Gemini 3.1 Pro vs GLM-5.2
Gemini 3.1 Pro wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose Gemini 3.1 Pro if you…
- Native audio and video
- Strong scientific computing
- Largest 2M context
- Search integration
- Best for: Multimodal, Scientific reasoning, Audio & video
Choose GLM-5.2 if you…
- Strong open-source ecosystem
- Good Chinese
- Mature enterprise services
- Best for: Chinese, Open source, Coding
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | Gemini 3.1 Pro | GLM-5.2 | Verdict |
|---|---|---|---|
| Vendor | Google (US) | Zhipu AI (CN) | Different vendors |
| Released | 2026.05 | 2026.06 | GLM-5.2 is newer |
| Overall (rank) | 83 · #3 | 73 · #10 | Gemini 3.1 Pro +10 |
| Coding | 74 · #9 | 66 · #15 | Gemini 3.1 Pro +8 |
| Multimodal | 92 · #2 | 79 · #12 | Gemini 3.1 Pro +13 |
| Context window | 2M | 128K | Gemini 3.1 Pro larger |
| Max output | 128K | 64K | GLM-5.2 longer |
| Effective-context | 92 | 88 | Gemini 3.1 Pro more reliable |
| Input $/1M | $2 | $1.4 | GLM-5.2 cheaper |
| Output $/1M | $12 | $4.4 | GLM-5.2 cheaper |
| Cache discount | none | none | Tie |
| Speed | ~60 tok/s | ~50 tok/s | Gemini 3.1 Pro faster |
| TTFT | 0.7s | 0.6s | GLM-5.2 snappier |
| Function calling | 78 | 80 | GLM-5.2 ahead |
| Refusal rate | ~12% | ~8% | GLM-5.2 less restrictive |
| English | 90 | 70 | Gemini 3.1 Pro |
| Chinese | 75 | 90 | GLM-5.2 |
| Modalities | text, image, audio, video | text, image | different coverage |
| Open weights | No | Yes | GLM-5.2 is open |
| Fine-tuning | Yes | Yes | |
| Free tier | Gemini App free with limited quota; API free tier available | ChatGLM free; free quota on the open platform | |
| SOC2 / no-train | yes / yes | no / yes | |
| Private deployment | No | Yes | GLM-5.2 |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
Gemini 3.1 Pro leads the overall aggregate by 10 points (83 vs 73). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GLM-5.2 remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a modest gap: Gemini 3.1 Pro scores 74 against 66. On multi-file edits, SWE-style tickets and long-horizon agent loops Gemini 3.1 Pro needs fewer correction turns; GLM-5.2 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
Gemini 3.1 Pro leads multimodal 92 vs 79. A concrete modality difference: Gemini 3.1 Pro additionally handles audio, video.
Speed & latency
Gemini 3.1 Pro is faster in interactive use: ~60 tok/s with 0.7s TTFT versus ~50 tok/s with 0.6s TTFT (about 1.2× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 2M for Gemini 3.1 Pro and 128K for GLM-5.2. Effective-context scores point the same way as window size — Gemini 3.1 Pro is ahead on usable recall (92 vs 88), so prefer it for long-document work where details cannot be missed.
Price & total cost
GLM-5.2 is the cheaper API at $1.4/$4.4 versus Gemini 3.1 Pro at $2/$12 per 1M input/output tokens — list input is about 1.4× lower.
Chinese vs English
English: Gemini 3.1 Pro 90 vs GLM-5.2 70. Chinese: 75 vs 90. For Chinese-language production, GLM-5.2 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: Gemini 3.1 Pro 78 vs GLM-5.2 80, so GLM-5.2 has the edge on structured tool use. Fine-tuning is available from Gemini 3.1 Pro and GLM-5.2. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | Gemini 3.1 Pro | GLM-5.2 | Gap |
|---|---|---|---|
| List priceno cache applied | $560100M in × $2 + 30M out × $12 | $272100M in × $1.4 + 30M out × $4.4 | 2.06×gap |
| With caching90% of inputs cache-hit | $560no published cache discount | $272no published cache discount | 2.06×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.