GLM-4.7-FlashX
zai/glm-4-7-flashxBy Z.AI / Zhipu · family: glm-flash · released 2026-01-19 · knowledge: 2025-04
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capabilities
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.
Coding62
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)6/20
- Provider availability6/10
Agents71
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability6/10
JSON / structured output49
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency75
- Headline price (log-scaled)70/95
- Has prompt-cache pricing5/5
Long context55
- Context window (100K → 2M)45/90
- Has published price for full window10/10
Production-readiness88
- Number of independent providers30/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers8/10
- Official model (not derivative)15/15
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 | $0.55 < $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 | $1.10 < $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 | $0.34 < $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 | $0.96 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $1.08 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Pricing detail
Recommended pricing from zai · glm-4.7-flashx
Cheapest provider: vercel · $0.060 input + $0.400 output
Available on 6 providers
| Provider | Provider model id | Input / 1M | Output / 1M | Context | Released |
|---|---|---|---|---|---|
| Z.AI zai | glm-4.7-flashx | $0.070 | $0.400 | 200K | 2026-01-19 |
| Zhipu AI zhipuai | glm-4.7-flashx | $0.070 | $0.400 | 200K | 2026-01-19 |
| Vercel AI Gateway vercel | zai/glm-4.7-flashx | $0.060 | $0.400 | 200K | 2025-01 |
| 302.AI 302ai | glm-4.7-flashx | $0.071 | $0.429 | 200K | 2026-01-20 |
| ZenMux zenmux | z-ai/glm-4.7-flashx | $0.070 | $0.420 | 200K | 2026-01-19 |
| LLM Gateway llmgateway | glm-4.7-flashx | $0.070 | $0.400 | 200K | 2026-01-19 |
Data inconsistencies across providers
- release_date varies (span 384d): 2025-01, 2026-01-19, 2026-01-20
Different providers report different values for this model. Quick facts above use the representative provider; consult the table for per-provider truth.
Frequently asked questions
How much does GLM-4.7-FlashX cost?
GLM-4.7-FlashX costs $0.070 per 1M input tokens and $0.400 per 1M output tokens, sourced from zai. 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-4.7-FlashX?
GLM-4.7-FlashX has a context window of 200K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does GLM-4.7-FlashX support tool calling?
Yes. GLM-4.7-FlashX 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-4.7-FlashX support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for GLM-4.7-FlashX in our data source. Verify with Z.AI / Zhipu's official documentation before relying on it in production.
Can GLM-4.7-FlashX accept image input?
No. GLM-4.7-FlashX only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is GLM-4.7-FlashX open-weight?
Yes. GLM-4.7-FlashX'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-4.7-FlashX?
If GLM-4.7-FlashX doesn't fit, consider GLM-5, GLM-4.7, GLM-5.1. 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 come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Z.AI / Zhipu models
- GLM-5$1.00 in / $3.20 out
- GLM-4.7$0.60 in / $2.20 out
- GLM-5.1$1.40 in / $4.40 out
- GLM-4.6$0.60 in / $2.20 out
- GLM-4.7-Flash$0.06 in / $0.40 out
Capability lists this model is in
Last updated:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.