GLM 5.2 vs Llama 3.3 70B: Open-Weight Models Compared on Speed, Benchmarks, and Cost

You are evaluating two open-weight models for a production workload and the decision comes down to a concrete trade-off: GLM 5.2 scores 9 points higher on the Artificial Analysis Intelligence Index and offers 8x more context, while Llama 3.3 70B costs roughly 80% less per token on Groq and runs on hardware many teams already own.

The short version. GLM 5.2 sits at AA Index 51 with a 1M-token context window, 158 tokens per second throughput, and strong reasoning benchmarks including 89% on GPQA Diamond. Llama 3.3 70B lands at ~AA Index 42, tops out at 128K context, delivers ~70-80 t/s on Groq, and costs $0.59/$0.79 per million input/output tokens — well below GLM 5.2's $1.40/$4.40. If your tasks fit inside 128K tokens and you are cost-sensitive or need full self-hosting freedom, Llama 3.3 is a credible choice. If you need frontier reasoning, long-document processing, or the highest code benchmark scores among open models, GLM 5.2 justifies the premium.

Choose GLM 5.2 if you process documents longer than 128K tokens, need the strongest available open-weight reasoning and coding scores, or want a single model that handles complex multi-step problems without hitting a context ceiling.

Choose Llama 3.3 70B if your workload fits comfortably inside 128K tokens, you are optimizing hard on cost, you need to self-host on your own A100s without licensing complications, or you depend on an ecosystem of existing fine-tunes, adapters, and community tools.

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Quick Comparison

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Benchmark Performance

The gap between these two models is most visible on reasoning-heavy benchmarks. GLM 5.2's 89% on GPQA Diamond — a graduate-level science question set widely used as a proxy for deep reasoning capability — reflects its 753B parameter Mixture-of-Experts architecture, which routes each token through 8 of 256 available expert layers per pass. That selective activation gives GLM 5.2 the capacity of a very large model while keeping inference costs manageable.

Llama 3.3 70B's ~88% HumanEval score is genuinely strong for a dense 70B model and it will handle the majority of practical coding tasks competently. The delta becomes meaningful when tasks require multi-step reasoning over long contexts or specialized scientific and engineering problem-solving, where the gap between ~42 and 51 on the AA Index translates to real error rates in production.

SWE-bench Pro at 62.1% is one of GLM 5.2's headline differentiators. This benchmark evaluates real-world software engineering tasks drawn from GitHub repositories — resolving issues, navigating codebases, and generating patches — making it the closest available proxy to actual developer tooling use cases. A published comparable Llama 3.3 70B score on SWE-bench Pro is not available at the time of writing, which itself reflects where the model sits in the capability tier.

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Pricing Breakdown

The pricing gap is substantial and deserves honest treatment. At $4.40 per million output tokens, GLM 5.2 costs more than five times Llama 3.3 70B's $0.79 on Groq for output-heavy workloads such as document generation, summarization pipelines, or high-volume chat. For a team running 10 million output tokens per day, that is roughly $36 in daily savings, or about $13,000 per year — real money for a startup.

GLM 5.2's cache hit pricing at $0.26 per million input tokens provides meaningful relief for workloads that repeatedly use the same long system prompt or document prefix. If you are querying a fixed 200K-token knowledge base repeatedly, the effective input cost drops sharply. That said, Llama 3.3 70B's base input price of $0.59 is already lower than GLM 5.2's cached price of $0.26 only when cache hits are very high.

For teams with the infrastructure to self-host, both models are free to run. GLM 5.2's MIT license imposes no restrictions whatsoever. Llama 3.3 operates under the Llama 3 Community License, which is permissive for most teams but includes a clause restricting commercial use for services exceeding 700 million monthly active users — a threshold that affects a very small number of organizations.

For mid-sized teams running managed API inference, Llama 3.3 70B on Groq is the clear cost winner. For teams that need the capability ceiling GLM 5.2 offers and can absorb the cost, the premium is defensible. See the GLM 5.2 pricing breakdown for a deeper look at cache and volume tiers.

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Speed and Latency

GLM 5.2's 158 tokens per second, reported by Artificial Analysis as the third-fastest frontier model in their dataset, is a meaningful result for a 753B-parameter model. The Mixture-of-Experts architecture is a key reason — activating only 40B parameters per forward pass means the model does not pay the full computational cost of its total parameter count at inference time.

Llama 3.3 70B on Groq achieves ~70-80 t/s, which is competitive for a dense 70B model and reflects Groq's custom Language Processing Unit (LPU) hardware. For interactive chat or real-time streaming applications, either model is fast enough that users will not notice the difference. For batch workloads processing millions of tokens per hour, GLM 5.2's roughly 2x throughput advantage translates directly into wall-clock time reduction.

GLM 5.2's time to first token of 1.54 seconds is acceptable for most production use cases. For latency-critical applications — autocomplete, real-time voice, or sub-second response requirements — both models will require careful infrastructure tuning, and TTFT will depend significantly on prompt length and provider load.

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Context Window

GLM 5.2's 1,048,576-token context window is not a marginal improvement over Llama 3.3 70B's 128K — it is a qualitatively different capability tier. To put the gap in concrete terms: 1M tokens can hold approximately 750,000 words of English text, or a book-length document of roughly 2,500 pages. Llama 3.3 70B's 128K window handles around 96,000 words, or a document roughly 300 pages long.

For most chat, Q&A, and summarization tasks, 128K is more than sufficient. The Llama 3.3 ceiling becomes a genuine limitation in specific high-value scenarios:

• Full codebase analysis. Large production repositories routinely exceed 128K tokens. GLM 5.2 can ingest an entire multi-service backend in a single context; Llama 3.3 70B requires chunking strategies that introduce retrieval complexity and miss cross-file dependencies.
• Legal and financial document review. Long contracts, prospectuses, or regulatory filings often run well past 128K tokens. Fitting them in a single pass preserves document coherence and reduces hallucination risk from truncation or retrieval misses.
• Multi-turn research sessions. A long conversation that accumulates retrieved context, tool outputs, and intermediate reasoning steps can fill a 128K window faster than expected. GLM 5.2's 1M window gives significantly more headroom before the model starts losing early context.
• Agentic workflows. Autonomous agents that execute many tool calls and accumulate results in context benefit from a larger window that keeps the full execution history visible.

If your typical prompt fits comfortably inside 50K-100K tokens, the context advantage is largely academic. If you regularly push against 128K or need headroom for unpredictable document sizes, the GLM 5.2 window is a functional requirement, not a luxury.

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License and Self-Hosting

Both models are open-weight and self-hostable, but their licenses differ in ways that matter for some organizations.

GLM 5.2 is released under the MIT license — the most permissive common open-source license. There are no restrictions on commercial use, no user-count thresholds, and no attribution requirements beyond including the license text. Organizations that need to deploy a model behind a firewall, modify weights, or integrate into proprietary products can do so freely. Weights are available on Hugging Face at THUDM/GLM-5.2.

Llama 3.3 70B uses the Llama 3 Community License, which permits most commercial uses but restricts services that reach more than 700 million monthly active users — a threshold essentially limited to the largest global consumer platforms. For the vast majority of companies, this restriction is irrelevant in practice.

Self-hosting hardware requirements differ substantially. Llama 3.3 70B at 70B parameters in BF16 precision requires approximately two A100 40GB GPUs, a configuration many ML teams already have access to. GLM 5.2's published weights and MoE architecture have different memory footprints; teams evaluating self-hosted GLM 5.2 should verify current hardware requirements on the Hugging Face model page, as MoE inference memory profiles depend on the batching strategy and expert caching implementation.

Llama 3.3 70B has a substantial ecosystem advantage: years of community development have produced a wide array of fine-tunes, quantizations, LoRA adapters, and integrations with frameworks like vLLM, llama.cpp, Ollama, and LangChain. If your team needs a fine-tuned variant for a specific domain, or wants to run a 4-bit quantized version on a single GPU, the Llama ecosystem has more off-the-shelf options today.

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When to Choose GLM 5.2

• Your documents, codebases, or conversation histories regularly exceed 128K tokens
• You need the strongest available open-weight reasoning scores (GPQA 89%)
• Code generation quality and SWE-bench Pro performance (62.1%) are business-critical
• You want MIT-licensed weights with zero commercial restrictions
• Throughput matters and 158 t/s is a real advantage for your batch workload
• You are accessing via API and can justify $1.40/$4.40 per million tokens for the capability

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When to Choose Llama 3.3 70B

• Your prompts and context consistently fit inside 128K tokens
• Cost is the primary constraint and 80% savings on output tokens is material
• You have A100 40GB hardware and want to self-host without managing a larger MoE system
• You need access to a large ecosystem of existing fine-tunes and community adapters
• Your use case is standard chat, summarization, or code completion that does not require frontier reasoning
• You are building on Groq's infrastructure and want the lowest-latency option within a budget

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Frequently Asked Questions

Is GLM 5.2 actually open source? GLM 5.2 is released under the MIT license with weights publicly available on Hugging Face at THUDM/GLM-5.2. MIT is one of the most permissive open-source licenses and places no restrictions on commercial use, modification, or redistribution beyond including the license text. In practical terms, it is as open as open-weight models get.

Can Llama 3.3 70B be used commercially? Yes, for virtually all companies. The Llama 3 Community License permits commercial use with a restriction for services exceeding 700 million monthly active users. Unless your product operates at the scale of major global consumer platforms, the license does not impose meaningful constraints.

How much does it cost to self-host Llama 3.3 70B? In BF16 precision, Llama 3.3 70B requires approximately two A100 40GB GPUs for inference. On-demand A100 cloud instances typically run $2-4 per hour per GPU, putting raw self-hosting costs at roughly $4-8 per hour. At moderate request volumes, Groq's $0.59/$0.79 managed pricing is often cheaper than self-hosting until you reach high sustained throughput that fully utilizes dedicated hardware.

What is the AA Intelligence Index? The Artificial Analysis Intelligence Index is a composite benchmark score that aggregates performance across multiple standardized evaluations to produce a single quality ranking. GLM 5.2 scores 51 and Llama 3.3 70B scores ~42. A 9-point gap at this tier of the index is meaningful and reflects real performance differences on complex tasks, though simpler tasks may not expose that gap.

Does GLM 5.2's 1M context window work reliably at full capacity? Published benchmark results report 1,048,576 tokens as the supported context length. Like all models with very large context windows, practical performance on tasks that require precise recall or reasoning across the full 1M-token span can vary depending on where in the context the relevant information sits. For production use cases that rely on recall near the edges of a very long context, testing on your specific workload is recommended before committing to the architecture.

Which model is faster for streaming chat? GLM 5.2 at 158 t/s delivers faster streaming throughput than Llama 3.3 70B's ~70-80 t/s on Groq. In a streaming chat interface, higher throughput means words appear on screen faster. For most conversational applications, both models stream fast enough that users do not perceive a meaningful difference — the gap becomes relevant at higher output volumes or batch scenarios.

Can I use GLM 5.2 without setting up an API key? Yes. The glm5.app chat interface provides immediate access to GLM 5.2 without requiring API key setup or configuration. For direct API integration, GLM 5.2 is available through Z.ai (base_url https://api.z.ai/v1, model glm-5.2) and through OpenRouter at z-ai/glm-5.2.

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Bottom Line

GLM 5.2 and Llama 3.3 70B are both legitimate open-weight choices — the decision turns almost entirely on whether your workload requires frontier reasoning, long context, or top benchmark scores (GLM 5.2), or whether cost and ecosystem breadth outweigh those capabilities (Llama 3.3 70B). A 9-point AA Index gap and an 8x context window difference are not trivial, but neither is an 80% reduction in output token cost for teams running high volumes of simpler tasks. Benchmark your specific use case against both before committing to either at scale.

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

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Sources

• Artificial Analysis — GLM 5.2 Model Profile
• Hugging Face — THUDM/GLM-5.2
• Groq — Llama 3.3 70B Pricing
• Meta — Llama 3.3 Model Page
• Artificial Analysis — Intelligence Index Methodology