Tier A — Frontier
Runs in:CNMade in:China
Z.ai (GLM / Zhipu)

GLM-4.6

Tier A — Frontier · 205K tokens

Tokonomix Editorial Team·Reviewed by Mes Kalkan··

GLM-4.6 is an established model in Zhipu’s GLM-4 line, built around a large context window and a focus on coding and agentic tool use. It offers most of GLM-4.7’s practical value and is one of the more mature GLM chat models.

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 latency102 runs
911855816206238533150008-1509-10ms
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.

49%
Coding
judge mean 75
1%
Creative
judge mean 29
36%
Factual
judge mean 58
5%
Reasoning
judge mean 24

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-4.6
$0.6000 per 1M input tokens
$2.20 per 1M output tokens
≈ $0.0008 per typical conversation (800 tokens)
Input vs output price (per 1M tokens)
per 1M input tokens$0.6000
per 1M output tokens$2.20

Pricing over time

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

$0.6000

input / 1M

— stable

$2.20

output / 1M

— stable

2026-07-122026-08-092026-09-06
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)21 / avg 71
2185

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

Availability

No measurements yet

We haven't recorded enough API calls to show availability stats for this model. Data appears once the model starts receiving live traffic.

Section 07

Tokonomix benchmark verdicts

⚖️
Endorsed by 1 judge
Independent LLM judges evaluated this model on our weekly intelligence tests
claude-sonnet-4-552/100 · 26 runs
11 correct1 partial14 wrong42% accuracy
● 2026-09-06

GLM-4.6 adds reasoning, tools, and JSON capabilities without benchmark scores

GLM-4.6 by Zhipu has expanded its technical capabilities in this window by adding structured JSON output support, function calling tools, and reasoning modes. These additions mark a meaningful evolution in the model's feature set, bringing it closer to parity with other contemporary language models that offer similar functionality. However, the model continues to operate without any publicly available benchmark scores across standard evaluation frameworks. The absence of performance data on metrics like MMLU, HumanEval, or other industry-standard tests makes it impossible to assess the model's actual capabilities relative to competitors. While the addition of reasoning capabilities suggests potential for complex problem-solving tasks, and tools integration indicates readiness for agentic workflows, users have no objective basis for understanding how well these features perform in practice. The lack of benchmark transparency remains a significant limitation for enterprises and developers who rely on quantitative assessments for model selection. Organizations considering GLM-4.6 will need to conduct their own extensive testing to evaluate whether the model meets their requirements, as no comparative performance data is available to inform deployment decisions.

Quality

—

Latency p50

—

Test runs

0

✓ Added reasoning capability✓ Tools and JSON support added✗ No benchmark scores available✗ Performance remains unverified
Section 08

Full model profile

GLM-4.6: long-context, coding-focused GLM

GLM-4.6 is an established model in Zhipu’s GLM-4 line, built around a large context window and a focus on coding and agentic tool use. It offers most of GLM-4.7’s practical value and is one of the more mature GLM chat models.

z.ai publishes GLM-4.6 at $0.60 per 1M input tokens and $2.20 per 1M output tokens, the same value tier as GLM-4.7.

It advertises a large ~200K-token context window — a defining feature of GLM-4.6 for long-file and repository-scale work.

Architecture & training signals

GLM-4.6 is part of the GLM-4 line from Zhipu AI, which earned attention for capable open-weight models tuned for coding and agentic tasks and for long context. It is a reasoning-capable chat model with tool-calling and 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

  • Long-context work — its large window is the headline feature.
  • Coding and agentic tool use.
  • A cost-effective, mature GLM option.

Where it falls short

  • GLM-4.7 and the GLM-5 generation may edge it on newer capabilities.
  • No Tokonomix benchmark scores yet; validate on your workload.
  • Non-EU hosting.

Real-world use cases

  • Repository-scale code understanding and generation.
  • Long-document analysis where the full text must fit in one call.
  • Agentic pipelines needing reliable tool-calls.

Tokonomix benchmark snapshot

GLM-4.6 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-4.6 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-4.6 is a dependable, long-context, coding-focused GLM at a value price. If you want the newest iteration, GLM-4.7; if you want the newest generation entirely, GLM-5.x. For free experimentation, pair it with the GLM Flash tiers.

Last automated test
Sep 10, 2026 · 08:00 UTC · Speed benchmark
P50 latency
9323 ms
P95 latency
11379 ms
Errors
0 / 6 runs
Last reviewed by Tokonomix Team·July 8, 2026