comparison
Gemini 2.5 Pro vs GLM 4.6
Token pricing, context window and real monthly cost, side by side. GLM 4.6 is the cheaper of the two for a typical workload — about 3.9× less.
Positioned by published specs — size, context and modality — not measured performance; a smaller model can sometimes outperform a larger one on your task.
Gemini 2.5 Pro is one class larger (Flagship vs Mid). Lean to the cheaper GLM 4.6 unless your task is demanding enough to need the larger class.
| metric | Gemini 2.5 Pro | GLM 4.6 |
|---|---|---|
| Input / 1M | $1.25 | $0.50 |
| Output / 1M | $10.00 | $2.00 |
| Context | 1.0M | 205K |
| Technical class | Flagship | Mid |
| Cost @ typical workload | $1,375/mo | $350/mo |
| Modality | text + image + audio + video | Text only |
| Price source | list | routed |
| Provider | Z.AI |
Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →
Which should you pick?
On a typical workload, GLM 4.6 costs $350/mo against Gemini 2.5 Pro's $1,375/mo — roughly 3.9× cheaper. But the ranking depends on your output-to-input ratio: output is the pricier direction for both, so an output-heavy job (code generation, long answers) widens the gap while an input-heavy one (summarization, retrieval) narrows it. If you need to fit more in a single prompt, Gemini 2.5 Pro has the larger 1.0M-token window (~1,573 pages). Only Gemini 2.5 Pro accepts image input — decisive if your prompts include images. By technical class (size and context, not measured capability), Gemini 2.5 Pro is a Flagship and GLM 4.6 a Mid — so the lower price partly reflects a smaller class, not just a discount.
These are list and routed market prices, not measured outcomes. Two models at the same rate can still cost different amounts to finish the same task, because verbose or reasoning-heavy models emit more tokens. That gap is exactly what measured cost-per-task captures. The technical-class read above is likewise spec-based — size, context and modality, not measured performance — so a smaller model can still outperform a larger one on your specific task.
Frequently asked questions
Is Gemini 2.5 Pro or GLM 4.6 cheaper?
For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), GLM 4.6 costs $350/mo versus $1,375/mo for Gemini 2.5 Pro — about 3.9× less. Because output is priced higher than input, the winner can flip if your workload writes much more or less than this; check your own numbers in the calculator.
What's the main difference between Gemini 2.5 Pro and GLM 4.6?
On price, Gemini 2.5 Pro is $1.25/$10.00 per 1M (in/out) and GLM 4.6 is $0.50/$2.00. By technical class (size & context) it's Flagship (Gemini 2.5 Pro) versus Mid (GLM 4.6). Gemini 2.5 Pro has the larger context window at 1.0M tokens. Only Gemini 2.5 Pro accepts image input.
Why is GLM 4.6 so much cheaper than Gemini 2.5 Pro?
GLM 4.6 has a much lower per-token rate — $2.00/1M output versus $10.00, and it's a smaller technical class (Mid vs Flagship). The headline rate isn't the whole story, though: a verbose model can cost more to finish a task than its rate implies — that's what measured cost-per-task captures.
Which handles longer prompts, Gemini 2.5 Pro or GLM 4.6?
Gemini 2.5 Pro — its 1.0M-token context window (~1,573 pages of text) is the larger of the two, by roughly 5×.
More comparisons
- GPT-5.5 vs Gemini 2.5 Pro
- GPT-5.4 vs Gemini 2.5 Pro
- Gemini 2.5 Pro vs GPT-5.4 mini
- GPT-5.4 mini vs GLM 4.6
- GLM 4.6 vs GPT-5.4 nano
- Claude Fable 5 vs Gemini 2.5 Pro
Related
- Gemini 2.5 Pro and GLM 4.6 — full specs and price history.
- API cost calculator — compare on your own workload.
- All comparisons — the full head-to-head index.