Qwen3.8-Max vs GLM-5.2
Qwen3.8-Max 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 Qwen3.8-Max if you…
- Top-tier Chinese
- Balanced multimodal
- Alibaba Cloud ecosystem
- Good value
- Best for: Chinese tasks, Multimodal, Coding
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 | Qwen3.8-Max | GLM-5.2 | Verdict |
|---|---|---|---|
| Vendor | Alibaba (CN) | Zhipu AI (CN) | Different vendors |
| Released | 2026.07 | 2026.06 | Qwen3.8-Max is newer |
| Overall (rank) | 82 · #4 | 73 · #10 | Qwen3.8-Max +9 |
| Coding | 77 · #6 | 66 · #15 | Qwen3.8-Max +11 |
| Multimodal | 92 · #2 | 79 · #12 | Qwen3.8-Max +13 |
| Context window | 1M | 128K | Qwen3.8-Max larger |
| Max output | 128K | 64K | GLM-5.2 longer |
| Effective-context | 95 | 88 | Qwen3.8-Max more reliable |
| Input $/1M | $2.5 | $1.4 | GLM-5.2 cheaper |
| Output $/1M | $7.5 | $4.4 | GLM-5.2 cheaper |
| Cache discount | 80% off | none | Qwen3.8-Max deeper |
| Speed | ~55 tok/s | ~50 tok/s | Qwen3.8-Max faster |
| TTFT | 0.6s | 0.6s | Tie |
| Function calling | 88 | 80 | Qwen3.8-Max ahead |
| Refusal rate | ~10% | ~8% | GLM-5.2 less restrictive |
| English | 78 | 70 | Qwen3.8-Max |
| Chinese | 98 | 90 | Qwen3.8-Max |
| Modalities | text, image | text, image | Same |
| Open weights | No | Yes | GLM-5.2 is open |
| Fine-tuning | Yes | Yes | |
| Free tier | Free credits for new Alibaba Cloud Bailiang users | ChatGLM free; free quota on the open platform | |
| SOC2 / no-train | no / yes | no / yes | |
| Private deployment | Yes | Yes | Both support it |
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
Qwen3.8-Max leads the overall aggregate by 9 points (82 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 clear gap: Qwen3.8-Max scores 77 against 66. On multi-file edits, SWE-style tickets and long-horizon agent loops Qwen3.8-Max needs fewer correction turns; GLM-5.2 is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
Qwen3.8-Max leads multimodal 92 vs 79. Neither emits native video, so the comparison is about parsing images and documents, not generation.
Speed & latency
Qwen3.8-Max is faster in interactive use: ~55 tok/s with 0.6s TTFT versus ~50 tok/s with 0.6s TTFT (about 1.1× 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 1M for Qwen3.8-Max and 128K for GLM-5.2. Effective-context scores point the same way as window size — Qwen3.8-Max is ahead on usable recall (95 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 Qwen3.8-Max at $2.5/$7.5 per 1M input/output tokens — list input is about 1.8× lower. Cache discounts (Qwen3.8-Max 80%) shift the effective bill, worked out below.
Chinese vs English
English: Qwen3.8-Max 78 vs GLM-5.2 70. Chinese: 98 vs 90. For Chinese-language production, Qwen3.8-Max is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.
Tool use & ecosystem
Function-calling score: Qwen3.8-Max 88 vs GLM-5.2 80, so Qwen3.8-Max has the edge on structured tool use. Fine-tuning is available from Qwen3.8-Max 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 | Qwen3.8-Max | GLM-5.2 | Gap |
|---|---|---|---|
| List priceno cache applied | $475100M in × $2.5 + 30M out × $7.5 | $272100M in × $1.4 + 30M out × $4.4 | 1.75×gap |
| With caching90% of inputs cache-hit | $29590M cached in × $0.5 + 10M in × $2.5 + 30M out × $7.5 | $272no published cache discount | 1.08×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.