Tier A — Frontier

Runs in:CNMade in:China

GLM-5

Tier A — Frontier · 205K tokens

Tokonomix Editorial Team·Reviewed by Mes Kalkan·Published July 6, 2026·Last reviewed July 8, 2026

GLM-5 is the base model of Zhipu’s GLM-5 generation on z.ai — the generation’s workhorse, priced below the GLM-5.2 flagship. It is an OpenAI-compatible reasoning chat model with tool and JSON support.

Section 01

Speed analysis

Latency measured across all benchmark runs. P50 (median) and P95 (95th percentile) give a realistic picture of response speed under normal and peak load.

P50 latency (median)P95 latency104 runs

Section 02

Quality scores

How this model compares to the rest of the field on each prompt category, from a pairwise fit over the same prompts. The raw judge score sits underneath each number.

57%

Coding

judge mean 93

89%

Creative

judge mean 98

64%

Factual

judge mean 91

62%

Reasoning

judge mean 98

Win rate per category: how often this model beats a field-average model on a prompt from that category. 50% is average, not a failing grade. It is not a percentage of correct answers.

Section 03

Pricing history

Direct provider rates per million tokens, plus a typical-conversation cost estimate.

💰

API rates — GLM-5

$1.00 per 1M input tokens

$3.20 per 1M output tokens

≈ $0.0012 per typical conversation (800 tokens)

Input vs output price (per 1M tokens)

per 1M input tokens$1.00

per 1M output tokens$3.20

Pricing over time

Input & output per 1M tokens · step-line = price changes

$1.00

input / 1M

— stable

$3.20

output / 1M

— stable

2026-07-122026-08-162026-09-06

Input

Output

Price change

⟳ synced weekly

Section 04

Tokens per second

Throughput in tokens per second, derived from measured P50 latency. Higher is better; fluctuations track provider-side load.

Throughput (tokens / s)88 / avg 93

Estimated from P50 latency × 200 output tokens — the absolute number depends on this assumption; the trend is what matters.

Section 05

Capabilities

jsonnotes: GLM emits a non-standard reasoning_content field beside content; read content for the answer.toolsreasoning

Section 06

Availability

How often this model answers when we call it — measured across real API requests and live tests over the last 30 days. This is separate from quality: these numbers only tell you whether the model responds, not how good the answer is.

Last 7 days

—

Last 30 days

33.3%

n=6

Median response time

91,708ms

n=2

Based on 385 measurements over the last 30 days.

Technical details

Only live API calls and live-test requests count — internal probes and benchmark runs are excluded.

Calls with a custom API key (BYOK) are excluded: those failures are key-specific, not a sign of model downtime.

Failed calls are NOT included in quality scores — quality is measured on successful responses only. Availability and quality are independent signals.

Median response time (p50) across successful calls with a recorded duration. Outliers (very slow or very fast calls) pull the median less than the average.

Total calls (30d)

OK responses (30d)

Total calls (7d)

OK responses (7d)

Section 07

Tokonomix benchmark verdicts

⚖️

Endorsed by 1 judge

Independent LLM judges evaluated this model on our weekly intelligence tests

claude-sonnet-4-594/100 · 28 runs

26 correct0 partial2 wrong93% accuracy

● 2026-09-06

GLM-5 shows slight quality decline with increased latency

GLM-5 has returned with limited benchmark data, showing a modest decline from previous performance levels. The overall quality score dropped from 97.0 to 96.0, while latency increased by 18% from 7051ms to 8287ms at the median. The current testing window includes only two test runs, compared to three in the previous period, which limits the statistical confidence of these results. Coding performance specifically declined from a perfect 100 to 96, though this remains a strong absolute score. Notably absent from the current window are factual and reasoning category results, which previously showed excellent performance at 100 and 91 respectively. This makes it difficult to assess whether the model has changed in these areas or if testing coverage has simply narrowed. The reduced test run count and missing category data suggest either limited evaluation activity or a more focused testing approach. Users should note that while the current scores remain competitive, the directional trend shows degradation across both performance and speed metrics. The model continues to demonstrate strong coding capabilities, but the incomplete picture from this benchmark window makes it challenging to draw definitive conclusions about overall capability changes.

Quality

96.0

Latency p50

8,287 ms

Test runs

✗ Quality score decreased to 96✗ Latency increased 18%✗ Coding score dropped from 100✗ Limited category coverage

Section 08

Full model profile

GLM-5: the base of Zhipu’s newest generation

z.ai publishes GLM-5 at $1.00 per 1M input tokens and $3.20 per 1M output tokens — cheaper than the GLM-5.2 flagship while staying in the same generation.

We registered it with a large (~200K-token) context window as a provisional figure; GLM-5-generation documentation is thin, so confirm the exact window on z.ai before relying on it.

Architecture & training signals

GLM-5 is the base of Zhipu AI’s GLM-5 generation. Public technical detail is still limited as of July 2026; we treat it as a large reasoning-oriented chat model with tool-calling and structured JSON over an OpenAI-compatible endpoint. Like the rest of the GLM line, it returns a non-standard reasoning_content field alongside content in its OpenAI-compatible responses; integrations should read content for the final answer and treat reasoning_content as an optional trace.

Where it shines

A balanced quality-vs-price point within the newest GLM generation.
Long-context reasoning and analysis.
Cross-family diversity in a consensus panel.

Where it falls short

Still pricier than the GLM-4.x line and the free flash tiers.
Limited reproducible benchmark data; verify on your workload.
Non-EU hosting.

Real-world use cases

General reasoning where you want current-generation GLM quality without the flagship price.
Document analysis and summarisation.
Agentic tool-use pipelines.

Tokonomix benchmark snapshot

GLM-5 is newly registered on Tokonomix and not yet activated, so we have not run it through our weekly intelligence test or speed benchmark. There are no Tokonomix scores to report yet — and we will not invent any.

When it goes live, it enters the same weekly harness as every other model: identical prompts, an independent cross-family judge, and reproducible latency and cost measurements. Until then, treat the pricing and capability notes on this page as the vendor-published starting point, not as measured Tokonomix results.

EU privacy & data residency

GLM-5 is built by Zhipu AI (z.ai), a China-headquartered lab, and is served from non-EU infrastructure. This is important to state plainly: routing a prompt to this model is not an EU-data-residency or GDPR-sovereign choice, and Tokonomix will never tag it as one.

If your use case requires data to stay within the EU, pick a model whose provider is EU-hosted (for example our OVH or Azure-EU routes) rather than a GLM model. Tokonomix keeps z.ai out of every EU-only / sovereign routing set by design. Use GLM where its capability or price is the priority and cross-border processing is acceptable for that workload.

Verdict & alternatives

GLM-5 is the sensible default within the GLM-5 generation: most of the newest-generation quality at a lower price than GLM-5.2. If you need the absolute top, go GLM-5.2; if budget dominates, drop to GLM-4.6/4.5 or the free flash models.

Last automated test

Sep 6, 2026 · 20:05 UTC · Speed benchmark

P50 latency

2271 ms

P95 latency

2402 ms

Errors

0 / 6 runs

Last reviewed by Tokonomix Team·July 8, 2026