8 Best Z.ai (GLM) Alternatives (2026)

The closest alternatives to Z.ai (GLM) are Qwen, Amazon Bedrock, Anthropic and Cerebras, with 4 more below. Each shares a comparison category with it, so every rationale here names both tools' real values. Z.ai (GLM) is marked No on Zero retention, where Amazon Bedrock records Yes — usually where a switch starts.

Website Docs Z.ai (GLM)logo & brand assets LLM APIs

Why do people look for Z.ai (GLM) alternatives?

Most people searching for Z.ai (GLM) alternatives are not shopping — they already run it and something has stopped fitting. That something is usually a specific number rather than a feeling, so this page starts from the fields where Z.ai (GLM) genuinely trails the rosters it appears in, and only then covers the reasons that never show up in a table.

The measured reasons.

Zero retention — No. Amazon Bedrock records Yes.
Context window — 200,000 tokens, 9th of 10. Anthropic records 1,000,000 tokens.

Any one of those is enough on its own if you sized your architecture around it. None of them is enough if you didn't — which is why the list is short and specific rather than a general case against Z.ai (GLM).

The reasons that never make it into a table. A price rise after a funding round. A licence change that turns a self-host into a subscription. A region you now need and they do not have. An acquisition. A support experience that quietly degrades. None of those are fields, and all of them move teams — which is why every entry below links back to LLM APIs, where the full field set and its sources live. The ones we catch get logged on the Z.ai (GLM) timeline.

How the shortlist is ordered. Every tool below shares at least one comparison category with Z.ai (GLM), sorted by how many categories the two overlap in. There is no editorial ranking, no sponsorship and no affiliate link — the order is the overlap count, and the rationale under each is generated from the two tools' own cells, so it names real values rather than adjectives.

1.Qwen

Qwen — Alibaba's model family — huge open-weight range, closed flagship. It meets Z.ai (GLM) in LLM APIs. It is ahead on MCP support (Partial against No), AU region (Partial against No) and Audio in/out (Partial against No). What you give up: Open weights (Partial, where Z.ai (GLM) records Yes) and $/M output ($6 /M tok, where Z.ai (GLM) records $2.2 /M tok). Best for finding an open-weight model at a specific size or modality. The best open-weight range in the category — there is a Qwen model at nearly every size and modality, mostly Apache 2.0. The hosted Max tier is a reasonable mid-price flagship but rarely the reason to be here; most teams use the open weights through a Western host and treat Model Studio as optional.

Where Qwen and Z.ai (GLM) actually differ

MCP support: PartialZ.ai (GLM): No
AU region: PartialZ.ai (GLM): No
Audio in/out: PartialZ.ai (GLM): No
Open weights: PartialZ.ai (GLM): Yes
$/M output: $6 /M tokZ.ai (GLM): $2.2 /M tok
$/M input: $1.2 /M tokZ.ai (GLM): $0.6 /M tok

Qwen profile Compare in LLM APIs

2.Amazon Bedrock

Amazon Bedrock — Multi-vendor model access inside your existing AWS account. It meets Z.ai (GLM) in LLM APIs. It is ahead on Zero retention (Yes against No), AU region (Yes against No) and Context window (1,000,000 tokens against 200,000 tokens). What you give up: OpenAI-compat API (No, where Z.ai (GLM) records Yes) and Trains on your data (No, where Z.ai (GLM) records Partial). Best for regulated workloads that must stay inside an existing AWS perimeter. Choose Bedrock for procurement and governance, not capability. IAM, VPC endpoints, an existing contract, a Sydney region and a published model lifecycle are worth real money to regulated teams. Accept that you will be weeks or months behind on features, and that the MCP connector and automatic caching are simply not there.

Where Amazon Bedrock and Z.ai (GLM) actually differ

Zero retention: YesZ.ai (GLM): No
OpenAI-compat API: NoZ.ai (GLM): Yes
AU region: YesZ.ai (GLM): No
Context window: 1,000,000 tokensZ.ai (GLM): 200,000 tokens
Computer use: YesZ.ai (GLM): No
Trains on your data: NoZ.ai (GLM): Partial

Amazon Bedrock profile Compare in LLM APIs

3.Anthropic

Anthropic — Claude models, built around long agentic runs and tool use. It meets Z.ai (GLM) in LLM APIs. It is ahead on MCP support (Yes against No), Context window (1,000,000 tokens against 200,000 tokens) and Computer use (Yes against No). What you give up: $/M output ($50 /M tok, where Z.ai (GLM) records $2.2 /M tok) and $/M input ($10 /M tok, where Z.ai (GLM) records $0.6 /M tok). Best for agent harnesses that call tools for minutes at a time. The best platform here for anything that runs a tool loop for more than a few turns — effort control, explicit cache breakpoints and MCP are designed for that shape of work rather than retrofitted. The continuity story regressed with the current generation: dropping dated snapshot IDs means you pin to a published retirement date, not to an immutable model. One flagship-specific trap worth wiring for on day one: Fable 5 can decline a request outright, returning HTTP 200 with stop_reason 'refusal' and no usable content, so a client that reads the first content block unconditionally breaks rather than errors. Anthropic ships a fallbacks parameter that re-runs the request on another model; use it, or handle the stop reason yourself.

Where Anthropic and Z.ai (GLM) actually differ

$/M output: $50 /M tokZ.ai (GLM): $2.2 /M tok
$/M input: $10 /M tokZ.ai (GLM): $0.6 /M tok
MCP support: YesZ.ai (GLM): No
Open weights: NoZ.ai (GLM): Yes
Context window: 1,000,000 tokensZ.ai (GLM): 200,000 tokens
Computer use: YesZ.ai (GLM): No

Anthropic profile Compare in LLM APIs

4.Cerebras

Cerebras — Wafer-scale inference — the fastest tokens per second available. It meets Z.ai (GLM) in LLM APIs. Against that: Trains on your data (No, where Z.ai (GLM) records Partial) and Effort control (No, where Z.ai (GLM) records Partial). Best for reasoning models where thinking-token latency is the bottleneck. Consistently the fastest single-stream generation you can buy, by a margin that is qualitative rather than incremental — reasoning models that take half a minute elsewhere return in a couple of seconds. The catch is the same as Groq's: a short menu, no bring-your-own-weights, and no continuity guarantees at all.

Where Cerebras and Z.ai (GLM) actually differ

Trains on your data: NoZ.ai (GLM): Partial
Effort control: NoZ.ai (GLM): Partial
Image input: NoZ.ai (GLM): Partial
Tool use: PartialZ.ai (GLM): Yes
Pinnable versions: PartialZ.ai (GLM): Yes
API since: 2024Z.ai (GLM): 2023

Cerebras profile Compare in LLM APIs

5.Cohere

Cohere — Enterprise-focused models built for RAG and private deployment. It meets Z.ai (GLM) in LLM APIs. It is ahead on Zero retention (Partial against No), AU region (Partial against No) and Schema output (Yes against Partial). What you give up: Trains on your data (No, where Z.ai (GLM) records Partial) and Effort control (No, where Z.ai (GLM) records Partial). Best for RAG pipelines where rerank quality drives the result. Has sensibly stopped chasing the frontier and now competes where it can win: retrieval quality, private deployment and enterprise contracts. Its rerank and embedding models remain best-in-class and are the more common reason to be a customer. As a general-purpose chat API it is priced like a frontier lab without matching one.

Where Cohere and Z.ai (GLM) actually differ

Trains on your data: NoZ.ai (GLM): Partial
Effort control: NoZ.ai (GLM): Partial
Zero retention: PartialZ.ai (GLM): No
AU region: PartialZ.ai (GLM): No
Schema output: YesZ.ai (GLM): Partial
OpenAI-compat API: PartialZ.ai (GLM): Yes

Cohere profile Compare in LLM APIs

6.DeepSeek

DeepSeek — Frontier-adjacent models at a small fraction of Western prices. It meets Z.ai (GLM) in LLM APIs. It is ahead on Trains on your data (Yes against Partial). What you give up: Pinnable versions (No, where Z.ai (GLM) records Yes) and Image input (No, where Z.ai (GLM) records Partial). Best for high-volume classification, extraction and summarisation. The price floor of the category and the reason everyone else's rates fell. For bulk text work the quality-per-dollar is unmatched. Do not send regulated data to the first-party endpoint — permissive retention terms and rolling aliases with no pinning make it unsuitable for anything sensitive or long-lived. Use the MIT weights through a Western host instead.

Where DeepSeek and Z.ai (GLM) actually differ

Pinnable versions: NoZ.ai (GLM): Yes
Image input: NoZ.ai (GLM): Partial
Tool use: PartialZ.ai (GLM): Yes
Trains on your data: YesZ.ai (GLM): Partial
Context window: 128,000 tokensZ.ai (GLM): 200,000 tokens
$/M output: $0.42 /M tokZ.ai (GLM): $2.2 /M tok

DeepSeek profile Compare in LLM APIs

7.Fireworks AI

Fireworks AI — Fast open-weight inference with strong structured-output support. It meets Z.ai (GLM) in LLM APIs. It is ahead on Audio in/out (Partial against No) and Schema output (Yes against Partial). What you give up: Trains on your data (No, where Z.ai (GLM) records Partial) and Effort control (No, where Z.ai (GLM) records Partial). Best for open-weight workloads where output must validate against a schema every time. Hard to separate from Together on paper; the practical split is that Fireworks invests more in constrained decoding and latency tuning. If your pipeline breaks when a response fails to parse, its grammar enforcement is the more reliable of the two. Benchmark both on your own workload — the difference is real but small.

Where Fireworks AI and Z.ai (GLM) actually differ

Trains on your data: NoZ.ai (GLM): Partial
Effort control: NoZ.ai (GLM): Partial
Audio in/out: PartialZ.ai (GLM): No
Tool use: PartialZ.ai (GLM): Yes
Schema output: YesZ.ai (GLM): Partial

Fireworks AI profile Compare in LLM APIs

8.Google Gemini

Google Gemini — Gemini via AI Studio for prototyping or Vertex AI for production. It meets Z.ai (GLM) in LLM APIs. It is ahead on AU region (Yes against No), Context window (1,000,000 tokens against 200,000 tokens) and Audio in/out (Yes against No). What you give up: Open weights (Partial, where Z.ai (GLM) records Yes) and $/M output ($12 /M tok, where Z.ai (GLM) records $2.2 /M tok). Best for workloads that genuinely need 500K+ tokens of context per request. The cheapest credible route to a million-token window and the only provider here with genuinely native audio in and out. Vertex is also the most convincing answer to an Australian data-residency requirement. The cost is churn — this line-up turns over faster than any other, so pin versions and expect a migration each year.

Where Google Gemini and Z.ai (GLM) actually differ

AU region: YesZ.ai (GLM): No
Context window: 1,000,000 tokensZ.ai (GLM): 200,000 tokens
Audio in/out: YesZ.ai (GLM): No
Zero retention: PartialZ.ai (GLM): No
MCP support: PartialZ.ai (GLM): No
Computer use: PartialZ.ai (GLM): No

Google Gemini profile Compare in LLM APIs

How this list was built

There is no editorial ranking on this page and no sponsorship behind it. The order is mechanical: every tool that shares a comparison category with Z.ai (GLM), sorted by how many categories the two overlap in. A tool that meets Z.ai (GLM) in three rosters sits above one that meets it in a single roster, because more overlap means the comparison is more like-for-like.

The rationale under each entry is composed from the two tools' own cells. Where they differ on a field we score, the sentence names both values and the unit. Where they don't differ, it says so instead of manufacturing a distinction — which is why some entries are short.

Values, sources and verification dates all live on the category tables: LLM APIs. If a figure here disagrees with a vendor's current pricing page, the vendor is right and we are stale.

Still deciding whether to move at all? The Z.ai (GLM) profile has the when-to-use and when-not-to-use blocks, and the Z.ai (GLM) timeline has the dated changes that usually trigger a migration.