Z.ai
GLM-5.3-FlashX
glm-5.3-flashx
Total Context
1M
Max Output
131.1K
Released
N/A
Input$0.2857 / M tokens
Output$1 / M tokens
GLM-5
glm-5GLM-5 is Z.ai’s new-generation foundation model for agentic engineering, complex systems engineering, and long-range agent tasks. Official docs describe it as an open foundation model aimed at production-grade programming, planning, backend reasoning, and iterative self-correction. Its page description should highlight complex engineering workflows rather than only “Chinese LLM” positioning.
Context Window
204.8K tokens
Maximum Output
131.1K tokens
Release Date
Feb 11, 2026
Modalities
GLM-5 Pricing
| Input Price | Output Price | Cache Read |
|---|---|---|
| $0.5714/M | $2.5714/M | $0.1143/M |
GLM-5 API Capabilities
Reasoning
Supported
Tool calling
Supported
Temperature parameter
Supported
Attachments
Not supported
Knowledge Base
—
Endpoint Protocols
Completions APIMessages APIgemini
GLM-5 Model Highlights
GLM-5 is an open-weight text model focused on complex systems engineering, long-range agent planning and deep software debugging.
Complex Systems Engineering
Understands multi-component software systems and supports frontend development, backend engineering and coordinated architectural changes.
Long-Range Agent Planning
Plans and carries out extended sequences of engineering actions with limited human intervention.
Deep Software Debugging
Investigates failures across code paths, proposes root causes and iterates on repairs using execution feedback.
GLM-5 Use Cases
GLM-5 suits backend refactoring, complex defect investigation and autonomous implementation workflows spanning multiple engineering stages.
Backend System Refactoring
Map dependencies in an existing backend, plan architectural changes and revise connected modules while retaining expected behavior.
Complex Defect Investigation
Trace failures across components, inspect relevant code and test hypotheses to produce a targeted repair.
Autonomous Engineering Workflow
Carry a software task from planning through implementation and verification, using intermediate results to adjust subsequent actions.
How to Use GLM-5 via the TokenHub API
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)GLM-5 Benchmarks
| Index score | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Artificial Analysis broad capability aggregate | 39.5 |
| Artificial Analysis Coding Index | Artificial Analysis software task aggregate | 44.2 |
| Knowledge & Reasoning | ||
| GPQA | Advanced science problem solving | 82% |
| HLE | Broad expert-level exam set | 27.2% |
| Coding & Engineering | ||
| SciCode | Scientific coding challenges | 46.2% |
| Terminal-Bench Hard | Hard terminal task execution | 43.2% |
| Instruction Following & Agent Tasks | ||
| IFBench | Prompt constraint adherence | 72.3% |
| AA-LCR | Long-context reasoning | 63.3% |
| τ²-Bench | Agent workflow tasks | 98.2% |
Metrics sourced from Artificial Analysis
GLM-5 FAQ
GLM-5: capabilities, use cases, limits, and TokenHub guidance.
What problem does GLM-5 address?+
GLM-5 is a Z.AI model for complex system engineering and long-range agent tasks.
When should I choose GLM-5?+
Best for complex coding, agent workflows and repository-scale development, especially when software-engineering quality is the priority.
What does GLM-5 do well?+
Key strength: a strong focus on complex system engineering and long-range agents.
What does GLM-5 sacrifice?+
It is text-focused and does not offer the same multimodal breadth as newer models. For long-horizon task completion, consider GLM-5.2.
How do I use GLM-5 in TokenHub?+
Use the exact ID shown by TokenHub; follow your account docs and verify current features.
Ready to use GLM-5?
Use one API key to access GLM-5 and more AI models through TokenHub.
Media and Discussions
Selected public videos and posts related to this model.
X (Twitter)
Z.ai ·
Introducing GLM-5: From Vibe Coding to Agentic Engineering GLM-5 is built for complex systems engineering and long-horizon agentic tasks. Compared to GLM-4.5, it scales from 355B params (32B active) to 744B (40B active), with pre-training data growing from 23T to 28.5T tokens. https://t.co/uGYQUjIbbs
https://pbs.twimg.com/media/HA5Ikg1aMAAEidm.jpgView originalZ.ai ·
GLM-5 is coming to Coding Plan Pro users within one week, and we're working to bring it to everyone after that. To be upfront: compute is very tight. Even before the GLM-5 launch, we were pushing every chip to its limit just to serve inference. We appreciate your understanding
View originalZ.ai ·
GLM-5, Gameboy and Long-Task Era → 700+ tool calls, 800+ context handoffs, and a single agent running for over 24 hours. https://t.co/FIcgTj7qZC https://t.co/fzGN8AmB5M
https://pbs.twimg.com/amplify_video_thumb/2021753977998913538/img/FLuk3vdwLNgKvlyP.jpgView originalReddit
GLM-5 lands with 50.4% on Humanity’s Last Exam (Thinking w/ tools)
redlikeazebra ·
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Here is the source from Z.ai themselves! https://z.ai/blog/glm-5?_gl=1*qx9wgd*_gcl_au*MTAwMTgwMTkxMy4xNzcwODMwMjY0*_ga*MTMzOTcyODAxOS4xNzcwODMwMjY0*_ga_Z8QTHYBHP3*czE3NzA4MzAyNjMkbzEkZzEkdDE3NzA4MzAzMjIkajEkbDAkaDA.#:~:text=35.4-,Humanity%27s%20Last%20Exam,w/%20Tools,-50.4 It just barely made the top 5 HLE scores though. Surely Deepseek V4 will set the record soon. -Claude Opus 4.6 best score as reported by them is 53.1 as current leader. -Next up is SupAI with an ensemble structure at 52.15. -Then we have Moonshot - Kimi K2-Thinking-0905 coming in at 51% -Even Grok 4 got 51% -So, yes it slide in to the top 5 at 50.4%, which shows we are making progress and it might show promise.
GLM 5 Review (Tested on Yodayo)
Kisame83 ·
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GLM-5 is the latest flagship from Z.ai, and after putting it through a layered stress test on Yodayo, I wanted to give you my thoughts (like I do from time to time lol). IMPORTANT DISCLAIMER: I ran this on Yodayo (aka Moescape) as a hosted model there. From what I can tell, the <think> function is suppressed, as it is significantly faster output compared to running via proxy elsewhere. Also, Yodayo has its own internal economy with a credit system that you can earn buy, sub, get daily, etc. You spend for messages, but direct cost is hard to compare because you get message and reply at a flat rate on Yodayo. Elsewhere, paying per token, you pay for input and output both, and context can ramp up as your chat increases PLUS a model like GLM 5 outputting consistent Chain of Thought will chew tokens. TLDR, for a pure cost analysis, this is only going to mention Yodayo's internal ecosystem. But in general, proxy will likely be cheaper on paper, but mileage can vary greatly with practical use and user habits. As for the mode's capability, the rest of this can apply to any instance, and with thinking going it is usually very good at prompt adherence. Anyway, back to the show! The scenario I used wasn’t light work. It involved shared history, bruised egos, mentor-student tension, social pressure in a classroom setting, combat choreography, and a pivot into dominance-charged intimacy. Not just “fight scene go brrr” but emotional continuity across escalating turns. Basically, there's a shared past, a challenge, layered feelings underneath, and I chose to escalate the opening conflict set up by the creator into a fight for dominance. GLM-5 handled it with confidence. That’s the word that kept coming to my mind. Confidence. It committed to narrative choices. It escalated when escalation made sense. It didn’t stall or hedge. It kept up the rival tension without too much melodrama. Even under a fairly constraint-heavy prompt (my own tweaked prompt and settings), it stayed disciplined - no user agency bleed, no random fourth wall cracks, no rushed resolution. To give it context, I ran the same chat with Claude Haiku and GLM-4.7. Haiku, as expected, played it well. It delivered similar quality and story flow with slightly less melodrama, but more restraint. If anything, Haiku felt safer, composed. But it also took fewer risks. GLM-5 showed more narrative aggression and character agency. Haiku was the veteran assassin. GLM-5 was the heavyweight striker. Ultimately, I see GLM-5 and Haiku as side-grades, quality-wise. YMMV depending on what you prefer. Haiku is the more cost-efficient choice. GLM-5 brings sharper scene propulsion. I'd argue, GLM-5 feels more alive. GLM-4.7 was the most interesting comparison. Under the same exact prompt, 4.7 carried the chat in a noticeably different direction. Its webnovel-style dramatic bias came out much stronger at the start - {{char}} was a lot more volatile out the gate. Then, when things got more intense, it pivoted hard into power dynamics and effectively deflated the rivalry tension instead of sustaining it. Where GLM-5 and Haiku dove into the fight, 4.7 ultimately backed away from it. {{char}} backed down and turned the boil into a simmer, moving the chat more into a bit of a cold war between us. It is a little hard to explain, and some of this may be my own prompt, which does urge for nuanced depictions that dont over-rely on genre tropes. But my point is that GLM 5 picked a lane and committed, but rolled with with the direction of the scene and let {{char}} evolve. 4.7 seemed to start off wanting to sprint, and then caught itself and decided to pace in the opposite direction. 4.7 is a model I have high praise for, so this isn't a dig - just noting I think 5 brings with it a lot more decisive scene choices. Overall, I think GLM-5 is a strong offering on Yodayo. Whether it’s worth the premium tier is up to the user, but it absolutely competes in that space. It feels more assertive than 4.7 and capable of subbing in for higher tier models in confrontation-heavy RP without feeling outclassed. It may be a hard sell to a Haiku fan, but it is worth considering for a Sonnet user, if that makes sense. One last note - if Yodayo ever enables visible Chain of Thought reasoning for GLM-5 the way they do for DeepSeek R1 or Gemini Pro, I think it would elevate even further. In off-platform testing, GLM-5 is exceptionally good at following a well-tuned prompt and adapting tightly to user preference. It rewards careful engineering.
Z.AI Performance Dashboard
JuergenAusmLager ·
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Because reliability and throughput are part of the daily discussion in this sub, I took it upon myself to create a small dashboard app to improve transparency. The dashboard tracks basic metrics such as tokens per second and time to first token for GLM-5 and GLM-4.7 once every hour. I additionally compare them to the performance of the standard API (let’s see for how long... API costs add up, after all). When discussing performance, it’s important to mention that I'm on the Pro plan. All metrics are, of course, only approximations and certainly not a dependable benchmark. Anyways try it here if you are interested: https://zaimonitor.vercel.app/
YouTube
GLM-5.2 (Fully Tested): I got EARLY ACCESS & This MODEL is CRAZY!
AICodeKing
View originalThe Only GLM 5.1 Review You Need To Watch
BridgeMind
View originalLet's Run GLM-5 - SUPER LARGE Local AI "Coding King" REVIEW
xCreate
View original