Zhipu AI (BigModel) Review: Pricing & Comparison

A large-model company that grew out of Tsinghua

Zhipu AI (bigmodel.cn) grew out of Tsinghua University and is one of the earliest teams in China to build large language models. The GLM (General Language Model) series is their core technical asset, with substantial recognition and a user base in China’s AI community — one of the few names, alongside DeepSeek and Qwen, that keeps coming up in conversations about the domestic large-model first tier.

Calling the GLM series through Zhipu AI’s official API is fundamentally different from going through a relay: you’re dealing directly with the model’s developer — no intermediary layer, lower latency, model versions update in sync with Zhipu’s releases, and if something breaks there’s official technical support behind it rather than a relay’s second-hand troubleshooting.

Official API vs. relay: why GLM is worth connecting to directly

Most other entries in this review discuss “relays” — third-party services that bulk-buy official accounts and resell access. Zhipu AI is entirely different: it is the model developer, and bigmodel.cn is its official platform — there’s no middleman taking a cut.

Choosing an official direct connection over a relay for the GLM series has a few concrete upsides: better account security, since you’re not trusting a third party’s custody of an official account, with no risk of getting suspended by association due to a relay’s policy violations; more direct technical support, since issues can be raised through official tickets or community channels rather than relayed secondhand through a relay’s support team; and faster access to new models — when a new flagship like GLM-5 ships, the official platform can call it immediately, without waiting for a relay to finish integration and testing.

Of course, a direct official connection has its own limits — it can’t, like a relay, let you call Claude, GPT, and other vendors’ models through the same account. That’s why many developers keep both an official account (for the GLM series) and a relay account (for other vendors’ models) simultaneously. This “official direct connection for your core model + a relay for supplementary coverage” combination strategy is quite common in the domestic developer community — it secures stability and technical support for your core business model while preserving flexibility to switch to other models via a relay.

GLM-5: February 2026’s flagship leap

Zhipu AI released its next-generation flagship model, GLM-5, on February 12, 2026 — a significant version jump from the GLM-4 series. Alongside the capability improvements came a sizable price adjustment: API pricing rose roughly 67% to 100% over the GLM-4 series, and Coding subscription plan pricing rose roughly 30% to 60% in tandem.

This price increase sparked notable discussion in the domestic large-model community — the core debate being whether GLM-5’s capability gains actually match the price increase. From a product logic standpoint, this “new flagship launch + price increase” pattern isn’t unusual — the cost advantages accumulated by the previous generation tend to get repriced along with the rising compute and training investment behind a new model. Users need to factor this repricing into their budget planning rather than carrying over price assumptions from the GLM-4 era.

Price table: GLM-5 pricing in detail

Per the latest 2026 information, GLM-5 is billed in tiers based on context length:

Tier	Input price (per M tokens)	Output price (per M tokens)
0-32K context	~¥4	~¥18
32K+ context	~¥6	~¥22
Cached input	Currently free	—

Free cached input is a detail worth paying attention to — if your application has a lot of repeated system prompts or reference documents (long-document Q&A, multi-turn conversations that repeatedly reference the same background material), making good use of the caching mechanism can meaningfully reduce your actual spend — a cost-optimization angle Zhipu left developers even while raising prices elsewhere.

For comparison, some reviews note GLM-5’s pricing strategy is “nearly 40% cheaper than the equivalent GPT-4.1 tier,” with cost-effectiveness several times better than the previous-generation GLM-4-Plus — cross-vendor, cross-version cost-effectiveness comparisons like this should be read cautiously, since whether they actually hold depends on task type and real token-consumption patterns. We’d recommend benchmarking against your own real workload rather than taking a single review’s conclusion at face value.

Industry context: a correction in the domestic price war

Understanding GLM-5’s price increase also means placing it in the context of the domestic large-model industry’s overall price trend. Over the past two or three years, China’s large-model market went through an intense price war — DeepSeek, Qwen, Zhipu and others each took turns claiming “lowest price on the market” or “best value for money,” pushing API prices steadily down, to the point some small-parameter models became permanently free. That price war benefited developers in the short term, but it also compressed vendors’ margins — especially for flagship models with high training and inference costs, competing on low price for market share long-term isn’t sustainable.

GLM-5’s fairly sizable price increase can, in a sense, be read as a “correction” following the domestic large-model price war — vendors resetting the balance between price and cost around a new flagship. Industry-watching write-ups note this price increase isn’t unique to Zhipu — multiple domestic large-model vendors raised prices to varying degrees in 2026, reflecting the industry’s broader shift from “burning cash for market share” toward “pursuing sustainable profitability.” For developers, this means the assumption that “domestic large models are always cheaper than international ones” needs updating — whether a price advantage actually holds for a specific model at a specific point in time needs to be re-verified rather than assumed from old pricing memory, especially when doing cross-year budget planning or cost comparisons, where live pricing matters more than historical impressions.

Coding subscription plans: another way developers pay

Beyond token-based API billing, Zhipu AI also offers a developer-focused Coding subscription plan — a monthly subscription that gives unlimited coding-scenario model calls within an allotment. The subscription currently supports GLM-5.1, GLM-5-Turbo, GLM-4.7, and GLM-4.5-Air, letting users switch between these models based on task complexity without per-call billing.

After the GLM-5 launch in February 2026, Coding subscription pricing rose roughly 30% to 60% in tandem, with the exact increase varying by plan tier. For developers who’ve already integrated the GLM series into their daily development flow (e.g. calling it through an AI coding tool), this price increase means monthly budgets need re-evaluating; for new users still on the fence, we’d suggest testing real-world usage with the free tier or the lowest-tier plan first before committing to a long-term subscription.

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="your Zhipu API key")

response = client.chat.completions.create(
    model="glm-4-flash",
    messages=[
        {"role": "user", "content": "Write a market analysis report outline in Chinese, topic: the AI coding tools market"}
    ]
)
print(response.choices[0].message.content)

Zhipu AI also offers an OpenAI-compatible interface, so you can connect with the OpenAI SDK:

from openai import OpenAI

client = OpenAI(
    api_key="your Zhipu API key",
    base_url="https://open.bigmodel.cn/api/paas/v4/"
)

The GLM series: a moat built on Chinese-language capability

Zhipu AI’s GLM series is specifically optimized along a few dimensions:

Long Chinese-document understanding: the GLM series supports very long context, suited to analyzing and summarizing large blocks of Chinese text — legal documents, research reports, product documentation — an area where many models trained mainly on English corpora are relatively weaker.

Chinese instruction-following: GLM models are specifically trained on the subtleties of Chinese language context (honorifics, simplified/traditional characters, internet slang, industry terminology), producing output that reads more naturally than a directly-translated English model, with less of a “translated” feel.

Tool calling: GLM-5 supports function calling, so you can build agent applications that call external tools; paired with Zhipu’s Coding subscription, it’s well suited to building Chinese-facing assistants or automated workflows — part of why several domestic agent products choose the GLM series as their underlying model.

Multimodal: one account for text, image, and video

Zhipu AI has multiple multimodal models, all managed under bigmodel.cn:

• CogView: image generation, with an understanding of Chinese-language descriptions that often fits Chinese context and culture better than many internationally common image generation models
• CogVideo: video generation from Chinese-language scene descriptions, suited to content creation and marketing-asset production
• GLM-4V: image-text understanding, able to analyze image content and perform OCR, well suited to applications needing image input understanding

For teams that need to integrate multiple AI capabilities into a product but want to manage everything under one account, this integration is a real advantage for Zhipu AI — no need to maintain accounts and bills across separate image-generation, video-generation, and text-generation platforms simultaneously. A single bigmodel.cn account covers most common needs, substantially simplifying vendor management and cost accounting.

Still worth it after the price increase?

The GLM-5 price increase is the most significant change in this review update, worth analyzing on its own. Before the increase, one of the GLM series’ big draws for domestic developers was “solid capability, friendly pricing” — after the increase, that label needs re-examining.

On the positive side: GLM-5 as the new flagship does show real capability improvements over the GLM-4 series, and some reviews still find its pricing below the equivalent GPT-4.1 tier, with free cached input leaving room for optimization for heavy users; on the cautious side: the increase is sizable (67%-100%), and if your application is highly cost-sensitive and doesn’t need GLM-5’s capability improvements, sticking with GLM-4.5-Air (whose cost-effectiveness hasn’t changed) or another vendor’s open-source model (DeepSeek, Qwen) may be the more economical choice.

Advice for budget-sensitive users: test the free tier first to see how much GLM-5 actually improves over GLM-4.5-Air for your use case — if the improvement isn’t significant, there’s no need to pay for the pricier flagship; if your use case genuinely needs GLM-5’s reasoning or long-context capability, the post-increase price is still reasonably competitive against other closed-source flagships and worth accepting. The key question isn’t “is it expensive” — it’s “does the capability gain from this increase actually address a real pain point in your workload.”

Three typical use cases

Scenario 1: A Chinese content-production platform. A team producing self-media content, marketing copy, and e-commerce product pages whose core need is natural, culturally fluent Chinese output — the GLM series’ Chinese-language optimization is usually less work than calling an international model and doing a second pass of localization, with less “translated” feel in the output.

Scenario 2: A legal or research team handling long documents. Law firms, investment research shops, and policy research departments frequently process tens or hundreds of pages of Chinese text. GLM’s long-context capability, paired with its Chinese-corpus training advantage, is well suited to contract review, research-report summarization, and policy-document interpretation — typically producing more coherent results than chopping documents into chunks and feeding them to an English model.

Scenario 3: A multimodal content-production workflow. A content team that needs text, images, and short-video assets simultaneously can use CogView for imagery, CogVideo for short video, and GLM-4V for reprocessing and classifying existing image assets — all under the same bigmodel.cn account and billing system, which fits domestic teams’ payment habits and unified-management needs far better than piecing together accounts across Midjourney, Runway, and other overseas platforms, and avoids the hassle of repeatedly connecting overseas payment channels.

A few things worth doing before you commit

Before migrating a workload onto GLM-5 or relying on the Zhipu API, a few preparation steps worth doing:

1. Run a comparison test with the free tier: run GLM-5 and whatever model you currently use (whether GLM-4.5-Air or another vendor’s) against the same test cases and quantify the difference in output quality and actual token consumption
2. Recalculate your monthly budget under the new prices: re-estimate your actual usage volume’s monthly cost under the new price table rather than carrying over pre-increase budget assumptions
3. Check whether caching fits your use case: if your application has a lot of repeated system prompts or reference material, evaluate how much cached-input support could save
4. Watch the official announcements page: pricing, model versions, and promotions can all change at any time — subscribing to official update notifications is more reliable than relying on static numbers from older material
5. Assess whether you need to keep a relay account too: if your business needs to call multiple vendors’ models side by side, plan ahead for the division of labor between the GLM series and other vendors’ models, so you’re not scrambling to add integration at the last minute and delaying launch

Best fit

Zhipu AI is a good fit for:

• Applications needing high-quality Chinese content generation (content creation, copywriting, translation)
• Chinese-document analysis needs (legal, financial, healthcare)
• Products needing integrated multimodal capability (text + image + video)
• Teams that want to use and support the domestic large-model ecosystem

Less of a fit for:

• Developers primarily using Claude Fable 5 or GPT-5.5 with no budget constraints
• Use cases sensitive to a model’s ranking on international English-language benchmarks
• Extremely price-sensitive users looking for alternatives after the GLM-5 price increase

Data security and enterprise compliance

As a company with Tsinghua roots that’s operated in China for years, Zhipu AI is generally more rigorous on data-handling transparency than many relays with limited public information — an official open platform typically has clear terms of service and a privacy policy explaining whether data is used for model training and what the log-retention policy is. For enterprise customers, these details are still worth confirming line-by-line before a formal contract or large-scale integration, especially for applications involving customer privacy data or trade secrets — contact Zhipu AI’s enterprise sales or business team directly for more detailed data-handling documentation and, if needed, supplementary contract terms, rather than assuming all compliance requirements are automatically met just because it’s a well-known official vendor.

Compared to many relays with opaque identities, Zhipu AI, with a real corporate entity behind it and relatively complete policy documentation, tends to clear internal compliance review more easily in enterprise procurement processes — part of why many domestic enterprises and government-affiliated organizations prioritize official channels from Zhipu, Baidu, Alibaba, and similar vendors with a clear corporate entity when selecting a domestic large model. These customers tend to care more about “who is the vendor, and can they be held accountable” than about price sensitivity alone.

Head-to-head comparisons

• vs. Moonshot’s Kimi: Kimi’s signature strength is ultra-long context; Zhipu AI’s edge is more about multimodal breadth (text + image + video integrated). If your core need is processing very long documents, Kimi may be more specialized; if you need multimodal coverage in one place, Zhipu fits better.
• vs. SiliconFlow: SiliconFlow focuses on hosting open-source models, with highly competitive DeepSeek/Qwen pricing — if you only need a text API and are highly cost-sensitive, an open-source model on SiliconFlow (especially after GLM’s price increase) may be the more economical option; Zhipu’s official GLM series wins on original-vendor support and multimodal integration.
• vs. Boluotu AI: Boluotu AI delivers stable, low-cost service through the official Azure channel, focused on value for money — if you need Claude/GPT-class international models rather than the domestic GLM series, Boluotu AI is the more relevant choice.

These three comparisons also highlight Zhipu AI’s distinct position among official domestic large-model channels: it’s neither the most specialized in a single capability (like Kimi’s long context) nor the cheapest (like SiliconFlow’s open-source model hosting), but occupies its own space on the combination of “multimodal integration + stability of an official direct connection.” Choosing Zhipu AI is essentially choosing a product philosophy that’s “willing to pay some premium for original-vendor support and a one-stop multimodal experience” — a trade-off that requires more active weighing now, post-GLM-5-price-increase, than it did before.

FAQ

Is there a transition-period discount for the GLM-5 price increase? Promotions can change at any time — check bigmodel.cn’s announcements page. Some vendors offer short-term transition discounts for existing users after a price increase; whether that applies to GLM-5 needs to be confirmed against live official announcements rather than assumed based on past experience.

Is the free tier enough? The free token credit given to new users is good for testing model capability and whether the integration meets your needs; real production use will almost certainly require payment. Check the console for the exact allotment and validity period — don’t assume at the design stage that the free tier can cover long-term production needs.

Coding subscription or pay-as-you-go — which to choose? If your usage is stable and concentrated on coding scenarios, the subscription is usually more cost-effective; if usage swings a lot or you need models outside the subscription’s scope, pay-as-you-go is more flexible — periodically re-evaluate based on your actual usage cycle.

GLM-5 or GLM-4.5-Air — which one? If your task doesn’t demand much model capability (simple text classification, summarization), GLM-4.5-Air’s cost-effectiveness post-price-increase may actually stand out more than GLM-5; if the task needs stronger reasoning or a longer context window, GLM-5’s capability gain may be worth the extra cost — we’d recommend a parallel comparison test on your real business data before deciding.

Will prices keep rising? No official information suggests continued price increases, but against the backdrop of this industry-wide 2026 repricing among domestic large-model vendors, users should stay in the habit of actively monitoring pricing rather than assuming the current price holds indefinitely — especially for long-term budget planning or annual procurement decisions, where it’s worth leaving some buffer for price fluctuation.

Bottom line

Zhipu AI is one of the more well-rounded choices among official domestic large-model APIs on Chinese-language capability and multimodal integration, and GLM-5’s release reflects continued investment in flagship model capability. But the February 2026 price increase (API up 67%-100%, Coding subscription up 30%-60%) is a change that can’t be ignored — the previous “capable enough, priced fairly” value label needs re-evaluating.

For teams that have already integrated the GLM series into their business, we’d recommend using the free tier and a short-term test to evaluate whether GLM-5’s actual improvement over GLM-4.5-Air is worth the post-increase cost; for new users still evaluating options, if budget is tight and you don’t specifically need Zhipu’s multimodal integration, a cheaper open-source model on SiliconFlow or the official DeepSeek API may be a more economical alternative.

The rating has been adjusted down slightly from 4.1 to 4.0, mainly reflecting the impact of this price increase on overall cost-effectiveness — the model capability and multimodal integration haven’t declined, but the actual cost-benefit ratio users get has narrowed noticeably post-increase. If Zhipu later introduces transition discounts for existing users or specific use cases, the rating could move back up depending on how things actually play out — worth continuing to watch the official pricing strategy going forward.

Information verified 2026-07-11. GLM series versions, pricing, and Coding subscription details continue to evolve — check bigmodel.cn’s official docs and live pricing for the latest.

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