GLM vs DeepSeek: An Objective Head-to-Head

GLM and DeepSeek are the two most-cited Chinese open-weight model families a US business will hit when it starts comparing alternatives to US frontier APIs. GLM is built by Z.AI (formerly Zhipu AI), a Tsinghua-spinout lab based in Beijing. DeepSeek is built by DeepSeek, a Hangzhou lab funded by High-Flyer, and rose to global attention with DeepSeek-V3 and the DeepSeek-R1 reasoning model.

Both labs publish weights on Hugging Face under permissive licenses, both undercut US frontier pricing, and both raise the same data-residency question for regulated buyers. This page compares them as objectively as possible: architecture and parameter counts, reasoning and coding benchmarks published in each lab's arXiv papers, real API pricing, context windows, license terms, and the compliance posture US firms actually have to defend.

The honest headline is that neither family is a universal winner. GLM's earlier reasoning-tuned releases (GLM-4.5 and GLM-4.6, before the current GLM-5.3) score competitively with DeepSeek-R1 on many public benchmarks and beat it on some agentic and tool-use tasks, while DeepSeek-V3 and DeepSeek-R1 still hold the edge on several coding and math evaluations and have a larger deployed ecosystem. The right pick depends on your workload, budget, and whether you can self-host.

GLM (Z.AI) vs. DeepSeek: Side-by-Side

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Quick Verdict: GLM vs DeepSeek

There is no automatic winner. GLM-4.5 and GLM-4.6 are Z.AI's serious answer to DeepSeek and match or beat it on several agentic and tool-use benchmarks while running on materially less GPU thanks to a smaller active-parameter count. DeepSeek-V3 and DeepSeek-R1 still hold the edge on many math, coding, and pure-reasoning evaluations, and DeepSeek has a bigger deployed ecosystem and more community tooling.

On current list prices, GLM-5.3-Flash ($0.15 in / $0.50 out) is the cheapest hosted tier here and works well for high-volume, lower-difficulty tasks, while DeepSeek-V4.1-Flash ($0.30 in / $1.20 out at peak, half that off-peak) is the cheapest general-purpose DeepSeek tier. At the top of each family, GLM-5.3 ($1.40 / $4.40) and DeepSeek-V4-Pro ($1.32 / $3.96 at peak) sit within about 10% of each other. On compliance, both families are Chinese-origin, so the data-residency question is identical, and self-hosting the open weights is the mitigation either way.

At-a-Glance Comparison Table

Prices are from the vendors' public pricing pages as of October 2026 (DeepSeek peak rates shown) and are worth re-checking before you commit. Benchmark leadership swings model-by-model and release-by-release, so the honest guidance is to test both on your own prompts.

Reasoning and Chain-of-Thought: R1 vs GLM

DeepSeek-R1 was the model that put reinforcement-learning reasoning on the map for open weights. Its arXiv paper (2501.12948) showed strong results on AIME 2024, MATH-500, and GPQA Diamond, and R1 remains the reference open-weight reasoning specialist for math-heavy and pure-logic problems.

Z.AI's GLM-4.5 and GLM-4.6 lean into agentic reasoning: multi-step tool use, browsing tasks, and function-calling. Z.AI's published evaluations put GLM-4.5 competitive with DeepSeek-R1 on general reasoning suites like MMLU-Pro and ahead of it on several agentic benchmarks such as BrowseComp. On raw closed-domain math (AIME-style problems) DeepSeek-R1 still tends to be the stronger pick.

Practical read: if your workflow is chains of tool calls, browsing, or structured agents, GLM's design goals line up with your task. If it is competition math, formal reasoning, or long chain-of-thought analysis of a single hard problem, DeepSeek-R1 is the stronger default.

• DeepSeek-R1: reference open-weight reasoning specialist; strong AIME / MATH-500 / GPQA scores per its arXiv paper.
• GLM-4.5 / 4.6: reasoning plus explicit agentic tuning; strong on tool-use and browsing benchmarks per Z.AI's evaluations.
• Neither dominates every reasoning benchmark; the winner shifts by task type and release version.

Coding Performance

DeepSeek has been treated as one of the stronger open-weight coding families since DeepSeek-V2.5 and continues that pattern with V3 and R1. Public evaluations show high scores on HumanEval, MBPP, LiveCodeBench, and Codeforces problems, and DeepSeek-R1 in particular does well on hard algorithmic tasks that reward long reasoning traces.

GLM-4.5 and GLM-4.6 are competitive rather than dominant on standard coding benchmarks and are notably strong on SWE-bench Verified for their active-parameter size class. Z.AI has publicly framed GLM-4.6 as an agentic-coding release, meaning it is tuned for tasks like editing repos through tool calls, not just single-file completion.

Neither family is a universal coding winner. For pure completion and hard algorithmic problems, DeepSeek tends to score higher. For agentic coding inside an IDE or a repo-level assistant that uses tools, GLM's design intent is the closer fit and its SWE-bench results support the claim.

Context Window and Long-Document Handling

GLM-4.5 / GLM-4.6 and DeepSeek-V3 / DeepSeek-R1 published 128K-token context windows, while DeepSeek's current V4-Pro and V4.1-Flash list 1M tokens with up to 384K of output. Even 128K is the same order of magnitude as the long-context models and is enough for most business long-document work: contracts, financial filings, moderate codebases, and long research threads. Neither family currently pushes into the multi-million-token territory that a small number of closed frontier models advertise.

The practical differences at long context show up in effective recall, not raw window size. Independent long-context evaluations (RULER, NIAH-style tests, LongBench) tend to show both families degrading past the mid-tens-of-thousands of tokens rather than staying uniformly sharp to 128K. If your workflow depends on precise recall from very long inputs, benchmark both on your own documents rather than trusting the headline number.

Pricing: Hosted API and Self-Host Reality

On hosted API pricing, both families are aggressively cheap versus US frontier models. Z.AI's published pricing puts the current GLM-5.3 at $1.40 per million input tokens and $4.40 per million output tokens, with GLM-5.3-Flash at $0.15 in and $0.50 out. DeepSeek's public pricing puts DeepSeek-V4-Pro at $1.32 in and $3.96 out and DeepSeek-V4.1-Flash at $0.30 in and $1.20 out on cache-miss requests at peak rates. Off-peak, DeepSeek charges half of those rates, and both vendors price cached input far lower, which can materially lower the effective cost.

GLM-5.3-Flash is the cheapest of these tiers for high-volume, lower-difficulty work, and DeepSeek-V4.1-Flash matches its input price only at off-peak rates. At the top of each family, GLM-5.3 and DeepSeek-V4-Pro sit within about 10% on price, so the tiebreaker is usually workload fit, not price.

Self-hosting costs are dominated by GPU capacity. GLM-4.5-Air (106B / 12B active MoE) is the easiest of the four to self-host and can run on a smaller H100 or MI300X cluster. GLM-4.5 at 355B total and DeepSeek-V3 at 671B total both need serious multi-GPU inference setups; DeepSeek-V3 is the larger of the two by total parameters and typically the more expensive to stand up in-house.

Always re-check both vendors' pricing pages before committing — public API prices for Chinese labs have moved several times in the past year.

→ Compare per-token pricing for every AI model

Open-Weights and License

Z.AI publishes GLM-4.5 family weights on Hugging Face under an MIT license, which is one of the most permissive options and clearly allows commercial use, modification, and redistribution.

DeepSeek publishes DeepSeek-R1 weights under an MIT license as well. DeepSeek-V3 weights are released under the DeepSeek Model License, which is not MIT but explicitly permits commercial use with some acceptable-use restrictions similar to those in the Llama community license.

For an enterprise buyer, both families are effectively 'open-weight and commercially usable,' but the DeepSeek-V3 license is worth having a legal reviewer read once. Both labs release enough tokenizer, config, and inference code alongside the weights that standard inference stacks (vLLM, SGLang, TensorRT-LLM) support them out of the box.

Data Residency and Compliance: The Question That Applies to Both

GLM and DeepSeek are both built and hosted by Chinese labs. Sending data to their hosted APIs means it may be processed on infrastructure governed by Chinese law, including obligations under the Cybersecurity Law, the Data Security Law, and the Personal Information Protection Law. Many US and EU regulated firms cannot accept that for customer or protected data.

The open weights are the escape hatch for both families. Because you can download GLM-4.5, GLM-4.5-Air, DeepSeek-V3, or DeepSeek-R1 and run them on your own infrastructure, you can keep every request inside your own VPC or data center and control exactly where data lives. Neither lab has a meaningful compliance edge over the other on this dimension — the difference is what it costs you to self-host each one.

For a fuller treatment of the Chinese-origin data question, see our DeepSeek data privacy and security risks guide; the analysis applies directly to GLM as well.

When to Choose Which

Pick GLM (Z.AI) when your workflow is agent-shaped: tool calls, browsing, structured function calls, or repo-level coding assistants. GLM-5.3-Flash is the right first stop for high-volume, lower-difficulty inference where per-token cost matters, and GLM-5.3 is the right stop when you need the fuller flagship.

Pick DeepSeek when your workflow rewards deep reasoning: math, formal analysis, hard algorithmic coding, or long single-thread chain-of-thought. DeepSeek-V4.1-Flash is the sensible general-purpose default for cost; DeepSeek-R1 remains the reference reasoning specialist for problems where a longer reasoning trace measurably lifts accuracy.

Pick both, in a router, when you can afford the engineering. Many production teams end up sending cheap, high-volume traffic to GLM-5.3-Flash or DeepSeek-V4.1-Flash and reserving DeepSeek-V4-Pro or GLM-5.3 for the hard tail. That is often better economics than picking a single family.

Limitations of Each

GLM limitations: shorter enterprise deployment history than DeepSeek outside China; less third-party tooling and fewer community fine-tunes; some benchmark leadership claims come from Z.AI's own evaluations and should be spot-checked against independent aggregators.

DeepSeek limitations: DeepSeek-V3 is the largest model of the four by total parameters and correspondingly the most expensive to self-host; the DeepSeek Model License on V3 is not MIT and needs a legal read; both labs' hosted APIs sit under Chinese jurisdiction, which is a hard blocker for some regulated buyers regardless of price or benchmark scores.

Common to both: benchmark leaderboards move with every release, so today's ranking is not permanent. Independent long-context and safety evaluations for both families are still catching up to the pace of release. The disciplined move is to run both on a representative slice of your own workload before you commit.

The Verdict

Choose GLM when your workloads are agentic (tool use, browsing, function-calling, or repo-level coding) or when you want the cheapest hosted option (GLM-5.3-Flash) or a lighter model to self-host.

Choose DeepSeek when reasoning depth, math, or hard coding dominates, or when you want the more battle-tested open-weight family with the larger deployment community.

Either way, if your data is regulated, plan to self-host the open weights. Both are Chinese-origin, so the data-residency question is identical and self-hosting is the practical mitigation.