GLM-5.2 (ByteDance)
llmgateway-providers/glm-5-2出品方： llmgateway-providers · 系列: glm · 發布 2026-06-13
⚠ 本模型為社群微調 / 衍生版本，並非廠商官方發布。
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清單
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding72
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)20/20
- Provider availability2/10
Agents67
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability2/10
JSON / structured output38
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume8/20
Cost efficiency48
- Headline price (log-scaled)43/95
- Has prompt-cache pricing5/5
Long context91
- Context window (100K → 2M)81/90
- Has published price for full window10/10
Production-readiness53
- Number of independent providers10/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers8/10
- Official model (not derivative)0/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $9.20 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $18.40 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $5.00 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $15.60 $0.02 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $19.44 $0.02 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定價詳情
推薦定價來自 llmgateway-providers · bytedance/glm-5.2
於 2 家供應商可用
| 服務商 | 服務商模型 ID | 輸入 / 1M | 輸出 / 1M | 上下文 | 發布日期 |
|---|---|---|---|---|---|
| LLM Gateway llmgateway-providers | bytedance/glm-5.2 | $1.40 | $4.40 | 1.02M | 2026-06-13 |
| LLM Gateway llmgateway-providers | alibaba/glm-5.2 | $1.40 | $4.40 | 1M | 2026-06-13 |
各渠道資料存在不一致
- context_window varies: 1000000, 1024000
各服務商對此模型的回報值不一致。上方「核心數據」採用代表性服務商的值；逐項請以下表為準。
Frequently asked questions
How much does GLM-5.2 (ByteDance) cost?
GLM-5.2 (ByteDance) costs $1.40 per 1M input tokens and $4.40 per 1M output tokens, sourced from llmgateway-providers. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of GLM-5.2 (ByteDance)?
GLM-5.2 (ByteDance) has a context window of 1.02M tokens, with a max output of 128K tokens per reply. This is the total combined size of prompt + completion.
Does GLM-5.2 (ByteDance) support tool calling?
Yes. GLM-5.2 (ByteDance) supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does GLM-5.2 (ByteDance) support structured output / JSON mode?
No. GLM-5.2 (ByteDance) does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.
Can GLM-5.2 (ByteDance) accept image input?
No. GLM-5.2 (ByteDance) only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is GLM-5.2 (ByteDance) open-weight?
Yes. GLM-5.2 (ByteDance)'s weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to GLM-5.2 (ByteDance)?
If GLM-5.2 (ByteDance) doesn't fit, consider GPT OSS 20B (Together AI), InclusionAI Ling 3.0 Flash (DeepInfra), Hy3 (DeepInfra). Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
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Capability lists this model is in
最近更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.