams run both: GLM as the default, Claude for the hard 10%. The same calculus shows up in our [DeepSeek vs Claude](https://www.edgewisely.com/deepseek-vs-claude/) and [Qwen vs Claude](https://www.edgewisely.com/qwen-vs-claude/) comparisons.

## Is GLM open source?

**Yes, and that is a real differentiator.** GLM-4.7 ships under the **MIT license** with open weights on [Hugging Face](https://huggingface.co/zai-org/GLM-4.7?ref=edgewisely.com). MIT is maximally permissive: you can self-host, fine-tune, run it air-gapped, and ship it in a commercial product without a usage fee.

**Claude is the opposite.** No weights, ever. You rent access through Anthropic's API or apps. That is fine for most teams, but it rules Claude out entirely if you have a hard data-residency or on-prem requirement.

That open/closed split is the structural difference under every other comparison on this page.

## Which should you use?

- **Solo dev or indie:** Start with the **GLM Coding Plan** in Claude Code. You get most of the capability at hobby-tier cost. Keep a pay-as-you-go **Claude** key for the occasional hard problem.
- **Platform team at scale:** Run **GLM-4.7** as your default agent model and route the hardest tasks to **Opus 5.5**. The cost delta at volume funds real engineering.
- **Enterprise with compliance needs:** If you must self-host or keep data in-region, **GLM-4.7** is the only one of the two you can actually deploy. If you need vendor support and the top reasoning tier and the cloud is acceptable, **Claude**.

## Our take

GLM-4.7 is the clearest sign yet that open-weight Chinese labs have **collapsed the price of "good-enough frontier coding."** A year ago, choosing an open model over Claude meant accepting a visible capability tax. In late 2026, for most dev work, the gap to Claude is no longer a can-it-do-the-task gap. It is a quality-of-life gap: reliability on long agent runs, cleaner output, a more mature ecosystem. That is a meaningful gap. It is not the same kind of gap, and that distinction changes the build-vs-buy math for anyone running Claude Code at scale.

Start with what the benchmarks actually tell you, which is less than the blogs pretend. A SWE-bench Verified number is a snapshot of single-shot patch success on a curated set. It does not measure whether a model stays coherent across 50 tool calls, recovers gracefully from a failed test, or holds the thread at 150K tokens of context. Those are the behaviors that separate a demo from a dependable agent, and they are exactly where Claude still earns its premium. GLM-4.7 is excellent at the discrete task and merely good at the marathon. When you read "I tested GLM for two weeks and went back to Claude," that marathon reliability is almost always the real reason, even when the writer frames it as raw intelligence.

The Claude-Code-with-GLM move is the most interesting thing happening in this space, because it decouples the tool from the model. You keep the harness developers like and swap in a model that costs 80-90% less. What you trade away is the last slice of reliability and some polish. For a huge amount of day-to-day engineering, that trade is obviously worth it. For the 10% of work that is genuinely hard, autonomous, and expensive to get wrong, it is not, and pretending otherwise is how teams end up debugging an agent instead of their product.

Then there is the dimension the benchmarks ignore entirely: the weights are yours. MIT licensing means GLM-4.7 can run air-gapped, on-prem, or in a specific jurisdiction. For a regulated enterprise, that is not a nice-to-have; it is the whole decision, and it is one Claude cannot answer at any price. The flip side is the trust question that follows every Chinese-origin model around, from data-handling assumptions to supply-chain comfort. If that is a live concern for you, it is worth working through deliberately. We dug into exactly that tension in our [DeepSeek vs Claude](https://www.edgewisely.com/deepseek-vs-claude/) piece, and the same reasoning applies to GLM: self-hosting the open weights is precisely what neutralizes most of the data-residency objection, because nothing leaves your network.

Run the cost math at honest volumes and the picture sharpens. At a few hundred million tokens a month, the difference between GLM-4.7 and Opus 5.5 is five figures a year. That money either disappears into an API bill or funds the engineering that makes GLM's reliability gap smaller: better prompts, better guardrails, better eval harnesses. The smart platform teams are not picking one model. They are routing, defaulting to cheap-and-good and escalating to Claude only when a task clears a complexity bar.

The verdict. **Best for cost-sensitive, high-volume coding and anything that must be self-hosted: GLM-4.7.** **Best for frontier reasoning, long autonomous agents, and teams that want a vendor to call: Claude.** Avoid GLM if your work lives in the hard 10% and reliability failures are expensive. Avoid Claude if you have a hard on-prem requirement or your token volume makes the premium indefensible. For the solo dev, GLM first, Claude on standby. For the platform team, both, with routing. For the enterprise, GLM if you must own the stack, Claude if the cloud is fine and support matters most.

## Frequently Asked Questions

### Is GLM as good as Claude?

On many individual coding and reasoning tasks, yes. GLM-4.7 self-reports **73.8%** on SWE-bench Verified and competitive LiveCodeBench scores. Claude still leads on long, autonomous agent runs, output polish, and reliability under heavy context. Treat them as close on discrete tasks, with Claude ahead on the hardest, messiest work.

### Is GLM cheaper than Claude?

Substantially. GLM-4.7 costs about **$0.60 input / $2.20 output** per 1M tokens on Z.ai, versus Claude Opus 5.5 at **$4 / $20** and Sonnet 5.5 at **$2 / $10**. That is roughly **5x to 10x** cheaper depending on the Claude tier. Z.ai's flat-rate GLM Coding Plan starts near **$18/month**.

### Can GLM run in Claude Code?

Yes. Z.ai provides an Anthropic-compatible API endpoint, so you can point Claude Code at GLM by changing the base URL and key, often a single environment variable. The GLM Coding Plan explicitly supports Claude Code and similar agent tools. You keep the Claude Code experience while paying GLM prices for the underlying model.

### Is GLM open source?

Yes. GLM-4.7 is released under the **MIT license** with open weights on Hugging Face, so you can self-host, fine-tune, and use it commercially without a usage fee. Claude is fully proprietary: API and apps only, with no weights available. For on-prem or data-residency requirements, GLM is the only option of the two.

### Which is better for coding, GLM or Claude?

For most everyday coding, GLM-4.7 is the better value and genuinely capable, especially inside Claude Code at a fraction of the cost. For the hardest autonomous agent work, long refactors, and maximum reliability, Claude Opus 5.5 is better. Many teams default to GLM and escalate the hardest 10% to Claude.

**Editor's note — sources:** Prices are first-party as of November 2026 (Z.ai/OpenRouter for GLM-4.7; Anthropic for Claude) and change often — verify before budgeting. Benchmark figures are each lab's self-reported numbers on different tests (GLM: SWE-bench Verified, LiveCodeBench-v6, Terminal-Bench 2.0 from the model card; Claude: Terminal-Bench 4.0 and SWE-bench Pro from Anthropic), so cross-model benchmark comparisons are directional, not like-for-like. The Artificial Analysis Intelligence Index figure (v5.0.1) is not comparable across index versions. All sources are linked inline at the claim they support.