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

GLM-4.7

Tier A — Frontier · 205K tokens

Tokonomix Editorial Team·Reviewed by Mes Kalkan··

GLM-4.7 is the most recent model in Zhipu’s well-established GLM-4 line, positioned as a strong general-purpose and coding/agentic model at a mid-market price. For many workloads it is the sweet spot of the GLM family — cheaper than the GLM-5 generation but still capable.

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 latency56 runs
1711915816606240533150007-0807-22ms
Section 02

Quality scores

Evaluation results from judge-model scoring across diverse task categories. Scores reflect coherence, accuracy and instruction-following.

100
Coding
95
Creative
Section 03

Pricing history

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

💰
API rates — GLM-4.7
$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

2026-07-122026-07-192026-07-19
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)30 / avg 40
1165

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

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

0.0%

n=1

Last 30 days

50.0%

n=2

Median response time

5,300ms

n=1

Based on 173 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)

2

OK responses (30d)

1

Total calls (7d)

1

OK responses (7d)

0

Section 07

Tokonomix benchmark verdicts

⚖️
Endorsed by 1 judge
Independent LLM judges evaluated this model on our weekly intelligence tests
claude-sonnet-4-599/100 · 6 runs
6 correct0 partial0 wrong100% accuracy
● 2026-07-19

GLM-4.7 adds tool use and reasoning; maintains coding strengths

GLM-4.7 has expanded its capabilities with the addition of JSON formatting, function calling tools, and reasoning features while preserving its established strengths in coding tasks. The model continues to demonstrate competitive performance in programming contexts, maintaining its position as a capable coding assistant. Its multilingual capabilities remain stable, particularly for Chinese language tasks where it shows consistent results. The new tool-calling functionality opens pathways for agentic applications and structured output generation, though real-world performance metrics for these features await broader testing. The reasoning capability addition suggests enhanced problem-solving potential, aligning with industry trends toward more deliberative AI responses. Users should note that while the model's core competencies in code generation and multilingual support remain unchanged, the expanded feature set positions it for more diverse use cases beyond pure text generation. The model's practical utility now extends to scenarios requiring structured data handling and multi-step task execution, though developers should evaluate these newer capabilities against their specific requirements before deployment in production environments.

Quality

—

Latency p50

—

Test runs

0

✓ Added tool calling support✓ New reasoning capabilities✓ JSON formatting now available
Section 08

Full model profile

GLM-4.7: the value workhorse of the GLM-4 line

GLM-4.7 is the most recent model in Zhipu’s well-established GLM-4 line, positioned as a strong general-purpose and coding/agentic model at a mid-market price. For many workloads it is the sweet spot of the GLM family — cheaper than the GLM-5 generation but still capable.

z.ai publishes GLM-4.7 at $0.60 per 1M input tokens and $2.20 per 1M output tokens — a mid-market price well below the GLM-5 generation.

It advertises a large context window (~200K tokens on our registration), suited to long files and multi-step agent runs.

Architecture & training signals

GLM-4.7 continues Zhipu AI’s GLM-4 line, which has been notable for capable open-weight releases with a focus on coding and agentic tool use. It is a reasoning-capable 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

  • Coding and agentic tool-use — a traditional strength of the GLM-4 line.
  • Strong quality-per-euro; a good default when GLM-5 pricing is overkill.
  • Long-context tasks within its ~200K window.

Where it falls short

  • Not positioned as an absolute frontier model — for the hardest reasoning, GLM-5.2 or a US frontier model may edge it.
  • Independent Tokonomix benchmark data is not in yet; verify on your tasks.
  • Non-EU hosting.

Real-world use cases

  • Coding assistants, refactors and agentic build loops.
  • General drafting, analysis and summarisation at scale.
  • A cost-effective, decorrelated consensus proposer.

Tokonomix benchmark snapshot

GLM-4.7 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-4.7 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-4.7 is the GLM family’s value workhorse: capable, coding-friendly and priced for volume. Reach for GLM-5.x only when you specifically need the newest generation’s reasoning; for zero-cost work, the GLM-4.7 Flash free tier is the natural companion.

Last automated test
Jul 22, 2026 · 02:04 UTC · Speed benchmark
P50 latency
6713 ms
P95 latency
30000 ms
Errors
1 / 6 runs
Last reviewed by Tokonomix Team·July 8, 2026