GLM 5.2 vs Gemini 2.5 Pro: Performance, Price, and When to Choose Each

You need a capable frontier model for a coding project or long-document pipeline — the question is whether Gemini 2.5 Pro's higher benchmark scores justify paying 2.3x more per output token and giving up the ability to self-host. This comparison gives you the numbers to decide.

The Short Version

Gemini 2.5 Pro leads on raw intelligence benchmarks, scoring ~75 on the Artificial Analysis Intelligence Index versus GLM 5.2's 51 — a meaningful 24-point gap. But GLM 5.2 generates tokens at 158 t/s against Gemini 2.5 Pro's ~50 t/s, making it roughly 3x faster for throughput-heavy workloads. Output pricing is $4.40/M for GLM 5.2 versus $10.00/M for Gemini 2.5 Pro under 200K context — a 56% savings on the most expensive part of most API bills. GLM 5.2 is MIT-licensed with open weights on Hugging Face; Gemini 2.5 Pro is closed source and API-only.

Choose GLM 5.2 if you need high throughput, want to self-host or avoid vendor lock-in, are building cost-sensitive applications, or are working with code and terminal tasks where its SWE-bench Pro score of 62.1% and Terminal-Bench v2.1 score of 78% are directly relevant.

Choose Gemini 2.5 Pro if you need multimodal input (images, audio, video), require the highest possible benchmark accuracy and your use case cannot tolerate a quality gap, or are deeply integrated into the Google ecosystem.

Quick Comparison

Benchmark Performance

The headline number — a 24-point gap on the Artificial Analysis Intelligence Index — reflects aggregated performance across reasoning, instruction following, and knowledge tasks. Gemini 2.5 Pro holds a real edge in general-purpose intelligence evaluation, and for tasks that mirror those benchmarks closely (open-ended reasoning, complex multi-step problem solving), that gap will show up in production.

The picture changes when you zoom into specific domains. On GPQA Diamond, a graduate-level science reasoning benchmark, GLM 5.2 posts 89% — competitive with or exceeding many frontier models including Gemini 2.5 Pro's published estimates. For software engineering (SWE-bench Pro at 62.1%) and terminal/shell tasks (Terminal-Bench v2.1 at 78%), GLM 5.2's numbers are strong and directly comparable to real agentic coding workflows.

If your application is primarily code generation, CLI automation, or technical document parsing, GLM 5.2's benchmark profile maps well onto what matters in production. If your application involves open-ended generation, complex reasoning chains, or tasks similar to the broad AA Index methodology, Gemini 2.5 Pro's higher overall score is the more relevant signal.

Pricing Breakdown

Input pricing is close at standard context lengths — Gemini 2.5 Pro is actually marginally cheaper at $1.25/M versus GLM 5.2's $1.40/M for inputs under 200K tokens. The divergence is on output, which drives the majority of real API costs in most applications. At $4.40/M versus $10.00/M, GLM 5.2 output tokens cost 56% less. For a pipeline generating 100M output tokens per month, that is a difference of $560,000 annually.

The gap widens further with long-context usage. Above 200K tokens, Gemini 2.5 Pro output climbs to $15.00/M — more than 3.4x GLM 5.2's flat $4.40/M rate. GLM 5.2 does not apply a surcharge for longer context, which matters for document-heavy or multi-turn applications.

GLM 5.2's cache hit rate of $0.26/M also significantly reduces costs for repeated-prompt patterns like RAG pipelines, system-prompt-heavy agents, or any workflow where the same large context block is reused across requests.

For details on how to optimize GLM 5.2 costs in practice, see the GLM 5.2 pricing guide.

Speed and Latency

Speed and latency pull in opposite directions between these two models. Gemini 2.5 Pro has a faster time to first token (~0.8s versus GLM 5.2's 1.54s), which is relevant for interactive applications where perceived responsiveness matters — chat interfaces, streaming completions, and any workflow where a user is watching text appear in real time.

GLM 5.2's throughput advantage is decisive for batch and generation workloads. At 158 tokens per second — ranked third fastest among frontier models per Artificial Analysis — it can complete long outputs, documents, or code files in roughly one-third the time Gemini 2.5 Pro requires. For pipelines that process large volumes of text (summarization at scale, report generation, agentic loops with long outputs), the throughput gap translates directly into infrastructure cost and turnaround time.

The practical split: if your users see tokens streaming in a chat window and judge quality by how quickly the first word appears, Gemini 2.5 Pro's TTFT advantage is real. If you are running background jobs, batch inference, or any pattern where total generation time matters more than initial latency, GLM 5.2's 3x throughput advantage dominates.

Context Window

Both models offer a 1M token context window (1,048,576 tokens for GLM 5.2). This is large enough to hold approximately 750,000 words — equivalent to a full-length novel, a multi-hundred-page legal document, or an entire medium-sized codebase in a single prompt.

The functional difference is cost at long context. GLM 5.2 charges the same $1.40/M input and $4.40/M output regardless of context length. Gemini 2.5 Pro doubles its input price from $1.25/M to $2.50/M above 200K tokens and raises output from $10.00/M to $15.00/M. For applications that routinely send 400K–1M token contexts, this pricing structure makes GLM 5.2 significantly more economical even before accounting for the base output price difference.

For context about how GLM 5.2's 1M window compares to other models in its class, see GLM 5.2 features.

Key Differentiators

Modalities

This is the clearest functional distinction between the two models. Gemini 2.5 Pro accepts text, images, audio, and video as input — making it the obvious choice for any application involving visual understanding, audio transcription, video analysis, or cross-modal reasoning. GLM 5.2 is text-only. If your pipeline needs to read a screenshot, process a recorded meeting, or analyze a chart image, GLM 5.2 is not a viable option without a separate vision model handling that layer.

License and Open Weights

GLM 5.2 is released under the MIT license with weights publicly available on Hugging Face at THUDM/GLM-5.2. This means you can download the model, run it on your own hardware, audit the weights, fine-tune it, and deploy it without API dependency or usage restrictions. Gemini 2.5 Pro is a closed-source proprietary model accessible only via Google's API. There is no self-hosted option.

For organizations with data residency requirements, compliance constraints, or a preference for infrastructure independence, this difference is often determinative. MIT open weights also mean GLM 5.2 will remain accessible even if its API is discontinued or pricing changes — a meaningful consideration for production systems with long lifespans.

Architecture Transparency

GLM 5.2's architecture is publicly documented: 753 billion total parameters in a Mixture of Experts design with 40 billion active parameters per forward pass, 78 transformer decoder layers, 256 experts per layer with 8 activated, and Dynamic Sparse Attention. Gemini 2.5 Pro's architecture details are undisclosed. For researchers, fine-tuners, or teams that need to reason about model behavior from first principles, GLM 5.2's transparency is a significant practical advantage.

When to Choose GLM 5.2

• Your application generates large output volumes and per-token cost is a significant constraint
• You need high throughput for batch processing, background jobs, or pipelines with many parallel requests
• You want to self-host for data residency, compliance, or infrastructure independence
• Your use case maps closely to coding, terminal tasks, or scientific reasoning (GPQA Diamond 89%, SWE-bench Pro 62.1%)
• You prefer open-weight models with MIT licensing and no vendor lock-in
• You are building on long-context workflows above 200K tokens where Gemini 2.5 Pro's price surcharge is significant
• You want to fine-tune or modify the model weights

When to Choose Gemini 2.5 Pro

• Your application requires image, audio, or video understanding — GLM 5.2 cannot process these modalities
• Your use case relies on general-purpose reasoning or instruction following at the highest available quality, and the 24-point AA Index gap matters in your specific task domain
• You are building within the Google Cloud ecosystem and need native integration with other Google services
• Interactive user-facing latency is critical and you prioritize lower TTFT (~0.8s) over generation throughput
• You are not cost-sensitive on output tokens and want to minimize model selection complexity

Frequently Asked Questions

Is GLM 5.2 actually production-ready compared to Gemini 2.5 Pro?

Yes. GLM 5.2 is a frontier-class model from Zhipu AI (Tsinghua University's research lab) with documented performance on standard benchmarks and a public API via Z.ai. Its 89% GPQA Diamond score and 62.1% SWE-bench Pro result are competitive with models in the same tier. The AA Intelligence Index gap reflects aggregate benchmark performance, not a binary production-readiness distinction — many teams run GLM 5.2 in production today.

Can I use both models in the same application?

Yes. Both expose OpenAI-compatible APIs, making it straightforward to route requests to either model based on task type. A common pattern: use GLM 5.2 for high-volume text generation and code tasks, and route multimodal requests to Gemini 2.5 Pro only when image or audio input is present. GLM 5.2 is accessible via https://api.z.ai/v1 with model glm-5.2, and also on OpenRouter as z-ai/glm-5.2.

How significant is the 24-point AA Intelligence Index gap in practice?

It depends entirely on your task. The AA Index aggregates across many benchmark types. For tasks involving scientific reasoning, GLM 5.2 posts 89% on GPQA Diamond — strong in absolute terms. For complex open-ended reasoning or instruction following at the frontier, Gemini 2.5 Pro's overall edge is real. The best approach is to run both on examples from your actual workload rather than extrapolating from aggregate scores.

Does GLM 5.2's MoE architecture affect quality compared to dense models?

The Mixture of Experts design (753B total / 40B active) is primarily an efficiency mechanism — it allows the model to have a large parameter count without activating all of them for every token, enabling higher throughput at lower inference cost. Modern MoE models including GLM 5.2 are competitive with dense models of similar effective size. The architecture is one reason GLM 5.2 achieves 158 t/s while maintaining frontier-level quality.

Can I fine-tune GLM 5.2?

Yes. Because GLM 5.2 is MIT-licensed with weights on Hugging Face (THUDM/GLM-5.2), you can fine-tune it on domain-specific data using standard training frameworks. This is not possible with Gemini 2.5 Pro, which provides no access to model weights. Fine-tuning is particularly valuable for specialized domains where a well-tuned smaller model can exceed a general-purpose frontier model on task-specific benchmarks.

What happens to pricing at very long context with each model?

GLM 5.2 charges a flat rate regardless of context length: $1.40/M input, $4.40/M output. Gemini 2.5 Pro charges $1.25/M input and $10.00/M output below 200K tokens, then doubles input to $2.50/M and raises output to $15.00/M above 200K. For a 500K token context generating 10K output tokens, GLM 5.2 costs approximately $0.744 versus Gemini 2.5 Pro's approximately $1.40 — an 88% premium on Gemini for long-context workloads.

Is GLM 5.2 suitable for multilingual applications?

GLM 5.2 is developed by a team with strong multilingual (especially Chinese and English) training emphasis. It performs well across major languages. Gemini 2.5 Pro also has broad multilingual support. For applications targeting predominantly English content, either model is appropriate. For Chinese-language tasks specifically, GLM 5.2's training lineage is a practical advantage.

Bottom Line

Gemini 2.5 Pro is the more capable general-purpose model on aggregate benchmarks and the only option when multimodal input is required. GLM 5.2 is faster, cheaper on output by 56%, open-weight under MIT, and self-hostable — advantages that compound in high-volume or cost-sensitive production environments. For text-only coding, agentic, and document workflows where throughput and cost control matter, GLM 5.2 is the stronger practical choice.

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

Sources

• Artificial Analysis Intelligence Index and model benchmarks
• GLM 5.2 on Hugging Face (THUDM/GLM-5.2)
• Z.ai API documentation
• Gemini 2.5 Pro — Google DeepMind
• Gemini API pricing — Google AI