GLM 5: Every Version from GLM-5 to GLM-5.3-Flash, Priced and Benchmarked

TL;DR

Last updated September 1, 2026. Morph rates come from the live billing table; every external number links to its source at the bottom of the page.

GLM 5 (often written GLM5) is not one model. It is the name Z.ai (Zhipu AI) has used for five releases in 2026: GLM-5.1, GLM-5.2, GLM-5.3, and GLM-5.3-Flash all sit on top of the original GLM-5 from February 11. The four large models share one 753B mixture-of-experts shape with about 40B active parameters per token. Context grew from 200K to 1M at GLM-5.2. Capability grew every release without a new pretraining run after GLM-5.2: GLM-5.3 is scaled post-training on the GLM-5.2 base.

Where it stands against Claude: on the independent Intelligence Index GLM-5.3 scores 60, Claude Opus 4.8 scores 57, Opus 5 scores 63. Z.ai's own table has GLM-5.3 ahead of Opus 4.8 on 12 of 16 benchmarks. Z.ai lists it at $1.40/$4.40 per million tokens against Opus's $5/$25. Morph serves GLM-5.3 as morph-glm53-744b at $1.25/$4.40 and GLM-5.3-Flash as morph-glm53flash at $0.13/$0.45, both with the full 1M context.

GLM 5 Family at a Glance

Every row below is sourced. Z.ai prices are list rates from its pricing docs. Morph prices are the live billing rates for the two versions Morph serves. Benchmarks in the last column are Z.ai-reported unless marked AA (Artificial Analysis, independent).

Parameter counts: Z.ai's docs say 744B for GLM-5 and GLM-5.1, while the Hugging Face checkpoint metadata for both reports 753.9B, the figure Z.ai rounds to 753B for GLM-5.2 and GLM-5.3. The config files for GLM-5 and GLM-5.1 are identical (78 layers, 256 routed experts plus 1 shared, 8 active per token). GLM-5.3-Flash pricing: Z.ai's pricing page shows a promotional $0.075 / $0.25; Artificial Analysis lists $0.15 / $0.50. Morph's morph-glm52-744b id still resolves and routes to the GLM-5.3 stack.

What Changed at Each Version

GLM-5 (February 11, 2026): the base

The only new pretraining run in the family. A 744B-total MoE with 40B active per token, 256 routed experts plus 1 shared with 8 active, and DeepSeek Sparse Attention: a lightweight indexer selects which prior tokens each query attends to, keeping long-context cost sub-quadratic. Pretraining ran 28.5T tokens, up from 23T for the previous generation, with context extended in stages from 32K to 128K to 200K. RL ran on Z.ai's asynchronous "slime" infrastructure. Z.ai reported 77.8 on SWE-bench Verified, 55.1 on SWE-bench Pro, and 56.2 on Terminal-Bench 2.0. Priced at $1.00/$3.20. Technical report: arXiv 2602.15763.

GLM-5.1 (April 7, 2026): long-horizon post-training

Same weights count, same config, new post-training. Z.ai built the release around duration: the model sustains a single task across hundreds of rounds and thousands of tool calls, up to 8 hours in one run. SWE-bench Pro moved 55.1 to 58.4 (the top score at launch, ahead of GPT-5.4 and Claude Opus 4.6), Terminal-Bench 2.0 56.2 to 63.5, CyberGym 48.3 to 68.7. Price rose to $1.40/$4.40, where it has stayed. Full breakdown: GLM-5.1.

GLM-5.2 (June 13, 2026): 1M context

The architectural release. IndexShare reuses one sparse-attention indexer across every four layers, which Z.ai reports cuts per-token FLOPs 2.9x at 1M tokens; that is what made the 200K to 1M jump economical. Multi-token-prediction speculative decoding gained KVShare (reusing the target's KV cache) and rejection sampling, for up to 20% longer draft acceptance. Effort levels arrived, with max as the default. SWE-bench Pro reached 62.1, Terminal-Bench 2.1 reached 81.0, and the Intelligence Index went from 41 to 53. Z.ai also disclosed more reward-hacking behavior than GLM-5.1 and added an anti-hack module. Full breakdown: GLM-5.2 and the GLM-5.2 API guide.

GLM-5.3 (August 14, 2026): scaled post-training, cyber

Same 753B base as GLM-5.2, no new pretraining, a much larger post-training program. Terminal-Bench 3.0 went 4.6 to 28.3, DeepSWE v1.1 46.2 to 66.9, CyberGym 77.2% to 84.5%, ExploitBench 24.4% to 54.4%. The Intelligence Index went 53 to 60, tying Kimi K3 for the open-weights lead. Z.ai says it used the model to find 2,436 vulnerabilities across 269 open-source projects and stood up a disclosure ledger at cvd.z.ai. Weights were held back for safety hardening and landed on Hugging Face on August 25 under the custom GLM-5.3 License. Full breakdown: GLM-5.3.

GLM-5.3-Flash (August 26, 2026): the small sibling

A different network: 320B total, 18B active, with vision input the large models lack, 1M context, MIT license. Artificial Analysis scores it 57 on the Intelligence Index, three points under GLM-5.3 and level with Claude Opus 4.8, at about a tenth of GLM-5.3's price. Morph serves it as morph-glm53flash. Full breakdown: GLM-5.3-Flash.

Terminal-Bench versions are not comparable to each other: 3.0 is a harder revision than 2.1, which is why GLM-5.2 shows 81.0 on 2.1 and 4.6 on 3.0. GLM-5 and GLM-5.1 report Terminal-Bench 2.0; Z.ai's GLM-5.2 docs list GLM-5.1 at 62.0 on 2.1.

GLM 5 vs Claude Opus

The comparison people run is GLM-5.3 against Claude Opus 4.8, because the two land on the same tier and differ 5x on output price. Opus 5 replaced Opus 4.8 at the same $5/$25 in Anthropic's lineup and scores higher, so the honest frame is: GLM-5.3 beats the Opus that was current when it shipped and trails the one that is current now. The earlier rounds of this fight are on GLM-5.2 vs Claude Opus and GLM-5.3 vs Claude.

Eval output tokens is the total Artificial Analysis's harness consumed running its Intelligence Index suite; higher means a more verbose model. AA lists Opus 4.8 as deprecated in favor of Opus 5.

Z.ai's benchmark table, GLM-5.3 vs Opus 4.8

Vendor-reported, from the GLM-5.3 model card. GLM-5.3 leads 12 of 16 rows; the three it loses are repo-scale generation (NL2Repo), the long-horizon SWE-Marathon, and Toolathlon.

Cost per task, not cost per token

The sticker gap is 3.6x on input and 5.7x on output at Z.ai list, or 4x and 5.7x on Morph. Verbosity eats part of it. GLM-5.2 burned about 43,000 output tokens per Intelligence Index task at max effort, 37,000 of them reasoning, against roughly 16,000 for GPT-5.5. GLM-5.3 kept the habit: 170M output tokens across the AA suite against Opus 4.8's 120M and Fable 5's 83M, and AA measures $0.68 per Index task for it.

Z.ai's own harness tells the same story from the other side. On its internal Code Bench, GLM-5.3 at high effort scores 31.4% using about 50K output tokens per task, while Opus 4.8 scores 29.5% using 120K. Take those token counts at list price and the output bill per task is $0.22 on GLM-5.3 against $3.00 on Opus 4.8. Take AA's default-effort measurement instead and GLM-5.3 spends 1.4x the tokens Opus 4.8 does, so its per-task cost advantage shrinks from 5.7x to roughly 4x. Either way it is cheaper; the size of the gap depends on the effort setting.

Serving GLM 5: Throughput and Caching

Open weights mean the same model runs on many hosts, and the hosts are not interchangeable. Artificial Analysis measures GLM-5.3 across providers with a 6.7x spread in output speed and a 2.5x spread in blended price for identical weights.

Morph's 80 tok/s is from the GLM-5.3 744B row of the private-deployment chart on Morph Models, measured with speculators trained on coding traffic; public endpoints run on shared capacity. Artificial Analysis does not measure Morph. GLM-5.3-Flash measures 42.5 tok/s on Z.ai's API.

Why the same weights run at different speeds

A 753B MoE at batch 1 is memory-bound: each token reads every active expert's weights once and does almost no arithmetic per byte, so tokens per second is bandwidth divided by bytes touched. The levers are the ones speculative decoding and quantization pull. GLM-5.2 and GLM-5.3 ship a multi-token-prediction draft head with KVShare, and Z.ai reports up to 20% longer acceptance from its rejection-sampling change. A draft trained on the target's own coding output accepts more tokens per step than a generic one, which is why Morph trains one speculator per model on coding traces. On the memory side, Morph serves GLM-5.3 with NVFP4 expert weights and an FP8 KV cache at tensor-parallel 8; the NVFP4 checkpoint is about 465 GB against roughly 1.5 TB for BF16.

Prefix caching

Every open model on Morph has automatic prefix caching with no cache-write surcharge. Cached input bills at $0.26/M on GLM-5.3 (about 80% off) and $0.02/M on GLM-5.3-Flash (about 85% off). Agent loops get this for free: each turn re-sends the previous turns verbatim, so everything but the newest turn hits. Two controls: prompt_cache_key pins a conversation to the worker holding its prefix, and cache_ttl sets retention from 5 minutes to 24 hours. Details in the caching docs.

Standby: send service_tier: "standby" and pay half; the request runs on spare capacity and sheds a fast 429 when a region is busy, billing nothing on rejection. The Batch API bills at the same rate with a 24-hour window. See the standby docs.

GLM 5 API: Morph and Z.ai

Both endpoints are OpenAI-compatible. Switching hosts is a base URL and model name change. On Morph the model ids are morph-glm53-744b for GLM-5.3 and morph-glm53flash for GLM-5.3-Flash; one key works for every model in the lineup.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.morphllm.com/v1",
    api_key="YOUR_MORPH_API_KEY",
)

resp = client.chat.completions.create(
    model="morph-glm53-744b",  # or "morph-glm53flash"
    messages=[
        {"role": "user", "content": "Find the race condition in this queue and fix it."},
    ],
)
print(resp.choices[0].message.content)

Claude Code

Morph serves the Anthropic Messages API at /v1/messages for every open model, so Claude Code runs on GLM-5.3 with env vars and no proxy. ANTHROPIC_MODEL remaps sonnet and opus; ANTHROPIC_SMALL_FAST_MODEL remaps the haiku background calls.

export ANTHROPIC_BASE_URL="https://api.morphllm.com"
export ANTHROPIC_AUTH_TOKEN="YOUR_MORPH_API_KEY"
export ANTHROPIC_MODEL="morph-glm53-744b"
export ANTHROPIC_SMALL_FAST_MODEL="morph-glm53flash"
claude

Use /effort high rather than max for routine work to keep the reasoning-token bill down. Full setup, including the settings.json form, in the Claude Code guide.

Cline, OpenCode, Cursor

Set the OpenAI-compatible provider base URL to https://api.morphllm.com/v1, the model to morph-glm53-744b, and the context window to 1000000. Leave image support unchecked for GLM-5.3; enable it for morph-glm53flash, which accepts images.

Z.ai first-party

Base URL https://api.z.ai/api/paas/v4, model glm-5.3 (or glm-5.3-flash), at $1.40/$4.40 per million tokens. Z.ai's Anthropic-format endpoint for Claude Code is https://api.z.ai/api/anthropic. Older ids glm-5.2, glm-5.1, and glm-5 remain callable on Z.ai; on Morph the older morph-glm52-744b id resolves to the GLM-5.3 stack.

GLM 5: Pros and Cons

FAQ

What is GLM 5?

Z.ai's family of open-weight coding models: GLM-5 (February 11, 2026), GLM-5.1 (April 7), GLM-5.2 (June 13), GLM-5.3 (August 14), and GLM-5.3-Flash (August 26). The large models are 753B MoEs with about 40B active per token; Flash is 320B/18B with vision. Also written GLM5 or GLM-5.

Is GLM 5 open source?

Open weights. GLM-5, 5.1, 5.2, and 5.3-Flash are MIT on Hugging Face. GLM-5.3 is public since August 25 under the custom GLM-5.3 License, which is MIT-like plus a security-review requirement for model-as-a-service operators above $10B in revenue. Training data and code are not released.

Is GLM 5 better than Claude?

GLM-5.3 scores 60 on the independent Intelligence Index against Opus 4.8's 57, Opus 5's 63, and Fable 5's 62, and leads Opus 4.8 on 12 of 16 rows of Z.ai's own table. So: above the Opus that was current at launch, below the current Anthropic top tier, at a fifth of Opus's output price and with no vision. Details on GLM-5.3 vs Claude.

How much does GLM 5 cost?

Z.ai list: $1.40/$4.40 per million tokens for GLM-5.1 through GLM-5.3, $1.00/$3.20 for the original GLM-5, $0.15/$0.50 for Flash. Morph: $1.25/$4.40 for GLM-5.3 and $0.13/$0.45 for GLM-5.3-Flash, half that on standby or batch.

How do I call the GLM 5 API?

OpenAI-compatible on both hosts. Morph: https://api.morphllm.com/v1 with morph-glm53-744b or morph-glm53flash. Z.ai: https://api.z.ai/api/paas/v4 with glm-5.3. Claude Code works against either through the Anthropic Messages API.

What is GLM 5's context window?

200K tokens on GLM-5 and GLM-5.1, 1M on GLM-5.2, GLM-5.3, and GLM-5.3-Flash, with 128K max output. Morph serves the full 1M on both models it hosts.