GLM 5.2 vs DeepSeek V3: Benchmarks, Pricing, and Use Cases

You are choosing between two open-weight Chinese frontier models with very different cost and capability trade-offs: GLM 5.2 is faster, smarter, and handles million-token documents, while DeepSeek V3 delivers near-comparable reasoning at a fraction of the price.

The short version: GLM 5.2 scores 51 on the AA Intelligence Index versus ~47 for DeepSeek V3, runs at 158 t/s versus ~55 t/s, and supports a 1M-token context window that is eight times larger than DeepSeek V3's 128K limit. DeepSeek V3 output tokens cost $1.10/M compared to $4.40/M for GLM 5.2 — a 75% reduction — and DeepSeek V3 carries an Apache 2.0 license that allows commercial self-hosting with fewer restrictions.

Choose GLM 5.2 if you are processing long documents, running agentic pipelines that need fast throughput, or need top-tier scores on coding and scientific reasoning benchmarks.

Choose DeepSeek V3 if cost per token is your primary constraint, you need to self-host under a permissive license, or your tasks fit comfortably within 128K context.

Quick Comparison

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Benchmark Performance

Both models are Mixture-of-Experts architectures with similar active parameter counts (~40B vs ~37B), which is why they land within striking distance on general benchmarks. The gap widens on scientific and agentic tasks.

GLM 5.2's 89% on GPQA Diamond is one of the highest published scores on that graduate-level science benchmark, suggesting it is meaningfully better at multi-step scientific reasoning than DeepSeek V3's ~75%. The 14-point GPQA gap is large enough to matter for research-adjacent use cases — chemistry, biology, physics — where approximate reasoning is not acceptable.

On HumanEval, both models converge around 90%, which reflects the ceiling effect on that benchmark: most frontier models saturate it. For coding evaluation, SWE-bench Pro is a better discriminator, and GLM 5.2's 62.1% on the production-code variant indicates strong software engineering ability. DeepSeek V3's SWE-bench numbers are not directly comparable because it was evaluated on a different version of the benchmark.

The AA Intelligence Index aggregates multiple benchmarks into a single number. A 4-point gap (51 vs ~47) at this tier of the frontier represents a real but not dramatic quality difference — for most routine language tasks, users would likely not notice it.

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Pricing Breakdown

DeepSeek V3 is substantially cheaper on both input and output. For a workload that processes 10M output tokens per day, GLM 5.2 costs $44/day while DeepSeek V3 costs $11/day — a $33/day difference that compounds to roughly $1,000/month in savings at that scale.

GLM 5.2's cache hit price of $0.26/M is exceptionally low and changes the math for applications with repetitive system prompts or fixed document prefixes. If your prompt is 80% static and you hit the cache consistently, your effective input cost drops close to $0.26/M, narrowing the pricing gap with DeepSeek V3.

For low-volume or experimental use, the absolute dollar difference is small. A developer running 1M output tokens per month pays $4.40 for GLM 5.2 versus $1.10 for DeepSeek V3 — a $3.30 monthly difference. At that scale, capability and API reliability matter more than price.

GLM 5.2 is accessible through Z.ai (base_url: https://api.z.ai/v1, model glm-5.2) and OpenRouter (z-ai/glm-5.2). For more on GLM 5.2 API access and cost structure, see the GLM 5.2 pricing overview.

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Speed and Latency

GLM 5.2 generates tokens roughly three times faster than DeepSeek V3. According to Artificial Analysis, GLM 5.2 ranks third among all frontier models on throughput — that is exceptional for a 753B-parameter model and reflects the efficiency of its sparse MoE design with 8 activated experts per token.

DeepSeek V3's TTFT of ~1.2 seconds is slightly faster than GLM 5.2's 1.54 seconds, meaning the first token appears sooner for DeepSeek V3. In interactive chat applications where perceived responsiveness matters, this ~0.3-second TTFT advantage can feel noticeable. However, once generation starts, GLM 5.2's 3x throughput advantage means it finishes long responses much faster in absolute time.

For agentic workloads with many sequential calls — where each tool-call response feeds into the next — GLM 5.2's higher throughput reduces wall-clock time meaningfully. A pipeline that makes 50 LLM calls with 500-token responses would complete roughly 3x faster with GLM 5.2, assuming similar infrastructure latency.

For streaming chat interfaces where users read as the model types, both models feel responsive. The throughput difference becomes critical for batch processing, background jobs, and multi-step agents where total time matters.

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Context Window

GLM 5.2 supports a 1,048,576-token (1M) context window. DeepSeek V3 supports 128,000 tokens. The practical implications depend heavily on your use case.

A 1M context window fits approximately:

• A full software repository with 700+ files of average size
• A 750-page legal contract or technical specification
• An entire book series or multi-year email archive
• Dozens of research papers in a single prompt

A 128K window (DeepSeek V3) fits approximately:

• A 100-page document
• 5-10 research papers
• A medium-sized codebase in its entirety

For most question-answering, summarization, and code generation tasks, 128K is sufficient. The majority of real-world prompts fit well within that limit. But when they do not — when you need to pass a full repository, a lengthy regulatory document, or a rich conversation history — DeepSeek V3 requires chunking, retrieval, or context compression strategies that add engineering overhead and risk losing relevant information.

GLM 5.2's 1M context is a qualitative capability, not just a quantitative one. It enables use cases that are architecturally impossible with a 128K model: cross-document reasoning across an entire document set, in-context learning from hundreds of examples, or maintaining full session history across a long agentic task without summarization loss.

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Key Differentiators: License and Self-Hosting

Both models are open-weight and self-hostable, but their licenses differ in important ways.

GLM 5.2 uses the MIT license. MIT is one of the most permissive licenses in software — it imposes almost no restrictions on use, modification, or distribution, including commercial use. Weights are available on Hugging Face at THUDM/GLM-5.2.

DeepSeek V3 uses Apache 2.0. Apache 2.0 is also a permissive commercial license, but it includes attribution requirements and explicit patent clauses that MIT omits. For most enterprise legal teams, both licenses clear the bar for commercial deployment. If your legal team has pre-approved MIT projects broadly, GLM 5.2 may face fewer internal review cycles.

Neither model supports image or audio input — both are text-only. If your application requires multimodal input, neither model is a fit in its current form. Check the GLM 5.2 full specs for the latest capability roadmap.

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When to Choose GLM 5.2

• Your documents or conversation histories exceed 128K tokens and chunking is not acceptable
• You are running agentic pipelines where throughput (158 t/s) directly reduces wall-clock time and cost
• GPQA Diamond-level scientific reasoning (89%) is required for your domain — chemistry, biology, medicine, advanced engineering
• You need SWE-bench-quality software engineering performance (62.1% on Pro variant)
• Your system prompt is large and static, making GLM 5.2's $0.26/M cache hit price cost-effective
• You want a proven MIT-licensed open-weight model with a straightforward commercial path
• You prefer the Z.ai API or OpenRouter for deployment without managing your own GPU infrastructure

When to Choose DeepSeek V3

• Output token cost is the dominant factor and you can work within 128K context
• You are self-hosting on your own infrastructure and want Apache 2.0's explicit patent protections
• Your use case is general text tasks — summarization, translation, content generation, chat — where the 4-point AA Index gap is unlikely to be perceptible
• You are prototyping or running low-volume experiments and want the lowest entry cost
• Your team already has DeepSeek V3 integrated and switching costs outweigh the performance delta
• TTFT (~1.2 s) matters more than generation throughput for your streaming use case

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Frequently Asked Questions

Is GLM 5.2 better than DeepSeek V3? On measurable benchmarks, GLM 5.2 scores higher across the board: 51 vs ~47 on the AA Intelligence Index, 89% vs ~75% on GPQA Diamond, and significantly faster generation at 158 t/s vs ~55 t/s. Whether "better" translates to your use case depends on whether those differences are in the dimensions you care about. For cost-sensitive general tasks, DeepSeek V3 delivers strong value. For scientific reasoning, long-context tasks, and agentic pipelines, GLM 5.2 is the stronger choice.

Can I use both models for the same application? Yes. A common pattern is to route cheap, high-volume tasks (classification, extraction, simple Q&A) to DeepSeek V3 and route complex, latency-sensitive, or long-context tasks to GLM 5.2. Both expose OpenAI-compatible APIs, making it straightforward to switch between them at the routing layer without changing application code.

How does GLM 5.2's 1M context window work in practice? GLM 5.2 uses a standard attention mechanism extended to 1M tokens through architectural optimizations. In practice, performance at the very far end of a 1M context can degrade — this is true of all long-context models. For best results with very long documents, structure the most important information near the beginning or end of the context, where attention tends to be strongest.

Which model is better for coding? Both models score around 90% on HumanEval. GLM 5.2's 62.1% on SWE-bench Pro suggests stronger performance on real-world software engineering tasks — bug fixing, multi-file edits, and repository-scale reasoning. For short, self-contained code generation, the models are roughly equivalent. For production software engineering workflows, GLM 5.2 has the edge.

Are both models available via API without self-hosting? Yes. GLM 5.2 is available through Z.ai (no API key required via glm5.app/chat for interactive use) and OpenRouter. DeepSeek V3 is available through DeepSeek's own API, as well as multiple third-party API providers. Self-hosting both models requires significant GPU resources given their 753B and 671B parameter counts respectively.

Does DeepSeek V3's lower price mean lower quality? Not necessarily — DeepSeek V3 achieves ~90% on HumanEval and ~88.5% on MMLU, which are competitive scores for frontier-tier models. The price difference reflects DeepSeek's infrastructure economics and business model rather than a direct quality compromise. For general language tasks, the quality gap is small. The gap widens on specialized scientific benchmarks (GPQA Diamond: 89% vs ~75%) and agentic tasks where GLM 5.2's higher AA Index score manifests.

Which model has the better open-source license? Both MIT (GLM 5.2) and Apache 2.0 (DeepSeek V3) are permissive licenses that allow commercial use, modification, and redistribution. MIT is simpler and slightly more permissive — it has no patent clause and fewer attribution requirements. For most commercial applications, both licenses are acceptable; consult your legal team if you have specific compliance requirements.

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Bottom Line

GLM 5.2 is the stronger model by every benchmark — it is faster, smarter on scientific tasks, and handles contexts eight times larger than DeepSeek V3. DeepSeek V3 is the better choice when you are optimizing for cost and your workloads fit within 128K tokens. At scale, the 75% output token savings are meaningful. At lower volumes, or when quality and speed matter, GLM 5.2 is worth the premium.

Try GLM 5.2 — no API key needed: glm5.app/chat.

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Sources

• Artificial Analysis — GLM 5.2 Model Analysis
• Artificial Analysis — DeepSeek V3 Model Analysis
• GLM 5.2 Model Weights — Hugging Face THUDM/GLM-5.2
• DeepSeek API Pricing
• Z.ai API Documentation