GLM 5.2 vs GPT-4o: Benchmarks, Pricing, and the Real Cost Difference

GPT-4o is the baseline most developers use when evaluating any new model. It's the default API for OpenAI users — familiar, well-documented, and backed by the most widely deployed AI infrastructure in the world. So when comparing GLM 5.2 against it, the question most teams actually want answered is simple: what do you get by switching, and what do you give up?

The short version: GLM 5.2 costs 56% less per output token, generates responses 3× faster, and carries a 1-million-token context window versus GPT-4o's 128K. On core reasoning benchmarks like GPQA Diamond, GLM 5.2 scores 89% versus GPT-4o's roughly 74%. What you give up: native audio input, image understanding in the same call, and the closed-system guarantees that some production teams require.

Choose GLM 5.2 if your workload is text-heavy coding, analysis, or agentic tasks at scale — and cost efficiency or raw speed is the constraint.

Choose GPT-4o if your application needs audio transcription, image-to-text, or multimodal reasoning in a single API call.

Quick Comparison

Benchmark Performance

GLM 5.2 and GPT-4o sit within the same general intelligence tier — both score in the upper-middle range of frontier models — but the benchmark composition tells a more interesting story.

The GPQA Diamond result is the sharpest divergence: GLM 5.2 at 89% versus GPT-4o at roughly 74%. GPQA tests doctoral-level reasoning in physics, chemistry, and biology — and GLM 5.2's stronger performance here reflects the training depth from THUDM's academic research focus.

On SWE-bench Pro — real GitHub issue resolution — GLM 5.2 at 62.1% outperforms most frontier models. GPT-4o's SWE-bench numbers have improved since 2025, but remain below GLM 5.2's current score.

GPT-4o's AA Intelligence Index of ~57 reflects its stronger multimodal reasoning score (MMMU-Pro), which GLM 5.2 doesn't participate in. On pure text benchmarks, the gap between the two models is smaller than the composite index suggests.

Pricing Breakdown

This is where the difference is most concrete and most consequential at scale.

The 56% output token discount is the number that compounds. Most real-world API workloads generate more output tokens than input tokens, which means the effective cost difference across a production workload is closer to 2–3× than the blended input average.

Concrete example: A code review task with 30K input tokens and 15K output tokens costs $0.108 with GLM 5.2 and $0.225 with GPT-4o — roughly 2.1× more per call. At 20,000 daily calls (a mid-sized production deployment), that's an annualized difference of around $840,000.

Cache hit pricing makes GLM 5.2's advantage even wider on repeated-context workloads. At $0.26/M on cache hits versus GPT-4o's $1.25/M, applications with large static system prompts — a fixed codebase, a product knowledge base, a long system prompt — see 79% cost reduction on those cached tokens.

Speed and Latency

GLM 5.2 generates text significantly faster than GPT-4o in throughput terms. GPT-4o has a slight edge on time to first token.

At 158 tokens per second, GLM 5.2 ranks third across all frontier models on the Artificial Analysis speed leaderboard. GPT-4o at around 45 t/s is mid-tier on the same ranking.

A 1,000-token response takes roughly 6 seconds from GLM 5.2 and about 22 seconds from GPT-4o. For an IDE copilot or a real-time code assistant where the user is watching the output stream, this is a perceptible difference. For a nightly batch analysis job, it's irrelevant.

GPT-4o's lower TTFT (~0.8s versus GLM 5.2's 1.54s) means its first tokens appear faster — which can feel snappier in interactive chat applications even if the total response time is longer. For streaming interfaces, consider whether TTFT or overall throughput matters more for your use case.

Context Window: 1M vs 128K

This is the largest single-dimension difference between the two models.

GLM 5.2 handles 1 million tokens in a single request — approximately 1,500 pages of text. GPT-4o maxes out at 128K tokens, about one-eighth of GLM 5.2's capacity.

In practical terms:

• Large codebase analysis: A 500K-token repository fits in one GLM 5.2 call. With GPT-4o, you're chunking and losing cross-file context.
• Long document workflows: Legal discovery sets, audit logs, multi-chapter research reports — all fit in one GLM 5.2 request.
• Agentic loops with long histories: Multi-step agent runs accumulate context. GPT-4o requires context management at 128K; GLM 5.2 can hold much longer histories without truncation.

For tasks that stay well under 100K tokens — a single file, a targeted code review, a focused Q&A — the context advantage is irrelevant to the choice.

Multimodal Capabilities

GPT-4o accepts text, images, and audio in the same API call. GLM 5.2 accepts text only.

GPT-4o's multimodal capabilities enable:

• Audio transcription and voice input within a single call (no separate Whisper step)
• Image-to-text: Screenshots → code generation, UI mockup analysis, chart interpretation
• Document processing with embedded images: PDFs, slides, technical diagrams
• Video frame analysis: Pass frames from video alongside text instructions

If any of these capabilities are core to your application, GPT-4o is the only option of the two. GLM 5.2's advantages in cost, speed, and context window are irrelevant when the task requires image or audio input.

For text-only workloads — the majority of API usage — GLM 5.2's lack of multimodal capability is not a limitation.

Open Source vs Closed Source

GLM 5.2 weights are available today on Hugging Face under the MIT license. GPT-4o is closed source — accessible only via OpenAI's API, with no self-hosting option.

What this means in practice:

• Air-gapped deployments: GLM 5.2 can run on your own infrastructure, fully offline. GPT-4o cannot.
• Fine-tuning: GLM 5.2 weights can be fine-tuned on proprietary data. GPT-4o's fine-tuning goes through OpenAI's API with data retention policies.
• Cost at inference scale: Self-hosted GLM 5.2 eliminates per-token API costs entirely for high-volume deployments. Infrastructure cost replaces marginal API cost.
• Regulatory compliance: Some industries require data residency guarantees that a fully self-hosted model provides more cleanly than a third-party API.

The trade-off: self-hosting a 753B-parameter MoE model requires substantial GPU infrastructure. See how to run GLM 5.2 locally for hardware requirements.

API Compatibility

Both models are OpenAI SDK-compatible via the chat completions format, which makes migration straightforward for most use cases.

GLM 5.2 is served at Z.ai's API endpoint. The switch from GPT-4o:

# Before: OpenAI
from openai import OpenAI
client = OpenAI(api_key="your_openai_key")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain MoE architectures."}]
)

# After: GLM 5.2 via Z.ai
client = OpenAI(
    api_key="your_zai_key",
    base_url="https://api.z.ai/v1"
)
response = client.chat.completions.create(
    model="glm-5.2",
    messages=[{"role": "user", "content": "Explain MoE architectures."}]
)

Most text-based integrations require only changing api_key, base_url, and model — the rest of the call is identical. Applications that pass image_url or audio content type inputs to GPT-4o will need a different approach, since GLM 5.2 doesn't support those modalities.

When to Choose GLM 5.2

GLM 5.2 is the better choice when:

• Your workload is entirely text or code — no image, audio, or video input required
• You're running at volume where the 56% output token cost saving compounds meaningfully
• Response throughput matters: 158 t/s gives your users faster completions
• Your task requires a context window larger than 128K — codebase analysis, long document processing, extended agent histories
• You need scientific reasoning at depth: 89% on GPQA Diamond versus GPT-4o's ~74%
• You want open-weight access for self-hosting, fine-tuning, or air-gapped deployment
• You're looking for a GPT-4o-compatible API that doesn't lock you into a single provider

When to Choose GPT-4o

GPT-4o is the better choice when:

• Your application requires image input: screenshot-to-code, UI mockup analysis, chart understanding
• You need audio input or voice transcription in the same API call
• Your existing prompt engineering is tightly calibrated to GPT-4o's behavior and re-testing isn't feasible
• Your organization has compliance or procurement requirements that OpenAI's enterprise tier already satisfies
• You need the brand reliability of the most widely deployed frontier model for customer-facing applications where "powered by OpenAI" matters to your users

Frequently Asked Questions

Is GLM 5.2 better than GPT-4o?

On GPQA Diamond (doctoral-level science reasoning), GLM 5.2 scores 89% versus GPT-4o's ~74% — a clear advantage. GLM 5.2 also leads on software engineering benchmarks and runs 3× faster. GPT-4o leads on the AA Intelligence composite (57 vs 51) partly because it participates in multimodal evaluations where GLM 5.2 doesn't compete. For text-only tasks, GLM 5.2 performs at least as well on most benchmarks at substantially lower cost.

How much cheaper is GLM 5.2 than GPT-4o?

Input tokens cost $1.40/M versus $2.50/M — 44% cheaper. Output tokens cost $4.40/M versus $10.00/M — 56% cheaper. Cache hits cost $0.26/M versus $1.25/M — 79% cheaper. For typical production workloads where output tokens dominate, the effective all-in cost is roughly 2–2.5× lower with GLM 5.2.

Does GLM 5.2 support images like GPT-4o?

No. GLM 5.2 is a text-only model. For image input alongside text (screenshot-to-code, diagram understanding, document processing), GPT-4o is the only option of the two. See Is GLM 5.2 Multimodal? for full details on GLM 5.2's modality support.

Can I switch from GPT-4o to GLM 5.2 without rewriting my code?

For text-based applications, usually yes — change api_key, base_url, and model. Both models use the OpenAI chat completions format. Applications that pass image or audio content types will need to handle those modalities differently, since GLM 5.2 doesn't support them.

Is GLM 5.2 faster than GPT-4o?

On throughput, yes — GLM 5.2 generates 158 t/s versus GPT-4o's ~45 t/s. GPT-4o has a lower time to first token (~0.8s versus GLM 5.2's 1.54s). For long responses, GLM 5.2 finishes significantly faster overall. For very short responses where you're measuring responsiveness to the first token, GPT-4o is quicker to start.

Is GLM 5.2 open source?

Yes. GLM 5.2 weights are available on Hugging Face under the MIT license, permitting commercial use, self-hosting, and fine-tuning with no commercial restrictions. GPT-4o is closed source and only accessible via OpenAI's API.

What is GLM 5.2's context window vs GPT-4o?

GLM 5.2 supports 1 million tokens (1,048,576 to be exact) — about 8× GPT-4o's 128K limit. For large-codebase analysis, full legal document review, or long multi-turn agent sessions, GLM 5.2 removes the need for chunking that GPT-4o requires.

Bottom Line

GLM 5.2 and GPT-4o serve different production profiles. The cost and speed differences are large enough to matter at scale: GLM 5.2 is 56% cheaper per output token and 3× faster in throughput. On core text and coding benchmarks, GLM 5.2 matches or exceeds GPT-4o, with a notably stronger result on scientific reasoning.

The decision reduces to one question: do you need image or audio input? If yes, GPT-4o. If no, GLM 5.2 delivers comparable intelligence at meaningfully lower cost and higher speed — with an 8× larger context window and open weights as a bonus.

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

Sources

• GLM-5.2 (max) — Intelligence, Performance & Price Analysis — Artificial Analysis (AA Index 51, speed 158 t/s, pricing $1.40/$4.40)
• GPT-4o — Intelligence, Performance & Price Analysis — Artificial Analysis (AA Index, speed, TTFT benchmark data)
• GLM-5.2 — Z.AI Developer Documentation (official modality spec, API parameters, context window)
• GPT-4o Pricing — OpenAI (input $2.50/M, output $10.00/M, context 128K)
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark — arXiv (benchmark methodology for doctoral-level science reasoning)